U.S. patent application number 15/099631 was filed with the patent office on 2016-12-29 for managing data privacy and information safety.
The applicant listed for this patent is International Business Machines Corporation. Invention is credited to YUK L. CHAN, CHRISTOPHER CRAMER, DEEPTI M. NAPHADE, JAIRO A. PAVA.
Application Number | 20160381064 15/099631 |
Document ID | / |
Family ID | 57602971 |
Filed Date | 2016-12-29 |
United States Patent
Application |
20160381064 |
Kind Code |
A1 |
CHAN; YUK L. ; et
al. |
December 29, 2016 |
MANAGING DATA PRIVACY AND INFORMATION SAFETY
Abstract
Automatically screen data associated with a user that may have
already been shared on a social network or about to be shared on
the social network for a potential security risk and assign a risk
score to the data. If the assigned risk score is above a threshold
risk score, a risk mitigation measure is generated and
executed.
Inventors: |
CHAN; YUK L.; (ROCHESTER,
NY) ; CRAMER; CHRISTOPHER; (TROY, NY) ;
NAPHADE; DEEPTI M.; (CUPERTINO, CA) ; PAVA; JAIRO
A.; (MIAMI, FL) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
International Business Machines Corporation |
Armonk |
NY |
US |
|
|
Family ID: |
57602971 |
Appl. No.: |
15/099631 |
Filed: |
April 15, 2016 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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14753114 |
Jun 29, 2015 |
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15099631 |
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Current U.S.
Class: |
726/25 |
Current CPC
Class: |
H04L 51/32 20130101;
H04W 12/02 20130101; H04L 63/1441 20130101; H04L 51/12 20130101;
H04L 63/104 20130101; H04L 51/063 20130101; H04L 63/1433
20130101 |
International
Class: |
H04L 29/06 20060101
H04L029/06; H04W 12/02 20060101 H04W012/02; H04L 12/58 20060101
H04L012/58 |
Claims
1. A computer implemented method for dynamically evaluating and
mitigating risk associated with data shared on a social network,
the method comprising: receiving, by a computing device, data
associated with a user for posting on a social network, wherein the
received data comprises at least one of text, digital images, audio
or video; assigning, by the computing device, a category to the
received data; assigning, by the computing device, a risk score to
the received data based on predefined risk scores associated with
the assigned category; generating, by the computing device, a risk
mitigation measure based on the assigned risk score being greater
than a threshold risk score, wherein the threshold risk score is
determined based on input from the user, wherein generating the
risk mitigation measure is based on a defined social network data
sharing setting associated with the user, wherein the risk
mitigation measure comprises at least one of: (i) modifying the
data, wherein modifying the data comprises removing one or more
text strings contained in the data, blurring a portion of a digital
image included in the data, deleting a portion of a digital image
included in the data, and permitting a selected group of
individuals to view the data, (ii) deleting the data, wherein
deleting the data comprises erasing the received data; (iii)
retaining the data wherein the data is not posted to the social
network until receiving an instruction to post the data to the
social network, wherein retaining the data comprises delaying a
posting of the data to the social network, and storing the data in
a database, (iv) removing metadata associated with the received
data, wherein removing metadata associated with the received data
comprises removing at least one of a location metadata or a time
stamp metadata, (v) communicating a message regarding the data to a
device, wherein communicating a message comprises sending an alert
to a mobile device; executing, by the computing device, the risk
mitigation measure; and posting, by the computing device, the
modified data to the social network.
Description
BACKGROUND
[0001] The present invention relates generally to the field of
protection of data processing, and more particularly to managing
data privacy and information safety on social networks.
[0002] Social network users uploading information onto a social
network may inadvertently share sensitive information online. For
example, a user posting vacation pictures to a social network in
real-time while still on vacation may expose the security of the
user's belongings back at the user's residence to undesirable acts
by ill-minded intruders who may use such sensitive information to
plan a burglary in the user's residence during user's absence.
SUMMARY
[0003] According to an embodiment of the invention, a method for
dynamically evaluating and mitigating risk associated with data
shared on a social network is provided. The method may receive data
associated with a user, by a computing device, for posting on a
social network. The method may assign a category to the received
data. The method may assign a risk score to the received data based
on predefined risk scores associated with the assigned category.
The method may also generate a risk mitigation measure based on the
assigned risk score being greater than a threshold risk score, the
risk mitigation measure comprising one or more of: (i) modifying
the data, (ii) deleting the data, (iii) retaining the data wherein
the data is not posted to the social network until receiving an
instruction to post the data to the social network, (iv) removing
metadata associated with the received data, and (v) communicating a
message regarding the data to a device.
[0004] According to another embodiment of the invention, a computer
program product for dynamically evaluating and mitigating risk
associated with data shared on a social network is provided. The
computer program product includes one or more computer-readable
storage media and program instructions stored on the one or more
computer-readable storage media, the program instructions
executable by a processor. The computer program product may include
program instructions to receive data associated with a user, by a
computing device, for posting on a social network. The computer
program product may also include program instructions to assign a
category to the received data. The computer program product may
also include program instructions to assign a risk score to the
received data based on predefined risk scores associated with the
assigned category. The computer program product may also include
program instructions to generate a risk mitigation measure based on
the assigned risk score being greater than a threshold risk score,
the risk mitigation measure comprising one or more of: (i)
modifying the data, (ii) deleting the data, (iii) retaining the
data wherein the data is not posted to the social network until
receiving an instruction to post the data to the social network,
(iv) removing metadata associated with the received data, and (v)
communicating a message regarding the data to a device.
[0005] According to another embodiment of the invention, a computer
system for dynamically evaluating and mitigating risk associated
with data shared on a social network is provided. The computer
system includes one or more computer processors, one or more
computer-readable storage media, and program instructions stored on
the computer-readable storage media for execution by at least one
of the one or more processors. The computer system includes program
instructions to receive data associated with a user, by a computing
device, for posting on a social network. The computer system may
also include program instructions to assign a category to the
received data. The computer system may also include program
instructions to assign a risk score to the received data based on
predefined risk scores associated with the assigned category. The
computer system may also include program instructions to generate a
risk mitigation measure based on the assigned risk score being
greater than a threshold risk score, the risk mitigation measure
comprising one or more of: (i) modifying the data, (ii) deleting
the data, (iii) retaining the data wherein the data is not posted
to the social network until receiving an instruction to post the
data to the social network, (iv) removing metadata associated with
the received data, and (v) communicating a message regarding the
data to a device.
BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS
[0006] FIG. 1 is a functional block diagram illustrating a data
privacy and information safety environment, in accordance with an
embodiment of the present invention.
[0007] FIG. 2 is a functional block diagram illustrating modules of
a data privacy and information safety environment program, in
accordance with one embodiment of the present invention.
[0008] FIG. 3 is a flowchart illustrating operational steps of the
data privacy and information safety program, in accordance with an
embodiment of the present invention.
[0009] FIG. 4 is a functional block diagram illustrating a cloud
computing node according to an embodiment of the present
invention.
[0010] FIG. 5 is a functional block diagram illustrating a cloud
computing environment according to an embodiment of the present
invention.
[0011] FIG. 6 is a functional block diagram illustrating
abstraction model layers according to an embodiment of the present
invention.
[0012] The drawings are not necessarily to scale. The drawings are
merely schematic representations, not intended to portray specific
parameters of the invention. The drawings are intended to depict
only typical embodiments of the invention. In the drawings, like
numbering represents like elements.
DETAILED DESCRIPTION
[0013] Detailed embodiments of the claimed structures and methods
are disclosed herein; however, it can be understood that the
disclosed embodiments are merely illustrative of the claimed
structures and methods that may be embodied in various forms. This
invention may, however, be embodied in many different forms and
should not be construed as limited to the exemplary embodiments set
forth herein. Rather, these exemplary embodiments are provided so
that this disclosure will be thorough and complete and will fully
convey the scope of this invention to those skilled in the art. In
the description, details of well-known features and techniques may
be omitted to avoid unnecessarily obscuring the presented
embodiments.
[0014] References in the specification to "one embodiment", "an
embodiment", "an example embodiment", etc., indicate that the
embodiment described may include a particular feature, structure,
or characteristic, but every embodiment may not necessarily include
the particular feature, structure, or characteristic. Moreover,
such phrases are not necessarily referring to the same embodiment.
Further, when a particular feature, structure, or characteristic is
described in connection with one embodiment, it is submitted that
it is within the knowledge of one skilled in the art to affect such
feature, structure, or characteristic in connection with other
embodiments whether or not explicitly described.
[0015] A social network user posting information to a social
network may inadvertently share sensitive information online which
an ill-minded intruder may use to plan illegal activities
negatively affecting the user, for example, by scheduling a
burglary in the user's residence while the user is on a vacation
based on information on an impending vacation included in one of
the user's posts to the social network. However, it may be
cumbersome for a typical user to manually analyze and predict all
prospective undesirable side-effects that may result from the
user's posts to the social network. It may be desirable to have a
system that may automatically screen posts to a social network
impacting a user for potential security risks before or soon after
they get posted online.
[0016] Embodiments of the present invention may automatically
screen data associated with a user that may have already been
shared on a social network or about to be shared on the social
network for a potential security risk. The data being screened may
include a social network post ("SNP") made on a social network
web-page or "wall". The screening may occur prior to a SNP being
posted or immediately after it is posted on a social network.
Embodiments of the present invention may assign a risk information
category and a risk score based on predefined risk scores
associated with the assigned category to the data. If the assigned
risk score is above a threshold risk score, embodiments of the
present invention may generate and execute a risk mitigation
measure.
[0017] As used herein, "social network" refers to a computer
network connecting entities, such as people or organizations, by a
set of social relationships, such as friendship, co-working, or a
community representing a group of members sharing common interests
or characteristics. A social network may include blogs and forums.
The social network may foster relationships between its members,
thereby offering a higher level of affiliation and trust than other
online media through which users can interact with each other such
as electronic message boards or forums. The social network may
display social network posts (SNP) posted by a plurality of users
on the social network. Social network may also refer to a computer
application or data connecting such entities by such social
relationships. Social network may provide an avenue for users to
post information and respond to previously posted information by
self and others. Members of a social network may elect to exchange
information with or transmit information to all participants within
the social network, a minority of participants, or a group that
encompasses other participants plus others that may be connected by
a second or subsequent degree links (such as e.g., friends of
friends). Exchange with or among second or subsequent degree
members may also be denied, limited or restricted for safety and
security reasons. A social networks may include an administrator
that uses lists to control the membership in the social
network.
[0018] Embodiments of the present invention may utilize data
analytic systems well known in the art to categorize a SNP and to
assign a risk score to the SNP if it the SNP deemed to present a
potential security risk and apply a risk mitigation measure
associated with the SNP. Embodiments of the present invention may
compute a risk score for a risk that the user may have no control
of or no knowledge of. The risk score may be computed using data
analytic systems known in the art based on dynamic inputs and
trending data, in addition to static factors. Embodiments of the
present invention may present the user with options to either
automatically or manually mitigate a risk. Embodiments of the
present invention may also provide the user with the ability to
override a risk mitigation measure implemented by embodiments of
the present invention.
[0019] Risk mitigation measures may include: alerting the user of
the potential security risk, editing the SNP, removing a portion of
the SNP, deleting the entire SNP, hiding a portion of the SNP or
the whole SNP from certain viewers on a social network, blurring a
portion of a digital image that may form part of the SNP.
Embodiments of the present invention may customize its service
features based on factors that may include: a user's risk tolerance
level, locale of the user, valuables owned by the user, frequency
of real-time SNPs uploaded by the user, frequency of real-time SNPs
uploaded members in user's network, general content of SNPs
uploaded by the user and by members in the user's network, and
characteristics associated with the user. Embodiments of the
present invention may use updated security threat and security risk
information available from public and private domains, and analyze
them for predicting a security risk contained in data present in a
specific SNP in light of a user's individual characteristics
including aspects such as: activity type, location, time, age,
marital status, profession, household income, gender, ethnicity,
recent criminal activity in an area, and type of data shared on the
social network. For example, when robberies occur in the user's
neighborhood in residences of individuals with a similar personal
profile as the user, embodiments of the present invention may
generate an alert customized to the user.
