A security-assured accuracy-maximised privacy preserving collaborative filtering recommendation algorithm

Zhigang Lu, Hong Shen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

13 Citations (Scopus)


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 nonsensitive 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.

Original languageEnglish
Title of host publicationACM International Conference Proceeding Series
EditorsBipin C. Desai, Motomichi Toyama
PublisherAssociation for Computing Machinery
Number of pages9
ISBN (Electronic)9781450334143
Publication statusPublished - 13 Jul 2015
Externally publishedYes
Event19th International Database Engineering and Applications Symposium, IDEAS 2015 - Yokohama, Japan
Duration: 13 Jul 201515 Jul 2015

Publication series

NameACM International Conference Proceeding Series


Conference19th International Database Engineering and Applications Symposium, IDEAS 2015


  • Differential privacy
  • Internet commerce
  • Neighbourhood-based collaborative filtering
  • Privacy preserving


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