49 research outputs found
Image sharing privacy policy on social networks using A3P
User Image sharing social site 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 an Adaptive Privacy Policy Prediction (A3P) system to help users compose privacy settings for their images. The 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 user’s social features. Image Sharing takes place both among previously established groups of known people or social circles and also increasingly with people outside the users social circles, for purposes of social discovery-to help them identify new peers and learn about peers interests and social surroundings, Sharing images within online content sharing sites, therefore, may quickly lead to unwanted disclosure. The aggregated information can result in unexpected exposure of one’s social environment and lead to abuse of one’s personal information
ADAPTIVE PRIVACY POLICY PREDICTION OF USER UPLOADED IMAGES ON CONTENT SHARING SITES
Usage of social media’s increased considerably in today world which enables the user to share their personal information like images with the other. This improved technology leads to privacy violation where the users are sharing the large volumes of images across more number of peoples. To provide security for the information, automated annotation of images are introduced which aims to create the meta data information about the images by using the novel approach called Semantic annotated Markovian Semantic Indexing(SMSI) for retrieving the images. To achieve this privacy settings for the people images we are using Adaptive Privacy Policy Prediction system. The proposed system automatically annotates the images using hidden Markov model and features are extracted by using color histogram and Scale-invariant feature transform (or SIFT) descriptor method. After annotating these images, semantic retrieval of images can be done by using Natural Language processing tool namely Word Net for measuring semantic similarity of annotated images in the database. Experimental result provides better retrieval performance when compare with the existing system
Understanding and Specifying Social Access Control Lists
Online social network (OSN) users upload millions of pieces of contenttoshare with otherseveryday. While asignificant portionofthiscontentis benign(andistypicallysharedwith all friends or all OSN users), there are certain pieces of content that are highly privacy sensitive. Sharing such sensitive content raises significant privacy concerns for users, and it becomes important for the user to protect this content from being exposed to the wrong audience. Today, most OSN services provide fine-grained mechanisms for specifying social access control lists (social ACLs, or SACLs), allowing users to restrict their sensitive content to a select subset of their friends. However, it remains unclear how these SACL mechanisms are used today. To design better privacy management tools for users, we need to first understand the usage and complexity of SACLs specified by users. In this paper, we present the first large-scale study of finegrained privacy preferences of over 1,000 users on Facebook, providing us with the first ground-truth information on how users specify SACLs on a social networking service. Overall, we find that a surprisingly large fraction (17.6%) of content is shared with SACLs. However, we also find that the SACL membership shows little correlation with either profile information or social network links; as a result, it is difficult to predict the subset of a user’s friends likely to appear in a SACL. On the flip side, we find that SACLs are often reused, suggesting that simply making recent SACLs available to users is likely tosignificantly reduce the burdenof privacy management on users. 1
Open challenges in relationship-based privacy mechanisms for social network services
[EN] Social networking services (SNSs) such as Facebook or Twitter have experienced an explosive
growth during the few past years. Millions of users have created their profiles on these services
because they experience great benefits in terms of friendship. SNSs can help people to maintain
their friendships, organize their social lives, start new friendships, or meet others that share
their hobbies and interests. However, all these benefits can be eclipsed by the privacy hazards
that affect people in SNSs. People expose intimate information of their lives on SNSs, and
this information affects the way others think about them. It is crucial that users be able to
control how their information is distributed through the SNSs and decide who can access it.
