Literature Overview - Privacy in Online Social Networks

In recent years, Online Social Networks (OSNs) have become an important\ud part of daily life for many. Users build explicit networks to represent their\ud social relationships, either existing or new. Users also often upload and share a plethora of information related to their personal lives. The potential privacy risks of such behavior are often underestimated or ignored. For example, users often disclose personal information to a larger audience than intended. Users may even post information about others without their consent. A lack of experience and awareness in users, as well as proper tools and design of the OSNs, perpetuate the situation. This paper aims to provide insight into such privacy issues and looks at OSNs, their associated privacy risks, and existing research into solutions. The final goal is to help identify the research directions for the Kindred Spirits project

A Privacy-Aware Framework for Friend Recommendations in Online Social Networks

Online social networks (OSN), such as Facebook, Twitter, and LinkedIn, have revolutionized the way how people share information and stay connected with family and friends. Along this direction, user’s privacy has been a significant concern to all users in the social networks. In this thesis, we propose a privacyaware framework that allows users to outsource their encrypted profile data to a cloud environment. In order to achieve better security and efficiency, our framework utilizes a hybrid approach that consists of Paillier’s encryption scheme and AES. Furthermore, we develop a privacy-aware friend recommendation protocol that recommends new friends to social network users without compromising their data. The proposed protocol adopts a collaborative analysis between the online social network provider and a cloud to increase the security in the suggested approach. Moreover, to increase the efficiency of the proposed protocol we utilize common-neighbors metric and universal hash functions. We compared our protocol with the existing work and demonstrate that our protocol is more efficient and achieves better security. We also conducted a set of experiments to evaluate the performance of our protocol and demonstrate its practicality

Fast and Secure Friend Recommendation in Online Social Networks

Online Social Networks have completely transformed communication in the world of social networks. Participation in online social networks have been growing significantly and is expected to continue to grow in the upcoming years. As user participation in online social media is on the rise, so is the concern pertaining to user privacy and information security; users want to interact on social media without jeopardizing their privacy and personal information. Extensive research has been conducted in the area of developing privacy-preserving protocols to allow users to interact in a secure and privacy-preserving environment. One of the elements that social media have is the feature or ability to befriend other users. While a user may manually search for friends to “add”, social media networks like Twitter, Facebook, Instagram, Snapchat and others facilitate friend recommendations to their users based on different criteria. We examine and compare the advantages and disadvantages of existing privacy-preserving techniques and schemes. We also analyze di↵erent models used to implement friend recommendation protocols and study proximity measurement metrics used in existing works. This thesis scrutinizes the security weaknesses and vulnerabilities of three Friend Recommendation Protocols from existing work and develop a corresponding solution. We propose a (FSFR) protocol that is based on Shamir’s Secret Sharing to facilitate friend recommendations in Online Social Networks in a fast, secure and private manner. After comparing our protocol with existing protocols in terms of security, computation efficiency, costs, flexibility and more, we conclude that our FSFR protocol guarantees a superior and more efficient friend recommendation protocol