[0020] Embodiments of the present invention may computationally
evaluate whether a whole SNP or a portion of a SNP should be shared
on a social network, thereby providing for granular control of
managing security risks posed by a SNP. Embodiments of the present
invention may provide advantages over manual risk management of
SNPs by a human administrator. An administrator manually reviewing
SNPs for such a manual risk mitigation mechanism may have
limitations such as: time delay in implementing the risk mitigation
mechanism; in-consistent subjective criteria being exercised;
administrator knowledge of risks being outdated; inefficiency
arising from fast changing risk scenarios; administrator not
capable of making a decision or making an incorrect decision in a
given context due to complexity of information; and, special
training requirements needed to keep administrator up to date on
emerging risks, among others.
[0021] Embodiments of the present invention may automatically apply
a risk mitigation measure in a scenario where a user may not have
posted a SNP but may be exposed to a security risk due to a SNP by
the user's roommate indicating that both the user and the roommate
are currently at a vacation spot located far from a location where
their rooms may be situated. Embodiments of the present invention
may also automatically apply risk mitigation measures in an
embodiment where the user's SNP may not pose a security risk, but
one or more follow-up SNPs being posted by others in response to
the user's SNP may generate a potential security risk. For example,
a follow-up SNP by a member in user's network may point out that an
expensive car remains parked at a garage in user's residence while
user is out of town, thus generating a risk. Embodiments of the
present invention may recommend and apply risk mitigation measures
in such a scenario.
[0022] In one scenario, embodiments of the present invention may
just remove a user's current location included in the user's SNP as
a risk mitigation measure. In another scenario, embodiments of the
present invention may delay the posting of a SNP to a social
network temporally until a security risk is eliminated. In another
scenario, embodiments of the present invention may allow sensitive
data in a SNP to be selectively viewable only by a few people
within the user's network that are on a list of "trusted members".
In another scenario, embodiments of the present invention may allow
a complete unedited version of a SNP to be available for view by a
few trusted members in user's network while simultaneously allowing
an edited version that has been stripped of sensitive information,
to be viewable by others. In another scenario, embodiments of the
present invention may strip off metadata such as, for example, GPS
location information and time stamps associated with a SNP before
it gets posted to a social network. In another scenario,
embodiments of the present invention may blur a portion of a
digital image in order that a landmark present in the image may be
rendered unrecognizable before SNP is posted to a social network.
In another scenario, embodiments of the present invention may trim
a portion of a digital image. In another scenario, embodiments of
the present invention may permit only selected people in a user's
social network to view certain data flagged to be sensitive.
[0023] The present invention will now be described in detail with
reference to the figures. All brand names and/or trademarks used
herein are the property of their respective owners.
[0024] FIG. 1 is a functional block diagram illustrating an
exemplary data privacy and information safety environment 100 for
managing data privacy and information safety associated with data
posted to social networks. In various embodiments of the present
invention data privacy and information safety environment 100 may
include a computing device 102 and a server 112, connected over
network 110.
[0025] The network 110 represents a worldwide collection of
networks and gateways, such as the Internet, that use various
protocols to communicate with one another, such as Lightweight
Directory Access Protocol (LDAP), Transport Control
Protocol/Internet Protocol (TCP/IP), Hypertext Transport Protocol
(HTTP), Wireless Application Protocol (WAP), etc. Network 110 may
also include a number of different types of networks, such as, for
example, an intranet, a local area network (LAN), or a wide area
network (WAN).
[0026] Computing device 102 represents a network connected user
computing device on which data privacy and information safety
associated with data posted to social networks will be managed, in
accordance with various embodiments of the invention. The computing
device 102 may be, for example, a mobile device, a smart phone, a
personal digital assistant, a netbook, a laptop computer, a tablet
computer, a desktop computer, or any type of computing device
capable of running a program and accessing a network, in accordance
with one or more embodiments of the invention. In an embodiment,
the computing device 102, and the server 112, which will be
explained later, may form part of an enterprise system. Computing
device 102 may include internal and external hardware components,
as depicted and described in further detail below with reference to
FIG. 4. In other embodiments, computing device 102 may represent,
for example, a local computing device 54A-N in a cloud computing
environment, as described in relation to FIGS. 4, 5, and 6, below.
In an embodiment, system components within computing device 102,
for example, RAM 30 (FIG. 4), may include read-only registers
and/or other data stores that contain device, network ID, user,
system date/time, and other system and user information that may be
accessible, for example, by application programming interfaces
(APIs). Computing device 102 may also support data and screen
capture, for example, by one or more proprietary or open source
screen capture APIs.
[0027] In one embodiment, the computing device 102 may include
applications such as a social network application 108 and a data
privacy & information safety 104. Social network application
108 represents an interface that may be used to access various
social networks. In an exemplary embodiment, Social network
application 108 may be, for example, an application that interfaces
with a network application, such as social network server interface
122 on server 112, both described in more detail below, or
interfaces with a local application residing on computing device
102, such as data privacy & information safety 104, described
in more detail below. In other embodiments, social network
application 108 may represent an interface that is integral to a
local application residing on computing device 102, such as the
data privacy & information safety 104. In various embodiments,
Social network application 108 may support monitoring of data
included in SNPs shared on social networks impacting a user, for
example, by one or more proprietary or open source APIs or add-ons,
so that an API or add-on may signal that data impacting the user
has been shared on a social network.
[0028] Social network server interface 122 represents an interface
that data privacy & information safety 104, social network
application 108 and a user utilizes to interact with a social
network. In one embodiment, social network server interface 122
represents an application that updates or otherwise augments the
information available on the risk mitigation data store 116, to be
described later. In one embodiment, extension application 126 may
represent an application that may permit a user to select the right
risk tolerance level and the appropriate privacy setting. In one
embodiment, social network server interface 122 may interact with
data privacy & information safety 104 in updating the contents
of the risk mitigation data store 116. In this mode, extension
application 126 may permit a user to receive up-to-date information
on new and evolving security risks based on the user's
characteristics.
