Authors
Aidmar Wainakh, Tim Grube, Jörg Daubert, Max Mühlhäuser
Publication date
2019/9/10
Book
Proceedings of the 13th ACM Conference on Recommender Systems
Pages
78-86
Description
Many recommender systems use association rules mining, a technique that captures relations between user interests and recommends new probable ones accordingly. Applying association rule mining causes privacy concerns as user interests may contain sensitive personal information (e.g., political views). This potentially even inhibits the user from providing information in the first place. Current distributed privacy-preserving association rules mining (PPARM) approaches use cryptographic primitives that come with high computational and communication costs, rendering PPARM unsuitable for large-scale applications such as social networks. We propose improvements in the efficiency and privacy of PPARM approaches by minimizing the required data. We propose and compare sampling strategies to sample the data based on social graphs in a privacy-preserving manner. The results on real-world datasets …
Total citations
2020202120222023202436334
Scholar articles
A Wainakh, T Grube, J Daubert, M Mühlhäuser - Proceedings of the 13th ACM Conference on …, 2019