Some recent work proposed differential private collaborative filtering (DPCF) recommender systems to protect user privacy from indirect access attacks, e.g., KNN attacks. As the cost of such protection, the MAE of recommendation was retained with insignificant increase but the more focused metrics, i.e., the precision and recall of top-k recommendations, degraded unacceptable. To address this problem, we propose a DPCF framework based on privacy-relevance of topics, named DPCFT. Firstly, DPCFT works on topic-preference level which highly aggregates user behaviors and keeps the precision and recall of top-k differential private recommendations acceptable. More importantly, considering the unnoticed fact that the information leakage of some special topics worries users much more than that of other ones in terms of privacy concerns, DPCFT introduces the topic privacy-relevance level in the similarity computation and neighbor selection to impose stronger privacy protection on higher privacy-relevance topics with overall differential privacy and recommendation performance reserved. Finally, to reduce the recommendation performance cost for differential privacy, DPCFT selects the top-k recommendation items at user side further with personal topic-preference without risk of data expose. Experimental results on the MovieLens dataset verify that the proposed framework DPCFT preserves differential privacy and top-k recommendation performance simultaneously.
['Tingting Feng', 'Yuchun Guo', 'Yishuai Chen']
A differential private collaborative filtering framework based on privacy-relevance of topics