A Security-assured Accuracy-maximised Privacy Preserving Collaborative Filtering Recommendation Algorithm
Zhigang Lu, Hong Shen

TL;DR
This paper introduces a novel privacy-preserving collaborative filtering algorithm that guarantees security against $k$NN attacks while maintaining optimal prediction accuracy through partitioned neighbor selection with differential privacy.
Contribution
The paper proposes Partitioned Probabilistic Neighbour Selection, a new method that balances security and accuracy in privacy-preserving recommender systems.
Findings
Ensures security against $k$NN attack with optimal accuracy.
Uses exponential differential privacy for neighbor selection.
Theoretical and experimental results validate effectiveness.
Abstract
The neighbourhood-based Collaborative Filtering is a widely used method in recommender systems. However, the risks of revealing customers' privacy during the process of filtering have attracted noticeable public concern recently. Specifically, NN attack discloses the target user's sensitive information by creating fake nearest neighbours by non-sensitive information. Among the current solutions against NN attack, the probabilistic methods showed a powerful privacy preserving effect. However, the existing probabilistic methods neither guarantee enough prediction accuracy due to the global randomness, nor provide assured security enforcement against NN attack. To overcome the problems of current probabilistic methods, we propose a novel approach, Partitioned Probabilistic Neighbour Selection, to ensure a required security guarantee while achieving the optimal prediction…
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Taxonomy
TopicsPrivacy-Preserving Technologies in Data · Recommender Systems and Techniques · Privacy, Security, and Data Protection
