SBlur: An Obfuscation Approach for Preserving Sensitive Attributes in Recommender System

IF 6.9 1区 管理学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Falguni Roy , Na Zhao , Xiaofeng Ding
{"title":"SBlur: An Obfuscation Approach for Preserving Sensitive Attributes in Recommender System","authors":"Falguni Roy ,&nbsp;Na Zhao ,&nbsp;Xiaofeng Ding","doi":"10.1016/j.ipm.2025.104282","DOIUrl":null,"url":null,"abstract":"<div><div>User interaction in the recommender system is treated as a way of expressing user preferences, which later serve as input to provide more accurate recommendations. However, such interaction data can be exploited to infer user private attributes, including gender, age, and personality traits, posing significant privacy implications. Existing obfuscation-based approaches endeavor to mitigate these vulnerabilities by adding or removing interactions from user profiles before or during recommender algorithm training. Nevertheless, these methods often compromise recommendation accuracy while facing challenges such as the cold-start user problem and the “rich get richer” effect, undermining recommendation diversity. To address these constraints, we propose SBlur, a strategic obfuscation approach designed to preserve users’ attribute privacy while balancing the privacy-accuracy-fairness trade-off and enhancing diversity. SBlur conceals gender inference attacks by strategically adding and removing items, supported by a combined similarity measure that integrates rating-based and genre preference-based similarities. This combined similarity enables precise user profile personalization for obfuscation, particularly in cold-start scenarios. We evaluate SBlur using three popular datasets (ML100k, ML1M, and Yahoo!Movie) and three state-of-the-art recommendation algorithms (UserKNN, ALS, and BPRMF). Experimental results demonstrate that SBlur achieves a balanced trade-off between privacy, recommendation accuracy, and fairness while promoting recommendation diversity.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 6","pages":"Article 104282"},"PeriodicalIF":6.9000,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information Processing & Management","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0306457325002237","RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0

Abstract

User interaction in the recommender system is treated as a way of expressing user preferences, which later serve as input to provide more accurate recommendations. However, such interaction data can be exploited to infer user private attributes, including gender, age, and personality traits, posing significant privacy implications. Existing obfuscation-based approaches endeavor to mitigate these vulnerabilities by adding or removing interactions from user profiles before or during recommender algorithm training. Nevertheless, these methods often compromise recommendation accuracy while facing challenges such as the cold-start user problem and the “rich get richer” effect, undermining recommendation diversity. To address these constraints, we propose SBlur, a strategic obfuscation approach designed to preserve users’ attribute privacy while balancing the privacy-accuracy-fairness trade-off and enhancing diversity. SBlur conceals gender inference attacks by strategically adding and removing items, supported by a combined similarity measure that integrates rating-based and genre preference-based similarities. This combined similarity enables precise user profile personalization for obfuscation, particularly in cold-start scenarios. We evaluate SBlur using three popular datasets (ML100k, ML1M, and Yahoo!Movie) and three state-of-the-art recommendation algorithms (UserKNN, ALS, and BPRMF). Experimental results demonstrate that SBlur achieves a balanced trade-off between privacy, recommendation accuracy, and fairness while promoting recommendation diversity.
模糊:一种保持推荐系统敏感属性的模糊方法
推荐系统中的用户交互被视为一种表达用户偏好的方式,用户偏好随后作为输入提供更准确的推荐。然而,这样的交互数据可以被用来推断用户的私有属性,包括性别、年龄和人格特征,从而产生重大的隐私影响。现有的基于混淆的方法通过在推荐算法训练之前或期间从用户配置文件中添加或删除交互来努力减轻这些漏洞。然而,这些方法往往会影响推荐的准确性,并面临冷启动用户问题和“富得更富”效应等挑战,破坏了推荐的多样性。为了解决这些限制,我们提出了SBlur,一种战略性混淆方法,旨在保护用户的属性隐私,同时平衡隐私-准确性-公平性的权衡和增强多样性。SBlur通过战略性地添加和删除物品来隐藏性别推断攻击,并结合了基于评级和基于类型偏好的相似性的组合相似性度量。这种组合的相似性可以实现精确的用户配置文件个性化,以消除混淆,特别是在冷启动场景中。我们使用三个流行的数据集(ML100k、ML1M和Yahoo!Movie)和三个最先进的推荐算法(UserKNN、ALS和BPRMF)来评估SBlur。实验结果表明,SBlur在促进推荐多样性的同时,实现了隐私、推荐准确性和公平性之间的平衡。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Information Processing & Management
Information Processing & Management 工程技术-计算机:信息系统
CiteScore
17.00
自引率
11.60%
发文量
276
审稿时长
39 days
期刊介绍: Information Processing and Management is dedicated to publishing cutting-edge original research at the convergence of computing and information science. Our scope encompasses theory, methods, and applications across various domains, including advertising, business, health, information science, information technology marketing, and social computing. We aim to cater to the interests of both primary researchers and practitioners by offering an effective platform for the timely dissemination of advanced and topical issues in this interdisciplinary field. The journal places particular emphasis on original research articles, research survey articles, research method articles, and articles addressing critical applications of research. Join us in advancing knowledge and innovation at the intersection of computing and information science.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信