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Efficiently Computing Private Recommendations

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Author: Erkin, Z. · Beye, M. · Veugen, P.J.M. · Lagendijk, R.L.
Type:article
Date:2011
Source:2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 22-27 May 2011, Prague, Czech Republic, 5864-5867
Identifier: 463790
doi: doi:10.1109/ICASSP.2011.5947695
Keywords: Informatics · Internet · Computational complexity · Cryptographic protocols · Data privacy · Information filtering · Recommender systems · Safety and Security · Defence, Safety and Security · Communication & Information · ISEC - Information Security · TS - Technical Sciences

Abstract

Online recommender systems enable personalized service to users. The underlying collaborative filtering techniques operate on privacy sensitive user data, which could be misused by the service provider. To protect user privacy, we propose to encrypt the data and generate recommendations by processing them under encryption. Thus, the service provider observes neither user preferences nor recommendations. The proposed method uses homomorphic encryption and se cure multi-party computation (MPC) techniques, which introduce a significant overhead in computational complexity. We minimize the introduced overhead by packing data and using cryptographic protocols particularly developed for this purpose. The proposed cryptographic protocol is implemented to test its correctness and performance.