[0029] Risk mitigation data store 116 represents a database that
includes information associated with risk mitigation. In one
embodiment, risk mitigation data store 116 may interact with
databases available on the worldwide web through social network
server interface 122 to collect and index security threats and
risks associated with data included in SNPs. In one embodiment,
risk mitigation data store 116, through social network server
interface 122, may represent a gateway to risk cataloguing
knowledge databases such as, for example, an online public safety
information database maintained by a governmental entity or a
private entity. In one embodiment, risk mitigation data store 116
may represent a database that includes organized collection of data
containing information corresponding to risk information
categories. In one embodiment, risk mitigation data store 116 may
represent a database that contains a listing of all databases
available on the worldwide web that contain information related to
security risks associated with data included in SNPs shared on
social networks. In one embodiment, risk mitigation data store 116
may be updated with new information via manual user entry through a
user interface on computing device 102 or through other means such
as by automatic periodic data transfers from an online database to
risk mitigation data store 116. In an exemplary embodiment, risk
mitigation data store 116 is stored locally on server 112, however
in other embodiments, context-sensitive translation &
reformatting data store 116 may be located remotely and accessed
via a network such as network 110.
[0030] Data privacy & information safety 104 operates to
dynamically evaluate, categorize, assign a security risk score,
recommend and undertake a risk mitigation measure to mitigate a
security risk associated with data included in a social network
post (SNP) shared on a social network.
[0031] FIG. 2 depicts modules that form part of data privacy &
information safety 104 of FIG. 1 that, in one embodiment, may
include: social network post receiving module 202, risk information
category analysis module 204, risk score assigning module 206, risk
score comparison module 208, risk mitigation measure determination
module 210, risk mitigation measure recommendation module 212, and
risk mitigation measure application module 214.
[0032] Social network post receiving module 202 may operate to
monitor a user's online social network account on an ongoing basis
and receive data associated with an original or secondary SNP
shared on a social network or to be shared on the social network.
Risk information category analysis module 204 may operate to
analyze data in the SNP for the presence of any potential security
threats or risks to the user from the data in the SNP. Risk score
assigning module 206 may operate to assign a risk score for an
identified risk information category that was assigned by risk
information category analysis module 204. Risk score comparison
module 208 may operate to compare the sum of all assigned risk
scores under all identified risk information categories for the
data in the SNP with the sum of the threshold risk scores found in
a table comprising threshold risk scores associated with multiple
risk information categories. Risk mitigation measure determination
module 210 may operate to determine one or more risk mitigation
measures that may reduce, minimize or neutralize the security risk
associated with the data. Risk mitigation measure recommendation
module 212 may operate to recommend a risk mitigation measure based
on a determination made by risk mitigation measure determination
module 210. Risk mitigation measure application module 214 may
operate to apply the risk mitigation mechanism identified by risk
mitigation measure determination module 210 and recommended by risk
mitigation measure recommendation module 212.
[0033] FIG. 3 is a flowchart depicting operational steps of data
privacy & information safety 104, in accordance with one
embodiment of the present invention. Steps depicted in FIG. 3 may
be implemented using one or more modules of a computer program such
as the data privacy & information safety 104, and executed by a
processor of a computer such as computing device 102 or server
112.
[0034] Social network post receiving module 202 may monitor a
user's online social network account on an ongoing basis and at
301a, social network post receiving module 202 may receive data
associated with an original SNP shared on a social network or to be
shared on the social network. In one embodiment, the SNP may be
shared on a user's personal web-page on the social network either
by the user or a source associated with the user. In one
embodiment, the source may represent another person connected to
the user and authorized by the user to share data on the user's
personal web-page on the social network. The received data may
include text, digital images, audio and video.
[0035] In one embodiment, at 301b, social network post receiving
module 202 may receive a secondary SNP posted to the social network
that may represent data associated with an original post by the
user. In one embodiment, the secondary SNP may represent data
corresponding to a post that includes information related to the
user, but not posted by the user, but posted by another source. In
one embodiment, social network post receiving module 202 may
identify an association between the original SNP and the secondary
SNP.
[0036] At 303, risk information category analysis module 204 may
analyze the SNP for the presence of any potential security threats
or risks to the user from the data in the SNP. Risk information
category analysis module 204 may accomplish this by first
identifying one or more risk information categories associated with
the data.
[0037] At 305, risk information category analysis module 204 may
identify one or more risk information categories associated with
the data. The risk information categories assigned by risk
information category analysis module 204 include: time, location,
activity type, trend, frequency of repeated occurrence of an event,
and targeted population associated with the data. As illustrative
examples, the risk information categories may include: a targeted
population of children between five years old and ten years old,
female teenagers between the ages of 16 and 18 years old, people
residing in individual houses, home owners, teenagers, and seniors
above 70 years old; locations that cannot be reached from the U.S.
mainland such as tourist resorts in the Caribbean islands; and,
house break-in events that occur more often than 50 incidents/month
within a city. In determining all risk information categories
associated with the data, risk information category analysis module
204 may access information stored in risk mitigation data store
116. In one embodiment, risk information category analysis module
204 may access a data analytics engine (not shown) via network 110
to help identify one or more risk information categories associated
with the received data. In one embodiment, risk information
category analysis module 204 may assign more than one risk
information categories to a single set of data.
[0038] In an exemplary embodiment that includes a securing risk of
residences of individuals of Chinese ethnicity being burglarized
during the Chinese New Year may assign the following risk
information categories to the data included in a SNP: location (is
user expected to be away from home for a period greater than a
predefined time duration); time (does the data describe an event
scheduled to occur in the future or at present); activity (does the
data describe an activity that is expected to occur longer than a
predefined time duration); and, ethnicity (whether the user is of
Chinese ancestry).
[0039] In one exemplary embodiment, in order to identify a risk
information category of location, risk information category
analysis module 204 may determine whether a location of the user
may be identified from the data. In one scenario, the location may
be inferred by a geographical location spelt out in the data. In
another scenario, it may be inferred by a famous physical landmark
included in a digital image present in the data. The same data may
also include a time category associated with it, such as for
example, a text string included in the data indicating that a
picture included in the data was taken 10 days ago.
