Differentially private user-based collaborative filtering recommendation based on k-means clustering

作者：

Highlights：

• Existing DP schemes degrade the recommendation performance too much.

• K-means clustering helps to select relevant data and thus improve the performance.

• Exponential mechanism is applied only once to reduce the noise added.

• Experimental results demonstrate a significant performance improvement.

摘要

•Existing DP schemes degrade the recommendation performance too much.•K-means clustering helps to select relevant data and thus improve the performance.•Exponential mechanism is applied only once to reduce the noise added.•Experimental results demonstrate a significant performance improvement.

论文关键词：Differential privacy,k-means clustering,Recommendation system,Collaborative filtering

论文评审过程：Received 23 April 2019, Revised 22 November 2020, Accepted 22 November 2020, Available online 24 November 2020, Version of Record 5 December 2020.

论文官网地址：https://doi.org/10.1016/j.eswa.2020.114366