Efficiently computing private recommendations

Z. Erkin, M. Beye, T. Veugen, R. L. Lagendijk

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

29 Citations (Scopus)
36 Downloads (Pure)


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 secure 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.
Original languageEnglish
Title of host publication2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Number of pages4
ISBN (Print)978-1-4577-0537-3
Publication statusPublished - 2011
Externally publishedYes
EventIEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2011 - Prague, Czech Republic
Duration: 22 May 201127 May 2011


ConferenceIEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2011
Abbreviated titleICASSP
Country/TerritoryCzech Republic


  • Servers
  • Encryption
  • Privacy
  • Cryptographic protocols
  • Recommender systems


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