[0040] At 307, risk score assigning module 206 may assign a risk
score for an identified risk information category that was assigned
by risk information category analysis module 204. Risk score
assigning module 206 may assign the risk score for the risk
information category based on a sensitive item contained in the
data. The sensitive item may include: a timeliness of a statement
contained in the data, a location indicated in the data, a
precision of the statement contained in the data, an activity
indicated in the data, and a risk-susceptible item contained in the
data. In assigning the risk score, risk score assigning module 206
may access information available from the risk mitigation data
store 116 and information available on the worldwide web and
accessible via social network server interface 122. Risk score
assigning module 206 may utilize such accessed information in order
to assign a risk score data based on predefined risk scores
associated with the assigned category, as catalogued in a table
format available within or through the risk mitigation data store
116. In one embodiment, assignment of the risk score may be based
on one or more characteristics associated with the user, the
characteristics including: an activity type mentioned in the data
in the SNP, a location indicated in the data, a time mentioned in
the data, an age of the user, a marital status of the user, a
profession of the user, an household income associated with the
user, a gender of the user, an ethnicity associated with the user,
and a recent criminal activity in an area associated with the user.
In one embodiment, risk score assigning module 206 may use a
defined social network privacy goal of the user to determine the
risk score associated with a risk information category
corresponding to the data included in the SNP.
[0041] In an exemplary embodiment that includes a risk information
category of time, risk score assigning module 206 may assign a risk
score based on predefined characteristics associated with the user
as follows: an event described in the data occurred in the
past=risk score of 0; event to occur at least one month in the
future=risk score of 1; and event occurring at the instant the data
was posted=risk score of 2; and event to occur within the next 30
days=risk score of 3.
[0042] In an exemplary embodiment that includes a risk information
category of frequency of repeated occurrence of an event that
corresponds to several children kidnapping incidents that recently
occurred in the city of Miami, Fla. risk score assigning module 206
may assign a risk score based on predefined characteristics
associated with the user as follows: a user with children
vacationing in Albany, N.Y.=risk score of 0; a user with children
vacationing in Miami, Fla.=risk score of 0; and a user with
children permanently residing in Miami, Fla.=risk score of 2.
[0043] Risk score assigning module 206 may then calculate a sum of
all assigned risk scores under all identified risk information
categories for the data. In one embodiment, risk score assigning
module 206 may calculate a weighted average of all assigned risk
scores under all identified risk information categories for the
data. Generally, different risk scores assigned to different
identified risk information categories may carry a different weight
on a security risk on the user from the SNP depending on the
characteristics associated with the user. In one embodiment, risk
score assigning module 206 may use other statistical analysis
methods known in the art to incorporate all assigned risk scores
associated with the data depending on the relative security risks
posed by each of the assigned risk scores.
[0044] At 309, risk score comparison module 208 may compare the sum
of all assigned risk scores under all identified risk information
categories for the data in the SNP with the sum of the threshold
risk scores found in a table comprising threshold risk scores
associated with multiple risk information categories, the threshold
risk scores in the table being customized for one or more
characteristics associated with the user.
[0045] In one embodiment, the table may reside in the risk
mitigation data store and accessible by risk score assigning module
206 via the network 110. In instances where the sum is greater than
the threshold risk score in the table, it would indicate that there
is a security risk to the user from the data is deemed unsafe and
therefore a risk mitigation measure may be needed. When the sum is
greater than the threshold risk score, risk score assigning module
206 may transfer the data and the associated analyses to risk
mitigation measure determination module 210.
[0046] At 311, risk mitigation measure determination module 210 may
operate to determine one or more risk mitigation measures that may
reduce, minimize or neutralize the security risk associated with
the data. The risk mitigation measures evaluated by risk mitigation
measure determination module 210 may include: modifying the data,
deleting the data, retaining the data wherein the data is not
posted to the social network component until receiving an
instruction to post the data to the social network, removing
metadata associated with the received data, and communicating a
message regarding the data to a device. In one embodiment
recommending a risk mitigation measure may be based on a risk
tolerance setting of the user.
[0047] At 313, risk mitigation measure recommendation module 212
may operate to recommend a risk mitigation measure based on a
determination made by risk mitigation measure determination module
210. In one scenario, the risk mitigation measure recommendation
may include a suggestion to just remove a user's current location
included in the user's SNP as a risk mitigation measure. In another
scenario, the risk mitigation measure recommendation may include a
suggestion to delay the posting of a SNP to a social network
temporally until a security risk is eliminated. In another
scenario, the risk mitigation measure recommendation may include a
suggestion to allow sensitive data in a SNP to be selectively
viewable only by a few people within the user's network that are on
a list of "trusted members". In another scenario, the risk
mitigation measure recommendation may include a suggestion to allow
a complete unedited version of a SNP to be available for view by a
few trusted members in user's network while simultaneously allowing
an edited version that has been stripped of sensitive information,
to be viewable by others. In another scenario, the risk mitigation
measure recommendation may include a suggestion to strip off
metadata such as, for example, GPS location information and time
stamps associated with a SNP before it gets posted to a social
network. In another scenario, the risk mitigation measure
recommendation may include a suggestion to blur a portion of a
digital image in order that a landmark present in the image may be
rendered unrecognizable before SNP is posted to a social network.
In another scenario, the risk mitigation measure recommendation may
include a suggestion to trim a portion of a digital image. In
another scenario the risk mitigation measure recommendation may
include a suggestion for permitting only selected individuals in a
user's social network to view certain data flagged to be sensitive.
In another scenario, the risk mitigation measure recommendation may
include posting the data on a social network after a defined period
of time has elapsed. In another scenario, may include a suggestion
for permitting only selected individuals in the user's social
network to be able to view the whole unedited SNP after a defined
period of time has elapsed. In another scenario, the risk
mitigation measure recommendation may include a combination of two
or more mitigation measure suggestions mentioned above.
[0048] Risk mitigation measure recommendation module 212 may
communicate the risk mitigation measure recommendation to the user
using understandable language. In one embodiment, risk mitigation
measure recommendation module 212 may display a message on a GUI
display of the computing device indicating the risk score and the
risk mitigation measure that is being recommended for that risk
score.
