Published January 1, 2014 | Version v1
Journal article Open

Robustness analysis of privacy-preserving model-based recommendation schemes

  • 1. Anadolu Univ, Dept Comp Engn, TR-26470 Eskisehir, Turkey

Description

Privacy-preserving model-based recommendation methods are preferable over privacy-preserving memory-based schemes due to their online efficiency. Model-based prediction algorithms without privacy concerns have been investigated with respect to shilling attacks. Similarly, various privacy-preserving model-based recommendation techniques have been proposed to handle privacy issues. However, privacy-preserving model-based collaborative filtering schemes might be subjected to shilling or profile injection attacks. Therefore, their robustness against such attacks should be scrutinized.

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