Privacy policy inference of user uploaded

PRIVACY POLICY INFERENCE OF USER-UPLOADED
IMAGES ON CONTENT SHARING SITES
Abstract—With the increasing volume of images users share through social sites,
maintaining privacy has become a major problem, as demonstrated by a recent
wave of publicized incidents where users inadvertently shared personal
information. In light of these incidents, the need of tools to help users control
access to their shared content is apparent. Toward addressing this need, we propose
an Adaptive Privacy Policy Prediction (A3P) system to help users compose privacy
settings for their images. We examine the role of social context, image content, and
metadata as possible indicators of users’ privacy preferences. We propose a two-
level framework which according to the user’s available history on the site,
determines the best available privacy policy for the user’s images being uploaded.
Our solution relies on an image classification framework for image categories
which may be associated with similar policies, and on a policy prediction
algorithm to automatically generate a policy for each newly uploaded image, also
according to users’ social features. Over time, the generated policies will follow
the evolution of users’ privacy attitude. We provide the results of our extensive
evaluation over 5,000 policies, which demonstrate the effectiveness of our system,
with prediction accuracies over 90 percent.

EXISTING SYSTEM:
Several recent works have studied how to automate the task of privacy settings
Bonneau et al. proposed the concept of privacy suites which recommend to users a
suite of privacy settings that n“expert” users or other trusted friends have already
set, so that normal users can either directly choose a setting or only need to do
minor modification. Similarly, Danezis proposed a machine-learning based
approach to automatically extract privacy settings from the social context within
which the data is produced. Parallel to the work of Danezis, Adu-Oppong et al.
develop privacy settings based on a concept of “Social Circles” which consist of
clusters of friends formed by partitioning users’ friend lists. Ravichandran et al.
studied how to predict a user’s privacy preferences for location-based data (i.e.,
share her location or not) based on location and time of day. Fang et al. proposed a
privacy wizard to help users grant privileges to their friends. The wizard asks users
to first assign privacy labels to selected friends, and then uses this as input to
construct a classifier which classifies friends based on their profiles and
automatically assign privacy labels to the unlabeled friends. More recently,
Klemperer et al. studied whether the keywords and captions with which users tag
their photos can be used to help users more intuitively create and maintain access-

control policies. Their findings are inline with our approach: tags created for
organizational purposes can be repurposed to help create reasonably accurate
access-controlrules.
PROPOSED SYSTEM:
We propose an Adaptive Privacy Policy Prediction (A3P) system which aims to
provide users a hassle free privacy settings experience by automatically generating
personalized policies. The A3P system handles user uploaded images, and factors
in the following criteria that influence one’s privacy settings of images: The
impact of social environment and personal characteristics. Social context of users,
such as their profile information and relationships with others may provide useful
information regarding users’ privacy preferences. For example, users interested in
photography may like to share their photos with other amateur photographers.
Users who have several family members among their social contacts may share
with them pictures related to family events. However, using common policies
across all users or across users with similar traits may be too simplistic and not
satisfy individual preferences. Users may have drastically different opinions even
on the same type of images. For example, a privacy adverse person may be willing
to share all his personal images while a more conservative person may just want to

share personal images with his family members. In light of these considerations, it
is important to find the balancing point between the impact of social environment
and users’ individual characteristics in order to predict the policies that match each
individual’s needs. Moreover, individuals may change their overall attitude toward
privacy as time passes. In order to develop a personalized policy recommendation
system, such changes on privacy opinions should be carefully considered. The role
of image’s content and metadata. In general, similar images often incur similar
privacy preferences, especially when people appear in the images. For example,
one may upload several photos of his kids and specify that only his family
members are allowed to see these photos. He may upload some other photos of
landscapes which he took as a hobby and for these photos, he may set privacy
preference allowing anyone to view and comment the photos. Analyzing the visual
content may not be sufficient to capture users’ privacy preferences. Tags and other
metadata are indicative of the social context of the image, including where it was
taken and why , and also provide a synthetic description of images, complementing
the information obtained from visual content analysis.

Module 1
A3P-CORE
There are two major components in A3P-core: (i) Image classification and (ii)
Adaptive policy prediction. For each user, his/her images are first classified based
on content and metadata. Then, privacy policies of each category of images are
analyzed for the policy prediction. Adopting a two-stage approach is more suitable
for policy recommendation than applying the common one-stage data mining
approaches to mine both image features and policies together. Recall that when a
user uploads a new image, the user is waiting for a recommended policy. The two-
stage approach allows the system to employ the first stage to classify the new
image and find the candidate sets of images for the subsequent policy
recommendation. As for the one-stage mining approach, it would not be able to
locate the right class of the new image because its classification criteria needs both
image features and policies whereas the policies of the new image are not available
yet. Moreover, combining both image features and policies into a single classifier
would lead to a system which is very dependent to the specific syntax of the
policy. If a change in the supported policies were to be introduced, the whole
learning model would need to change.
Module 2

