Date of Award

5-2021

Document Type

Thesis

Degree Name

Master of Science (MS)

College/School

College of Science and Mathematics

Department/Program

Computer Science

Thesis Sponsor/Dissertation Chair/Project Chair

Bharath Kumar Samanthula

Committee Member

Kazi Zakia Sultana

Committee Member

Jiacheng Shang

Abstract

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.

File Format

PDF

Available for download on Friday, December 31, 2021

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