Title: Online Social Networks Privacy and Protection Using Asymmetric Matching Protocols
Authors: Pavuluri Subbarao, Ch.Dayakar Reddy & Gouda Rajesh
Organisation: CMR College of Engineering & Technology.
Abstract: Online social networks (OSNs) have experienced tremendous growth in recent years and become a de facto portal for hundreds of millions of Internet users. These OSNs offer attractive means for digital social interactions and information sharing, but also raise a number of security and privacy issues. While OSNs allow users to restrict access to shared data, they currently do not provide any mechanism to enforce privacy concerns over data associated with multiple users. Online Social Networks (OSNs), which attract thousands of million people to use everyday, also greatly extend OSN users’ social circles by friend recommendations. OSN users’ existing social relationship can be characterized as 1-hop trust relationship, and further establish a multi-hop trust chain during the recommendation process. As the same as what people usually experience in the daily life, the social relationship in cyberspaces are potentially formed by OSN users’ shared attributes, e.g., colleagues, family members, or classmates, which indicates the attribute-based recommendation process would lead to more fine grained social relationships between strangers.Unfortunately, privacy concerns raised in the recommendation process impede the expansion of OSN users’ friend circle. Some OSN users refuse to disclose their identities and their friends’ information to the public domain.This project is motivated by the recognition of the need for a finer grain and more personalized privacy in data publication of social networks. It proposes a privacy protection scheme that not only prevents the disclosure of identity of users but also the disclosure of selected features in users’ profiles. An individual user can select features of his/her profiles that should not be disclosed to others.Social networking is modelled as graphs in which users are nodes and features are labels.
Labels are denoted either as sensitive or as non-sensitive. It treats node labels both as background knowledge an adversary may possess, and as sensitive information that has to be protected. It also presents privacy protection algorithms that allow for graph data to be published in a form such that an adversary who possesses information about a node’s neighbourhood cannot safely infer its identity and its sensitive labels. It shows that our solution is effective, efficient and scalable while offering stronger privacy guarantees than those in previous research.
General Terms: Networking, Security, Node detection Algorithms.
Keywords: Online social networks, data privacy, social networking, privacy protection algorithms.
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