A Secure Friend Recommendation Framework for Online Social Networks using OpenAI Embeddings

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The popularity of Online Social Networks (OSNs), such as Facebook and Twitter, have led organizations to use OSN features to improve their business operations. Although OSNs amass billions of followers, privacy remains a pertinent concern for many users. A common functionality of OSNs is to facilitate friend recommendations (FR) without compromising user privacy. While users can add friends manually, most OSNs utilize different FR approaches that collect users profile data and employ linkage metrics to recommend new friends. In this paper, we propose an efficient and secure framework for creating friend recommendations. At the core of our framework, we use the OpenAI text embeddings to study the benefits of using new AI platforms and demonstrate its applicability to effectively address the FR problem in a privacy-preserving manner. Furthermore, our experimental results show that the proposed framework is superior in terms of accuracy, and efficiency, and also incurs minimal cost.

Original languageEnglish
Title of host publicationIEEE MIT Undergraduate Research Technology Conference, URTC 2023 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350308600
DOIs
StatePublished - 2023
Event2023 IEEE MIT Undergraduate Research Technology Conference, URTC 2023 - Hybrid, Cambridge, United States
Duration: 6 Oct 20238 Oct 2023

Publication series

NameIEEE MIT Undergraduate Research Technology Conference, URTC 2023 - Proceedings

Conference

Conference2023 IEEE MIT Undergraduate Research Technology Conference, URTC 2023
Country/TerritoryUnited States
CityHybrid, Cambridge
Period6/10/238/10/23

Keywords

  • Cryptographic Hash
  • Friend Recommendation
  • Online Social Network
  • OpenAI
  • Privacy
  • Text Embeddings

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