Date of Award
Master of Science (MS)
College of Science and Mathematics
Thesis Sponsor/Dissertation Chair/Project Chair
Bharath Kumar Samanthula
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.
Alkanhal, Mona Fahad, "A Privacy-Aware Framework for Friend Recommendations in Online Social Networks" (2019). Theses, Dissertations and Culminating Projects. 281.