[0049] At 315, risk mitigation measure application module 214 may
operate to apply the risk mitigation mechanism identified in step
313. In one embodiment applying, a risk mitigation measure may be
based on a risk tolerance setting of the user. In one embodiment
where the data in a SNP is previously shared on the social network,
applying a risk mitigation measure may include generating new data
based on applying a risk mitigation measure and replacing the data
on the social network with the new data. In one embodiment, risk
mitigation measure recommendation module 212 may automatically
apply a risk mitigation measure without first recommending the same
to the user. In one embodiment, Executing the risk mitigation
measure may include SNP modification including: removing one or
more text strings contained in the data, blurring a portion of a
digital image included in the data, deleting a portion of a digital
image included in the data, and permitting a selected group of
individuals to view the data. Executing the risk mitigation measure
may also include deleting or erasing the entire SNP. Executing the
risk mitigation measure may also include retaining the SNP and
delaying posting of the SNP to the social network, and storing the
same in a database. Executing the risk mitigation measure may also
include removing metadata associated with the SNP including
removing a location metadata, and a time stamp metadata. Finally,
executing the risk mitigation measure may also include
communicating a message include sending an alert to a mobile
device. In one embodiment, risk mitigation measure recommendation
module 212 may upload a modified SNP the social network after a
risk mitigation measure has been applied to it.
[0050] The programs described herein are identified based upon the
application for which they are implemented in a specific embodiment
of the invention. However, it should be appreciated that any
particular program nomenclature herein is used merely for
convenience, and thus the invention should not be limited to use
solely in any specific application identified and/or implied by
such nomenclature.
[0051] Based on the foregoing, a computer system, method, and
computer program product have been disclosed. However, numerous
modifications and substitutions can be made without deviating from
the scope of the present invention. Therefore, the present
invention has been disclosed by way of example and not
limitation.
[0052] It is understood in advance that although this disclosure
includes a detailed description on cloud computing, implementation
of the teachings recited herein are not limited to a cloud
computing environment. Rather, embodiments of the present invention
are capable of being implemented in conjunction with any other type
of computing environment now known or later developed.
[0053] Cloud computing is a model of service delivery for enabling
convenient, on-demand network access to a shared pool of
configurable computing resources (e.g. networks, network bandwidth,
servers, processing, memory, storage, applications, virtual
machines, and services) that can be rapidly provisioned and
released with minimal management effort or interaction with a
provider of the service. This cloud model may include at least five
characteristics, at least three service models, and at least four
deployment models.
[0054] Characteristics are as follows:
[0055] On-demand self-service: a cloud consumer can unilaterally
provision computing capabilities, such as server time and network
storage, as needed automatically without requiring human
interaction with the service's provider.
[0056] Broad network access: capabilities are available over a
network and accessed through standard mechanisms that promote use
by heterogeneous thin or thick client platforms (e.g., mobile
phones, laptops, and PDAs).
[0057] Resource pooling: the provider's computing resources are
pooled to serve multiple consumers using a multi-tenant model, with
different physical and virtual resources dynamically assigned and
reassigned according to demand. There is a sense of location
independence in that the consumer generally has no control or
knowledge over the exact location of the provided resources but may
be able to specify location at a higher level of abstraction (e.g.,
country, state, or datacenter).
[0058] Rapid elasticity: capabilities can be rapidly and
elastically provisioned, in some cases automatically, to quickly
scale out and rapidly released to quickly scale in. To the
consumer, the capabilities available for provisioning often appear
to be unlimited and can be purchased in any quantity at any
time.
[0059] Measured service: cloud systems automatically control and
optimize resource use by leveraging a metering capability at some
level of abstraction appropriate to the type of service (e.g.,
storage, processing, bandwidth, and active user accounts). Resource
usage can be monitored, controlled, and reported providing
transparency for both the provider and consumer of the utilized
service.
[0060] Service Models are as follows:
[0061] Software as a Service (SaaS): the capability provided to the
consumer is to use the provider's applications running on a cloud
infrastructure. The applications are accessible from various client
devices through a thin client interface such as a web browser
(e.g., web-based e-mail). The consumer does not manage or control
the underlying cloud infrastructure including network, servers,
operating systems, storage, or even individual application
capabilities, with the possible exception of limited user-specific
application configuration settings.
[0062] Platform as a Service (PaaS): the capability provided to the
consumer is to deploy onto the cloud infrastructure
consumer-created or acquired applications created using programming
languages and tools supported by the provider. The consumer does
not manage or control the underlying cloud infrastructure including
networks, servers, operating systems, or storage, but has control
over the deployed applications and possibly application hosting
environment configurations.
[0063] Infrastructure as a Service (IaaS): the capability provided
to the consumer is to provision processing, storage, networks, and
other fundamental computing resources where the consumer is able to
deploy and run arbitrary software, which can include operating
systems and applications. The consumer does not manage or control
the underlying cloud infrastructure but has control over operating
systems, storage, deployed applications, and possibly limited
control of select networking components (e.g., host firewalls).
[0064] Deployment Models are as follows:
[0065] Private cloud: the cloud infrastructure is operated solely
for an organization. It may be managed by the organization or a
third party and may exist on-premises or off-premises.
[0066] Community cloud: the cloud infrastructure is shared by
several organizations and supports a specific community that has
shared concerns (e.g., mission, security requirements, policy, and
compliance considerations). It may be managed by the organizations
or a third party and may exist on-premises or off-premises.
[0067] Public cloud: the cloud infrastructure is made available to
the general public or a large industry group and is owned by an
organization selling cloud services.
[0068] Hybrid cloud: the cloud infrastructure is a composition of
two or more clouds (private, community, or public) that remain
unique entities but are bound together by standardized or
proprietary technology that enables data and application
portability (e.g., cloud bursting for load-balancing between
clouds).
[0069] A cloud computing environment is service oriented with a
focus on statelessness, low coupling, modularity, and semantic
interoperability. At the heart of cloud computing is an
infrastructure comprising a network of interconnected nodes.
[0070] Referring now to FIG. 4, a schematic of an example of a
cloud computing node is shown. Cloud computing node 10 is only one
example of a suitable cloud computing node and is not intended to
suggest any limitation as to the scope of use or functionality of
embodiments of the invention described herein. Regardless, cloud
computing node 10 is capable of being implemented and/or performing
any of the functionality set forth hereinabove.
[0071] In cloud computing node 10 there is a computer system/server
12, which is operational with numerous other general purpose or
special purpose computing system environments or configurations.