Image Classification
To obtain groups of images that may be associated with similar privacy
preferences, we propose a hierarchical image classification which classifies images
first based on their contents and then refine each category into subcategories based
on their metadata. Images that do not have metadata will be grouped only by
content. Such a hierarchical classification gives a higher priority to image content
and minimizes the influence of missing tags. Note that it is possible that some
images are included in multiple categories as long as they contain the typical
content features or metadata of those categories. The content-based classification
creates two categories: “landscape” and “kid”. Images C, D, E and F are included
in both categories as they show kids playing outdoor which satisfy the two themes:
“landscape” and “kid”. These two categories are further divided into subcategories
based on tags associated with the images. As a result, we obtain two subcategories
under each theme respectively. Notice that image G is not shown in any
subcategory as it does not have any tag; image A shows up in both subcategories
because it has tags indicating both “beach” and “wood”.
Module 3
PolicyMining

We propose a hierarchical mining approach for policy mining. Our approach
leverages association rule mining techniques to discover popular patterns in
policies. Policy mining is carried out within the same category of the new image
because images in the same category are more likely under the similar level of
privacy protection. The basic idea of the hierarchical mining is to follow a natural
order in which a user defines a policy. Given an image, a user usually first decides
who can access the image, then thinks about what specific access rights (e.g., view
only or download) should be given, and finally refine the access conditions such as
setting the expiration date. Correspondingly, the hierarchical mining first look for
popular subjects defined by the user, then look for popular actions in the policies
containing the popular subjects, and finally for popular conditions in the policies
containing bothpopular subjects and conditions.
_ Step 1: In the same category of the new image, conduct association rule mining
on the subject component of polices. Let S1, S2; . . ., denote the subjects occurring
in policies. Each resultant rule is an implication of the form X ) Y, where X, Y _
fS1, S2; . . . ; g, and X Y ¼ ;. Among the obtained rules, we select the best rules
according to one of the interestingness measures, i.e., the generality of the rule,
defined using support and confidence as introduced in [16]. The selected rules
indicate the most popular subjects (i.e., single subject) or subject combinations
(i.e., multiple subjects) in policies. In the subsequent steps, we consider policies

which contain at least one subject in the selected rules. For clarity, we denote the
set of such policies as Gsub i corresponding to a selected rule Rsub i .
_ Step 2: In each policy set Gsub i , we now conduct association rule mining on the
action component. The result will be a set of association rules in the form of X ) Y,
where X, Y _fopen, comment, tag, downloadg, and X Y ¼ ;. Similar to the first
step, we will select the best rules according to the generality interestingness. This
time, the selected rules indicate the most popular combination of actions in policies
with respect to each particular subject or subject combination. Policies which do
not contain any action included in the selected rules will be removed. Given a
selected rule Ract j we denote the set of remaining policies as Gact j , and note that
Gact j _ Gsub
_ Step 3: We proceed to mine the condition component in each policy set Gact j .
Let attr1, attr2, ..., attrn denote the distinct attributes in the condition component of
the policies in Gact j . The association rules are in the same format of X ) Y but
with X, Y _fattr1, attr2; . . . ; attrng. Once the rules are obtained, we again select
the best rules using the generality interestingness measure. The selected rules give
us a set of attributes which often appear in policies. Similarly, we denote the
policies containing at least one attribute in the selected rule Rcon k as Gcon k and
Gconk _ Gact j

_ Step 4: This step is to generate candidate policies. Given Gcon k _ Gact j _ Gsub
i , we consider each corresponding series of best rules: Rcon kx , Ract jy and Rsub
iz . Candidate policies are required to possess all elements in Rcon kx , Ract jy and
Rsub iz Note that candidate policies may be different from the policies as result of
Step 3. This is because Step 3 will keep policies as long as they have one of the
attributes in the selected rules.
Module 4
A3P-SOCIAL
The A3P-social employs a multi-criteria inference mechanism that generates
representative policies by leveraging key information related to the user’s social
context and his general attitude toward privacy. As mentioned earlier, A3Psocial
will be invoked by the A3P-core in two scenarios. One is when the user is a newbie
of a site, and does not have enough images stored for the A3P-core to infer
meaningful and customized policies. The other is when the system notices
significant changes of privacy trend in the user’s social circle, which may be of
interest for the user to possibly adjust his/her privacy settings accordingly. In what
follows, we first present the types of social context considered by A3P-Social, and
then present the policy recommendation process.
CONCLUSION:

We have proposed an Adaptive Privacy Policy Prediction (A3P) system that helps
users automate the privacy policy
settings for their uploaded images. The A3P system provides a comprehensive
framework to infer privacy preferences based on the information available for a
given user. We also effectively tackled the issue of cold-start, leveraging social
context information. Our experimental study proves that our A3P is a practical tool
that offers significant improvements over current approaches to privacy.
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