An Improved (k,p,l)-Anonymity Method for Privacy Preserving Collaborative Filtering

Ruoxuan Wei, Hong Shen, Hui Tian

研究成果: Conference contribution同行評審

3 引文 斯高帕斯(Scopus)

摘要

Collaborative Filtering (CF) is a successful technique that has been implemented in recommender systems and Privacy Preserving Collaborative Filtering (PPCF) aroused increasing concerns of the society. Current solutions mainly focus on cryptographic methods, obfuscation methods, perturbation methods and differential privacy methods. But these methods have some shortcomings, such as unnecessary computational cost, lower data quality and hard to calibrate the magnitude of noise. This paper proposes a (k,p,l) anonymity method that improves the existing k-anonymity method in PPCF. The method works as follows: First, it applies Latent Factor Model (LFM) to reduce matrix sparsity. Then it improves Maximum Distance to Average Vector (MDAV) microaggregation algorithm based on importance partitioning to increase homogeneity among records in each group which can retain better data quality and (p,l)-diversity model where p is attacker's prior knowledge about users' ratings and l is the diversity among users in each group to improve the level of privacy preserving. Theoretical and experimental analyses show that our approach ensures a higher level of privacy preserving based on lower information loss.

原文English
主出版物標題2017 IEEE Global Communications Conference, GLOBECOM 2017 - Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
頁面1-6
頁數6
ISBN(電子)9781509050192
DOIs
出版狀態Published - 1 7月 2017
對外發佈
事件2017 IEEE Global Communications Conference, GLOBECOM 2017 - Singapore, Singapore
持續時間: 4 12月 20178 12月 2017

出版系列

名字2017 IEEE Global Communications Conference, GLOBECOM 2017 - Proceedings
2018-January

Conference

Conference2017 IEEE Global Communications Conference, GLOBECOM 2017
國家/地區Singapore
城市Singapore
期間4/12/178/12/17

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