Examples of well-known computing systems, environments, and/or
configurations that may be suitable for use with computer
system/server 12 include, but are not limited to, personal computer
systems, server computer systems, thin clients, thick clients,
hand-held or laptop devices, multiprocessor systems,
microprocessor-based systems, set top boxes, programmable consumer
electronics, network PCs, minicomputer systems, mainframe computer
systems, and distributed cloud computing environments that include
any of the above systems or devices, and the like.
[0072] Computer system/server 12 may be described in the general
context of computer system-executable instructions, such as program
modules, being executed by a computer system. Generally, program
modules may include routines, programs, objects, components, logic,
data structures, and so on that perform particular tasks or
implement particular abstract data types. Computer system/server 12
may be practiced in distributed cloud computing environments where
tasks are performed by remote processing devices that are linked
through a communications network. In a distributed cloud computing
environment, program modules may be located in both local and
remote computer system storage media including memory storage
devices.
[0073] As shown in FIG. 4, computer system/server 12 in cloud
computing node 10 is shown in the form of a general-purpose
computing device. The components of computer system/server 12 may
include, but are not limited to, one or more processors or
processing units 16, a system memory 28, and a bus 18 that couples
various system components including system memory 28 to processor
16.
[0074] Bus 18 represents one or more of any of several types of bus
structures, including a memory bus or memory controller, a
peripheral bus, an accelerated graphics port, and a processor or
local bus using any of a variety of bus architectures. By way of
example, and not limitation, such architectures include Industry
Standard Architecture (ISA) bus, Micro Channel Architecture (MCA)
bus, Enhanced ISA (EISA) bus, Video Electronics Standards
Association (VESA) local bus, and Peripheral Component
Interconnects (PCI) bus.
[0075] Computer system/server 12 typically includes a variety of
computer system readable media. Such media may be any available
media that is accessible by computer system/server 12, and it
includes both volatile and non-volatile media, removable and
non-removable media.
[0076] System memory 28 can include computer system readable media
in the form of volatile memory, such as random access memory (RAM)
30 and/or cache memory 32. Computer system/server 12 may further
include other removable/non-removable, volatile/non-volatile
computer system storage media. By way of example only, storage
system 34 can be provided for reading from and writing to a
non-removable, non-volatile magnetic media (not shown and typically
called a "hard drive"). Although not shown, a magnetic disk drive
for reading from and writing to a removable, non-volatile magnetic
disk (e.g., a "floppy disk"), and an optical disk drive for reading
from or writing to a removable, non-volatile optical disk such as a
CD-ROM, DVD-ROM or other optical media can be provided. In such
instances, each can be connected to bus 18 by one or more data
media interfaces. As will be further depicted and described below,
memory 28 may include at least one program product having a set
(e.g., at least one) of program modules that are configured to
carry out the functions of embodiments of the invention.
[0077] Program/utility 40, having a set (at least one) of program
modules 42, may be stored in memory 28 by way of example, and not
limitation, as well as an operating system, one or more application
programs, other program modules, and program data. Each of the
operating system, one or more application programs, other program
modules, and program data or some combination thereof, may include
an implementation of a networking environment. Program modules 42
generally carry out the functions and/or methodologies of
embodiments of the invention as described herein.
[0078] Computer system/server 12 may also communicate with one or
more external devices 14 such as a keyboard, a pointing device, a
display 24, etc.; one or more devices that enable a user to
interact with computer system/server 12; and/or any devices (e.g.,
network card, modem, etc.) that enable computer system/server 12 to
communicate with one or more other computing devices. Such
communication can occur via Input/Output (I/O) interfaces 22. Still
yet, computer system/server 12 can communicate with one or more
networks such as a local area network (LAN), a general wide area
network (WAN), and/or a public network (e.g., the Internet) via
network adapter 20. As depicted, network adapter 20 communicates
with the other components of computer system/server 12 via bus 18.
It should be understood that although not shown, other hardware
and/or software components could be used in conjunction with
computer system/server 12. Examples, include, but are not limited
to: microcode, device drivers, redundant processing units, external
disk drive arrays, RAID systems, tape drives, and data archival
storage systems, etc.
[0079] Referring now to FIG. 5, illustrative cloud computing
environment 50 is depicted. As shown, cloud computing environment
50 comprises one or more cloud computing nodes 10 with which local
computing devices used by cloud consumers, such as, for example,
personal digital assistant (PDA) or cellular telephone 54A, desktop
computer 54B, laptop computer 54C, and/or automobile computer
system 54N may communicate. Nodes 10 may communicate with one
another. They may be grouped (not shown) physically or virtually,
in one or more networks, such as Private, Community, Public, or
Hybrid clouds as described hereinabove, or a combination thereof.
This allows cloud computing environment 50 to offer infrastructure,
platforms and/or software as services for which a cloud consumer
does not need to maintain resources on a local computing device. It
is understood that the types of computing devices 54A-N shown in
FIG. 2 are intended to be illustrative only and that computing
nodes 10 and cloud computing environment 50 can communicate with
any type of computerized device over any type of network and/or
network addressable connection (e.g., using a web browser).
[0080] Referring now to FIG. 6, a set of functional abstraction
layers provided by cloud computing environment 50 (FIG. 5) is
shown. It should be understood in advance that the components,
layers, and functions shown in FIG. 6 are intended to be
illustrative only and embodiments of the invention are not limited
thereto. As depicted, the following layers and corresponding
functions are provided:
[0081] Hardware and software layer 60 includes hardware and
software components. Examples of hardware components include:
mainframes 61; RISC (Reduced Instruction Set Computer) architecture
based servers 62; servers 63; blade servers 64; storage devices 65;
and networks and networking components 66. In some embodiments,
software components include network application server software 67
and database software 68.
[0082] Virtualization layer 70 provides an abstraction layer from
which the following examples of virtual entities may be provided:
virtual servers 71; virtual storage 72; virtual networks 73,
including virtual private networks; virtual applications and
operating systems 74; and virtual clients 75.
[0083] In one example, management layer 80 may provide the
functions described below. Resource provisioning 81 provides
dynamic procurement of computing resources and other resources that
are utilized to perform tasks within the cloud computing
environment. Metering and Pricing 82 provide cost tracking as
resources are utilized within the cloud computing environment, and
billing or invoicing for consumption of these resources. In one
example, these resources may comprise application software
licenses. Security provides identity verification for cloud
consumers and tasks, as well as protection for data and other
resources. User portal 83 provides access to the cloud computing
environment for consumers and system administrators. Service level
management 84 provides cloud computing resource allocation and
management such that required service levels are met. Service Level
Agreement (SLA) planning and fulfillment 85 provide pre-arrangement
for, and procurement of, cloud computing resources for which a
future requirement is anticipated in accordance with an SLA.
