Towards Differential Privacy in Sequential Recommendation: A Noisy Graph Neural Network Approach

IF 4 3区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS ACM Transactions on Knowledge Discovery from Data Pub Date : 2024-01-30 DOI:10.1145/3643821
Wentao Hu, Hui Fang
{"title":"Towards Differential Privacy in Sequential Recommendation: A Noisy Graph Neural Network Approach","authors":"Wentao Hu, Hui Fang","doi":"10.1145/3643821","DOIUrl":null,"url":null,"abstract":"<p>With increasing frequency of high-profile privacy breaches in various online platforms, users are becoming more concerned about their privacy. And recommender system is the core component of online platforms for providing personalized service, consequently, its privacy preservation has attracted great attention. As the gold standard of privacy protection, differential privacy has been widely adopted to preserve privacy in recommender systems. However, existing differentially private recommender systems only consider static and independent interactions, so they cannot apply to sequential recommendation where behaviors are dynamic and dependent. Meanwhile, little attention has been paid on the privacy risk of sensitive user features, most of them only protect user feedbacks. In this work, we propose a novel DIfferentially Private Sequential recommendation framework with a noisy Graph Neural Network approach (denoted as DIPSGNN) to address these limitations. To the best of our knowledge, we are the first to achieve differential privacy in sequential recommendation with dependent interactions. Specifically, in DIPSGNN, we first leverage piecewise mechanism to protect sensitive user features. Then, we innovatively add calibrated noise into aggregation step of graph neural network based on aggregation perturbation mechanism. And this noisy graph neural network can protect sequentially dependent interactions and capture user preferences simultaneously. Extensive experiments demonstrate the superiority of our method over state-of-the-art differentially private recommender systems in terms of better balance between privacy and accuracy.</p>","PeriodicalId":49249,"journal":{"name":"ACM Transactions on Knowledge Discovery from Data","volume":"5 1","pages":""},"PeriodicalIF":4.0000,"publicationDate":"2024-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Transactions on Knowledge Discovery from Data","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1145/3643821","RegionNum":3,"RegionCategory":"计算机科学","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

With increasing frequency of high-profile privacy breaches in various online platforms, users are becoming more concerned about their privacy. And recommender system is the core component of online platforms for providing personalized service, consequently, its privacy preservation has attracted great attention. As the gold standard of privacy protection, differential privacy has been widely adopted to preserve privacy in recommender systems. However, existing differentially private recommender systems only consider static and independent interactions, so they cannot apply to sequential recommendation where behaviors are dynamic and dependent. Meanwhile, little attention has been paid on the privacy risk of sensitive user features, most of them only protect user feedbacks. In this work, we propose a novel DIfferentially Private Sequential recommendation framework with a noisy Graph Neural Network approach (denoted as DIPSGNN) to address these limitations. To the best of our knowledge, we are the first to achieve differential privacy in sequential recommendation with dependent interactions. Specifically, in DIPSGNN, we first leverage piecewise mechanism to protect sensitive user features. Then, we innovatively add calibrated noise into aggregation step of graph neural network based on aggregation perturbation mechanism. And this noisy graph neural network can protect sequentially dependent interactions and capture user preferences simultaneously. Extensive experiments demonstrate the superiority of our method over state-of-the-art differentially private recommender systems in terms of better balance between privacy and accuracy.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
序列推荐中的差异隐私:噪声图神经网络方法
随着各种网络平台上高曝光率的隐私泄露事件日益频繁,用户越来越关注自己的隐私。而推荐系统是网络平台提供个性化服务的核心组成部分,因此,其隐私保护问题备受关注。作为隐私保护的黄金标准,差分隐私被广泛用于保护推荐系统中的隐私。然而,现有的差异化隐私推荐系统只考虑了静态和独立的交互,因此无法适用于行为是动态和依赖的连续推荐。同时,人们很少关注敏感用户特征的隐私风险,大多数系统只保护用户反馈。在这项工作中,我们提出了一种新颖的 "无噪声图神经网络"(DIPSGNN)隐私序列推荐框架,以解决这些局限性。据我们所知,我们是第一个在具有依赖性交互的顺序推荐中实现差异化隐私的人。具体来说,在 DIPSGNN 中,我们首先利用分片机制来保护敏感的用户特征。然后,我们基于聚合扰动机制,在图神经网络的聚合步骤中创新性地添加了校准噪声。这种有噪声的图神经网络可以保护顺序依赖的交互,并同时捕捉用户偏好。广泛的实验证明,我们的方法优于最先进的差异化隐私推荐系统,在隐私和准确性之间取得了更好的平衡。
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 去求助
来源期刊
ACM Transactions on Knowledge Discovery from Data
ACM Transactions on Knowledge Discovery from Data COMPUTER SCIENCE, INFORMATION SYSTEMS-COMPUTER SCIENCE, SOFTWARE ENGINEERING
CiteScore
6.70
自引率
5.60%
发文量
172
审稿时长
3 months
期刊介绍: TKDD welcomes papers on a full range of research in the knowledge discovery and analysis of diverse forms of data. Such subjects include, but are not limited to: scalable and effective algorithms for data mining and big data analysis, mining brain networks, mining data streams, mining multi-media data, mining high-dimensional data, mining text, Web, and semi-structured data, mining spatial and temporal data, data mining for community generation, social network analysis, and graph structured data, security and privacy issues in data mining, visual, interactive and online data mining, pre-processing and post-processing for data mining, robust and scalable statistical methods, data mining languages, foundations of data mining, KDD framework and process, and novel applications and infrastructures exploiting data mining technology including massively parallel processing and cloud computing platforms. TKDD encourages papers that explore the above subjects in the context of large distributed networks of computers, parallel or multiprocessing computers, or new data devices. TKDD also encourages papers that describe emerging data mining applications that cannot be satisfied by the current data mining technology.
期刊最新文献
Structural properties on scale-free tree network with an ultra-large diameter Learning Individual Treatment Effects under Heterogeneous Interference in Networks Deconfounding User Preference in Recommendation Systems through Implicit and Explicit Feedback Interdisciplinary Fairness in Imbalanced Research Proposal Topic Inference: A Hierarchical Transformer-based Method with Selective Interpolation A Compact Vulnerability Knowledge Graph for Risk Assessment
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1