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Private collaborative filtering under untrusted recommender server

机译：不受信任的推荐服务器下的私有协同过滤

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摘要

Recommender systems play an increasingly vital role in modern E-commerce. However, exploiting users' preferences with recommender algorithms leads to serious privacy risks, especially when recommender service providers are unreliable. To deal with the problem, this paper proposes a Client/Server framework to create a private recommender system (PrivateRS). The system assumes that the Server side is untrustworthy. On the Client side, each user firstly rates the items and randomizes the ratings with a differential privacy mechanism. The ratings are further substituted by private symbols which are autonomously defined by each user to hide the ordinal meaning of the ratings. Using those symbols, the Server applies a private collaborative filtering algorithm to predict the ratings of items for the user. During this process, new similarity metrics are provided to search the nearest neighbours for users or items without knowing the real meanings of those symbols. Experimental results demonstrate that even though the ordinal meaning of the rating is significantly obfuscated, the proposed algorithms can still generate accurate recommendations with acceptable loss.
机译：推荐系统在现代电子商务中起着越来越重要的作用。但是，利用推荐算法利用用户的偏好会导致严重的隐私风险，尤其是当推荐人服务提供商不可靠时。要处理问题，本文提出了一个客户/服务器框架来创建私人推荐系统（Privaters）。系统假设服务器端是不值得信任的。在客户端，每个用户首​​先利用项目并随机使用差分隐私机制随机化。评级还被每个用户自主定义的私有符号代替，以隐藏额定值的序号。使用这些符号，服务器应用私有协同过滤算法来预测用户的项目的额定值。在此过程中，提供新的相似性度量来搜索最近的邻居，用于用户或项目而不知道这些符号的实际含义。实验结果表明，即使评级的序数含义明显困扰，所提出的算法仍然可以通过可接受的损失产生准确的建议。

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  • 来源
    《Future generation computer systems》 |2020年第8期|511-520|共10页
  • 作者

    Ping Xiong; Lefeng Zhang; Tianqing Zhu; Gang Li; Wanlei Zhou;

  • 作者单位

    School of Information and Security Engineering Zhongnan University of Economics and Law China;

    School of Information and Security Engineering Zhongnan University of Economics and Law China;

    School of Mathematics and Computer Science Wuhan Polytechnic University China School of Information Technology Deakin University 221 Burwood Highway Vic 3125 Australia;

    School of Information Technology Deakin University 221 Burwood Highway Vic 3125 Australia;

    School of Information Technology Deakin University 221 Burwood Highway Vic 3125 Australia;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Recommender system; Collaborative filtering; Substitution; Differential privacy;

    机译：推荐系统;协同过滤;代换;差异隐私;

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