research-article

ID-SR: Privacy-Preserving Social Recommendation Based on Infinite Divisibility for Trustworthy AI

Authors:

Shaoying LiuAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data, Volume 18, Issue 7

Article No.: 161, Pages 1 - 25

https://doi.org/10.1145/3639412

Published: 19 June 2024 Publication History

Abstract

Recommendation systems powered by artificial intelligence (AI) are widely used to improve user experience. However, AI inevitably raises privacy leakage and other security issues due to the utilization of extensive user data. Addressing these challenges can protect users’ personal information, benefit service providers, and foster service ecosystems. Presently, numerous techniques based on differential privacy have been proposed to solve this problem. However, existing solutions encounter issues such as inadequate data utilization and a tenuous trade-off between privacy protection and recommendation effectiveness. To enhance recommendation accuracy and protect users’ private data, we propose ID-SR, a novel privacy-preserving social recommendation scheme for trustworthy AI based on the infinite divisibility of Laplace distribution. We first introduce a novel recommendation method adopted in ID-SR, which is established based on matrix factorization with a newly designed social regularization term for improving recommendation effectiveness. We then propose a differential privacy-preserving scheme tailored to the above method that leverages the Laplace distribution’s characteristics to safeguard user data. Theoretical analysis and experimentation evaluation on two publicly available datasets demonstrate that our scheme achieves a superior balance between privacy protection and recommendation effectiveness, ultimately delivering an enhanced user experience.

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Cited By

Ali WZhou XShao J(2025)Privacy-preserved and Responsible Recommenders: From Conventional Defense to Federated Learning and BlockchainACM Computing Surveys10.1145/370898257:5(1-35)Online publication date: 9-Jan-2025
https://dl.acm.org/doi/10.1145/3708982
Fan WZhao STang J(undefined)Introduction of the Special Issue on Trustworthy Artificial IntelligenceACM Transactions on Knowledge Discovery from Data10.1145/3712184
https://dl.acm.org/doi/10.1145/3712184

Index Terms

ID-SR: Privacy-Preserving Social Recommendation Based on Infinite Divisibility for Trustworthy AI

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Published In

cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 18, Issue 7

August 2024

505 pages

EISSN:1556-472X

DOI:10.1145/3613689

Editor:
Jian Pei
Duke University, USA

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 June 2024

Online AM: 02 January 2024

Accepted: 15 December 2023

Revised: 15 September 2023

Received: 15 September 2023

Published in TKDD Volume 18, Issue 7

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National Science Foundation of China
Tianjin Intelligent Manufacturing Special Fund Project
China Guangxi Science and Technology Plan Project—Guangxi Science and Technology Base and Talent Special Project
Hainan Provincial Natural Science Foundation of China
CCF-Nsfocus Kunpeng Fund Project

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Cited By

Ali WZhou XShao J(2025)Privacy-preserved and Responsible Recommenders: From Conventional Defense to Federated Learning and BlockchainACM Computing Surveys10.1145/370898257:5(1-35)Online publication date: 9-Jan-2025
https://dl.acm.org/doi/10.1145/3708982
Fan WZhao STang J(undefined)Introduction of the Special Issue on Trustworthy Artificial IntelligenceACM Transactions on Knowledge Discovery from Data10.1145/3712184
https://dl.acm.org/doi/10.1145/3712184

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