This paper presents a list of privacy threats that can affect SNS users, and what requirements
privacy mechanisms should fulfill to prevent this threats. Then, we review current approaches
and analyze to what extent they cover the requirementsThis article has been developed as a result of a mobility stay funded by the Erasmus Mundus Programme of the European Comission under the Transatlantic Partnership for Excellence in Engineering-TEE Project.LĂłpez FoguĂ©s, R.; Such Aparicio, JM.; Espinosa Minguet, AR.; GarcĂa-Fornes, A. (2015). Open challenges in relationship-based privacy mechanisms for social network services. International Journal of Human-Computer Interaction. 31(5):350-370. doi:10.1080/10447318.2014.1001300S35037031
User's Privacy in Recommendation Systems Applying Online Social Network Data, A Survey and Taxonomy
Recommender systems have become an integral part of many social networks and
extract knowledge from a user's personal and sensitive data both explicitly,
with the user's knowledge, and implicitly. This trend has created major privacy
concerns as users are mostly unaware of what data and how much data is being
used and how securely it is used. In this context, several works have been done
to address privacy concerns for usage in online social network data and by
recommender systems. This paper surveys the main privacy concerns, measurements
and privacy-preserving techniques used in large-scale online social networks
and recommender systems. It is based on historical works on security,
privacy-preserving, statistical modeling, and datasets to provide an overview
of the technical difficulties and problems associated with privacy preserving
in online social networks.Comment: 26 pages, IET book chapter on big data recommender system
Basic policy driven to shared photo on OSN’s
Now a days sharing images on social networking is common but maintaining security is a major issue, 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. The future framework naturally explains the pictures. With the expanding volume of pictures clients offer through social locales, keeping up protection has turned into a noteworthy issue, as exhibited by a late flood of plugged episodes where clients coincidentally shared individual data. In light of these occurrences, the need of apparatuses to push clients control access to their mutual substance is evident. Toward tending to this need, we propose a Versatile Protection Arrangement Expectation (A3P) framework to offer clients some assistance with composing security settings for their pictures. We look at the part of social setting, picture substance, and metadata as could be allowed pointers of clients' protection inclinations. We propose a two-level structure which as indicated by the client's accessible history on the site, decides the best accessible security approach for the client's pictures being transferred. Our answer depends on a picture grouping structure for picture classes which may be connected with comparative arrangements, and on a strategy expectation calculation to naturally create an approach for each recently transferred picture, likewise as per clients' social elements
Resolving Multi-party Privacy Conflicts in Social Media
Items shared through Social Media may affect more than one user's privacy ---
e.g., photos that depict multiple users, comments that mention multiple users,
events in which multiple users are invited, etc. The lack of multi-party
privacy management support in current mainstream Social Media infrastructures
makes users unable to appropriately control to whom these items are actually
shared or not. Computational mechanisms that are able to merge the privacy
preferences of multiple users into a single policy for an item can help solve
this problem. However, merging multiple users' privacy preferences is not an
easy task, because privacy preferences may conflict, so methods to resolve
conflicts are needed. Moreover, these methods need to consider how users' would
actually reach an agreement about a solution to the conflict in order to
propose solutions that can be acceptable by all of the users affected by the
item to be shared. Current approaches are either too demanding or only consider
fixed ways of aggregating privacy preferences. In this paper, we propose the
first computational mechanism to resolve conflicts for multi-party privacy
management in Social Media that is able to adapt to different situations by
modelling the concessions that users make to reach a solution to the conflicts.
We also present results of a user study in which our proposed mechanism
outperformed other existing approaches in terms of how many times each approach
matched users' behaviour.Comment: Authors' version of the paper accepted for publication at IEEE
Transactions on Knowledge and Data Engineering, IEEE Transactions on
Knowledge and Data Engineering, 201
Strengthen Privacy by Policy Generation & Secure Access on Content Sharing Sites
Creating privacy controls for social networks that are both expressive and usable is a major challenge. Lack of user understanding of privacy settings can lead to unwanted disclosure of private information and, in some cases, to material harm. 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 a Policy Hardening system to help users compose privacy settings for not only their images but securing each and every type of uploaded file. Dynamic groups are generated with particular policies of each group for secure access of files. We examine the role of social context, file content, and policies as possible indicators of usersďż˝ privacy preferences. We propose a policy framework where user can upload all kind of files and provide different policies with different users
Implementation of Privacy Policy Specification System for User Uploaded Images over Popular Content Sharing Sites
The regular use of social networking websites and application encompasses the collection and retention of personal and very often sensitive information about users. This information needs to remain private and each social network owns a privacy policy that describes in-depth how user’s information is managed and published. As there is increasing use of images for sharing through social sites, maintaining privacy has become a major problem. In light of these incidents, the need of tools to aid users control access to their shared content is necessary. This problem can be proposed by using an Privacy Policy Specification system to help users compose privacy settings for their shared images. Toward addressing this need, we propose Privacy Policy Specification system to help users to specify privacy settings for their images. Privacy Policy Specification System configure a policy for a group and apply appropriate policies (comment, share, expiry, download) on image for sharing in the group