计算机工程 ›› 2019, Vol. 45 ›› Issue (1): 96-102.doi: 10.19678/j.issn.1000-3428.0049731

• 安全技术 • 上一篇    下一篇

基于LSH的隐私保护POI推荐算法

沈鑫娣1,翟东君1,张得天2,刘安1   

  1. 1.苏州大学 计算机科学与技术学院,江苏 苏州 215006; 2.江南大学 数字媒体学院,江苏 无锡 2141221.
  • 收稿日期:2017-12-18 出版日期:2019-01-15 发布日期:2019-01-15
  • 作者简介:沈鑫娣(1992—),女,硕士研究生,主研方向为时空数据、数据隐私;翟东君,硕士研究生;张得天,讲师;刘安,副教授
  • 基金项目:

    国家自然科学基金面上项目(61572336);国家自然科学基金青年基金(61702227)

Privacy Preserving POI Recommendation Algorithm Based on LSH

SHEN Xindi 1,ZHAI Dongjun 1,ZHANG Detian 2,LIU An 1   

  1. 1.School of Computer Science and Technology,Soochow University,Suzhou,Jiangsu 215006,China; 2.School of Digital Media,Jiangnan University,Wuxi,Jiangsu 214122,China
  • Received:2017-12-18 Online:2019-01-15 Published:2019-01-15

摘要:

基于位置的社交网络利用用户的签到数据进行兴趣点(POI)推荐,但是出于对数据隐私的考虑,各种社交平台之间不愿意直接共享数据。为综合各个社交平台的数据从而提供更好的POI推荐服务,提出一种基于局部敏感哈希(LSH)的隐私保护POI推荐算法。通过LSH选取相似用户集合,极大地减少计算量,满足用户的快速响应需求。利用LSH和Paillier同态加密技术,在计算过程中保护数据隐私不被泄露。真实数据集上的实验结果表明,在响应时间和预测准确度上,该算法优于传统基于用户的协同过滤推荐算法。

关键词: 局部敏感哈希, 隐私保护, 推荐算法, 兴趣点, 同态加密

Abstract:

The Location-Based Social Network(LBSN) uses the user’s check-in data to recommend the Point of Interest (POI),but for the consideration of data privacy,various social platforms are unwilling to share data directly.In order to provide a better POI recommendation service by synthesizing the data of various social platforms,a privacy preserving POI recommendation algorithm based on Locality-Sensitive Hashing(LSH) is proposed.The similar user set is selected by LSH,which greatly reduces the computation cost and satisfies the user’s rapid response demand.LSH and Paillier homomorphic encryption techniques are used to protect data privacy from disclosure.Experimental results on real data sets show that the proposed algorithm is superior to the traditional collaborative filtering recommendation algorithm based on users in response time and prediction accuracy.

Key words: Locality-Sensitive Hashing(LSH), privacy preserving, recommendation algorithm, Point of Interest(POI), homomorphic encryption

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