A Security-assured Accuracy-maximised Privacy Preserving Collaborative Filtering Recommendation Algorithm

Lu, Zhigang, and Shen, Hong (2015) A Security-assured Accuracy-maximised Privacy Preserving Collaborative Filtering Recommendation Algorithm. In: Proceedings of the 19th International Database Engineering & Applications Symposium. pp. 72-80. From: IDEAS '15: 19th International Database Engineering & Applications Symposium, 13-15 July 2015, Yokohama, Japan.

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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, kNN attack discloses the target user's sensitive information by creating k fake nearest neighbours by non-sensitive information. Among the current solutions against kNN 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 kNN attack. To overcome the problems of current probabilistic methods, we propose a novel approach, Probabilistic Partitioned Neighbour Selection, to ensure a required security guarantee while achieving the optimal prediction accuracy against kNN attack. In this paper, we define the sum of k neighbours' similarity as the accuracy metric α, the number of user partitions, across which we select the k neighbours, as the security metric β. Differing from the present methods that globally selected neighbours, our method selects neighbours from each group with exponential differential privacy to decrease the magnitude of noise. Theoretical and experimental analysis show that to achieve the same security guarantee against kNN attack, our approach ensures the optimal prediction accuracy.

Item ID: 77415
Item Type: Conference Item (Research - E1)
ISBN: 9781450334143
Copyright Information: © 2015 ACM.
Date Deposited: 07 Feb 2023 23:12
FoR Codes: 46 INFORMATION AND COMPUTING SCIENCES > 4604 Cybersecurity and privacy > 460402 Data and information privacy @ 100%
SEO Codes: 22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220405 Cybersecurity @ 100%
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