[0084] Workloads layer 90 provides examples of functionality for
which the cloud computing environment may be utilized. Examples of
workloads and functions which may be provided from this layer
include: mapping and navigation 91; software development and
lifecycle management 92; virtual classroom education delivery 93;
data analytics processing 94; transaction processing 95; and, data
privacy & information safety program 96.
[0085] The present invention may be a system, a method, and/or a
computer program product. The computer program product may include
a computer readable storage medium (or media) having computer
readable program instructions thereon for causing a processor to
carry out aspects of the present invention.
[0086] The computer readable storage medium can be a tangible
device that can retain and store instructions for use by an
instruction execution device. The computer readable storage medium
may be, for example, but is not limited to, an electronic storage
device, a magnetic storage device, an optical storage device, an
electromagnetic storage device, a semiconductor storage device, or
any suitable combination of the foregoing. A non-exhaustive list of
more specific examples of the computer readable storage medium
includes the following: a portable computer diskette, a hard disk,
a random access memory (RAM), a read-only memory (ROM), an erasable
programmable read-only memory (EPROM or Flash memory), a static
random access memory (SRAM), a portable compact disc read-only
memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a
floppy disk, a mechanically encoded device such as punch-cards or
raised structures in a groove having instructions recorded thereon,
and any suitable combination of the foregoing. A computer readable
storage medium, as used herein, is not to be construed as being
transitory signals per se, such as radio waves or other freely
propagating electromagnetic waves, electromagnetic waves
propagating through a waveguide or other transmission media (e.g.,
light pulses passing through a fiber-optic cable), or electrical
signals transmitted through a wire.
[0087] Computer readable program instructions described herein can
be downloaded to respective computing/processing devices from a
computer readable storage medium or to an external computer or
external storage device via a network, for example, the Internet, a
local area network, a wide area network and/or a wireless network.
The network may comprise copper transmission cables, optical
transmission fibers, wireless transmission, routers, firewalls,
switches, gateway computers and/or edge servers. A network adapter
card or network interface in each computing/processing device
receives computer readable program instructions from the network
and forwards the computer readable program instructions for storage
in a computer readable storage medium within the respective
computing/processing device.
[0088] Computer readable program instructions for carrying out
operations of the present invention may be assembler instructions,
instruction-set-architecture (ISA) instructions, machine
instructions, machine dependent instructions, microcode, firmware
instructions, state-setting data, or either source code or object
code written in any combination of one or more programming
languages, including an object oriented programming language such
as Smalltalk, C++ or the like, and conventional procedural
programming languages, such as the "C" programming language or
similar programming languages. The computer readable program
instructions may execute entirely on the user's computer, partly on
the user's computer, as a stand-alone software package, partly on
the user's computer and partly on a remote computer or entirely on
the remote computer or server. In the latter scenario, the remote
computer may be connected to the user's computer through any type
of network, including a local area network (LAN) or a wide area
network (WAN), or the connection may be made to an external
computer (for example, through the Internet using an Internet
Service Provider). In some embodiments, electronic circuitry
including, for example, programmable logic circuitry,
field-programmable gate arrays (FPGA), or programmable logic arrays
(PLA) may execute the computer readable program instructions by
utilizing state information of the computer readable program
instructions to personalize the electronic circuitry, in order to
perform aspects of the present invention.
[0089] Aspects of the present invention are described herein with
reference to flowchart illustrations and/or block diagrams of
methods, apparatus (systems), and computer program products
according to embodiments of the invention. It will be understood
that each block of the flowchart illustrations and/or block
diagrams, and combinations of blocks in the flowchart illustrations
and/or block diagrams, can be implemented by computer readable
program instructions.
[0090] These computer readable program instructions may be provided
to a processor of a general purpose computer, special purpose
computer, or other programmable data processing apparatus to
produce a machine, such that the instructions, which execute via
the processor of the computer or other programmable data processing
apparatus, create means for implementing the functions/acts
specified in the flowchart and/or block diagram block or blocks.
These computer readable program instructions may also be stored in
a computer readable storage medium that can direct a computer, a
programmable data processing apparatus, and/or other devices to
function in a particular manner, such that the computer readable
storage medium having instructions stored therein comprises an
article of manufacture including instructions which implement
aspects of the function/act specified in the flowchart and/or block
diagram block or blocks.
[0091] The computer readable program instructions may also be
loaded onto a computer, other programmable data processing
apparatus, or other device to cause a series of operational steps
to be performed on the computer, other programmable apparatus or
other device to produce a computer implemented process, such that
the instructions which execute on the computer, other programmable
apparatus, or other device implement the functions/acts specified
in the flowchart and/or block diagram block or blocks.
[0092] The flowchart and block diagrams in the figures illustrate
the architecture, functionality, and operation of possible
implementations of systems, methods, and computer program products
according to various embodiments of the present invention. In this
regard, each block in the flowchart or block diagrams may represent
a module, segment, or portion of instructions, which comprises one
or more executable instructions for implementing the specified
logical function(s). In some alternative implementations, the
functions noted in the block may occur out of the order noted in
the figures. For example, two blocks shown in succession may, in
fact, be executed substantially concurrently, or the blocks may
sometimes be executed in the reverse order, depending upon the
functionality involved. It will also be noted that each block of
the block diagrams and/or flowchart illustration, and combinations
of blocks in the block diagrams and/or flowchart illustration, can
be implemented by special purpose hardware-based systems that
perform the specified functions or acts or carry out combinations
of special purpose hardware and computer instructions.
[0093] The many features and advantages of the present invention
are apparent from the written description, and thus, it is intended
by the appended claims to cover all such features and advantages of
the invention. Further, since numerous modifications and changes
will readily occur to those skilled in the art, it is not desired
to limit the invention to the exact construction and operation as
illustrated and described. Hence, all suitable modifications and
equivalents may be considered to fall within the scope of the
invention.
* * * * *