计算机科学 ›› 2021, Vol. 48 ›› Issue (9): 21-35.doi: 10.11896/jsjkx.201100083

• 智能数据治理技术与系统* 上一篇    下一篇

面向推荐应用的差分隐私方案综述

董晓梅, 王蕊, 邹欣开   

  1. 东北大学计算机科学与工程学院 沈阳110169
  • 收稿日期:2020-11-10 修回日期:2021-03-13 出版日期:2021-09-15 发布日期:2021-09-10
  • 通讯作者: 董晓梅(xmdong@mail.neu.edu.cn)
  • 基金资助:
    国家自然科学基金联合基金重点项目(U1811261)

Survey on Privacy Protection Solutions for Recommended Applications

DONG Xiao-mei, WANG Rui, ZOU Xin-kai   

  1. School of Computer Science and Engineering,Northeastern University,Shenyang 110169,China
  • Received:2020-11-10 Revised:2021-03-13 Online:2021-09-15 Published:2021-09-10
  • About author:DONG Xiao-mei,born in 1970,Ph.D,associate professor,is a member of Information Security Committee of China Computer Society.Her main research interests include data security and privacy protection,network security,and cloud computing security.
  • Supported by:
    Joint Funds of the National Natural Science Foundationof China(U1811261)

摘要: 大数据时代背景下,各行各业希望能基于用户行为数据来训练推荐模型,为用户提供精准推荐,所用数据的共性特点为总量庞大、携带敏感信息、易于获取。推荐系统在带来精准推荐和市场盈利的同时也正在实时分享着用户的隐私数据,差分隐私保护技术作为一门隐私保护技术,能够巧妙地解决推荐应用中存在的隐私泄露问题,其优势在于不需要考虑攻击方所具备的任何相关的背景知识、严格地对隐私保护进行了定义、提供了量化评估方法来保证数据集(在不同参数条件下)所提供的隐私保护水平具有可比较性。首先简述了差分隐私的概念和主流推荐算法的近期研究成果,其次重点分析了差分隐私与推荐算法相结合的应用情况,涉及的推荐算法有矩阵分解、深度学习推荐、协同过滤等,并对基于差分隐私保护的推荐算法的准确性进行了对比实验;然后讨论了与每种推荐算法结合的使用场景以及目前仍存在的问题,最后对基于差分隐私的推荐算法的未来发展方向提出了有效建议。

关键词: 推荐算法, 差分隐私, 深度学习, 协同过滤, 矩阵分解

Abstract: In the context of the era of big data,various industries want to train recommendation models based on user behavior data to provide users with accurate recommendations.The common characteristics of the used data are huge amount,carrying sensitive information,and easy to obtain.The recommendation system is sharing users' private data in real time while bringing accurate recommendation and market profit.Differential privacy,as a privacy protection technology,can cleverly solve the problem of privacy leakage in recommendation applications.No matter the attacker has any relevant background knowledge,differential privacy strictly defines privacy protection,and provides quantitative evaluation methods to ensure that the level of privacy protection provided by the data set is comparable.First,the concept of differential privacy and the research on mainstream recommendation algorithms is briefly described.Second,the combined application of differential privacy and recommendation algorithms is analyzed,such as matrix factorization,deep learning recommendation,and collaborative filtering.A large number of comparative experiments have been conducted on recommendation algorithms based on differential privacy technology.Then the application scenarios of the combination of differential privacy and each recommendation algorithm and the remaining problems are discussed.Finally,effective suggestions are put forward for the future development direction of the recommendation algorithm based on differential privacy.

Key words: Recommendation algorithm, Differential privacy, Deep learning, Collaborative filtering, Matrix decomposition

中图分类号: 

  • TP309.2
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