TY - GEN
T1 - A Secure Friend Recommendation Framework for Online Social Networks using OpenAI Embeddings
AU - Singh, Mohit
AU - Samanthula, Bharath K.
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Cryptographic Hash
KW - Friend Recommendation
KW - Online Social Network
KW - OpenAI
KW - Privacy
KW - Text Embeddings
UR - http://www.scopus.com/inward/record.url?scp=85195455274&partnerID=8YFLogxK
U2 - 10.1109/URTC60662.2023.10534967
DO - 10.1109/URTC60662.2023.10534967
M3 - Conference contribution
AN - SCOPUS:85195455274
T3 - IEEE MIT Undergraduate Research Technology Conference, URTC 2023 - Proceedings
BT - IEEE MIT Undergraduate Research Technology Conference, URTC 2023 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE MIT Undergraduate Research Technology Conference, URTC 2023
Y2 - 6 October 2023 through 8 October 2023
ER -