A security-assured accuracy-maximised privacy preserving collaborative filtering recommendation algorithm
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(Restricted Access)
Date
2015
Authors
Lu, Z.
Shen, H.
Editors
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Conference paper
Citation
Proceedings of the 19th International Database Engineering & Applications Symposium, 2015, vol.0, iss.CONFCODENUMBER, pp.72-80
Statement of Responsibility
Zhigang Lu, Hong Shen
Conference Name
19th International Database Engineering & Applications Symposium (IDEAS) (13 Jul 2015 - 15 Jul 2015 : Yokohama, Japan)
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. Specif- ically, kNN attack discloses the target user's sensitive in- formation 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 β, the number of user partitions, across which we select the k neighbours, as the security metric β Privacy Preserving, Differential Privacy, Neighbourhood-based
Collaborative Filtering, Internet Commerce. 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.