Privacy Preserving Collaborative Filtering from Asymmetric Randomized Encoding

  • Yongjun Zhao
  • Sherman S. M. Chow
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8975)


Collaborative filtering is a famous technique in recommendation systems. Yet, it requires the users to reveal their preferences, which has undesirable privacy implications. Over the years, researchers have proposed many privacy-preserving collaborative filtering (PPCF) systems using very different techniques for different settings, ranging from adding noise to the data with centralized filtering, to performing secure multi-party computation. However, either privacy protection is unsatisfactory or the computation is prohibitively expensive.

In this work, we propose a decentralized PPCF system, which enables a group of users holding (cryptographically low-entropy) profile to identify other similar users in a privacy-preserving yet very efficient way, without the help of any central server. Its core component is a novel primitive which we named as asymmetric randomized encoding (ARE). Similar to the spirt of other cryptographic primitives, it is asymmetric in the sense that, honest party could enjoy performance boost (via precomputation) with the knowledge of a profile, whilst adversary aiming to recover the hidden profile can only launch dictionary attack against each encoded profile. Thanks to the simple design of ARE, our solution is very efficient, which is demonstrated by our performance evaluation. Besides PPCF, we believe that ARE will find further applications which require a balance between privacy and efficiency.


Asymmetric randomized encoding Privacy-preserving collaborative filtering Recommendation system Peer-to-peer network 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Department of Information EngineeringThe Chinese University of Hong KongHong KongHong Kong SAR

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