| Reference : Facilitating Privacy-preserving Recommendation-as-a-Service with Machine Learning |
| Scientific congresses, symposiums and conference proceedings : Poster | |||
| Engineering, computing & technology : Computer science | |||
| Security, Reliability and Trust | |||
| http://hdl.handle.net/10993/37324 | |||
| Facilitating Privacy-preserving Recommendation-as-a-Service with Machine Learning | |
| English | |
Wang, Jun  [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >] | |
| Delerue Arriaga, Afonso [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > APSIA >] | |
| Tang, Qiang [] | |
| Ryan, Peter [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >] | |
| Oct-2018 | |
| Yes | |
| International | |
| the 2018 ACM SIGSAC Conference | |
| from 15-10-2018 to 19-10-2018 | |
| [en] Privacy-preserving ; recommender system ; homomorphic encryption | |
| [en] Machine-Learning-as-a-Service has become increasingly popular, with Recommendation-as-a-Service as one of the representative examples. In such services, providing privacy protection for users is an important topic. Reviewing privacy-preserving solutions which were proposed in the past decade, privacy and machine learning are often seen as two competing goals at stake. Though improving cryptographic primitives (e.g., secure multi-party computation (SMC) or homomorphic encryption (HE)) or devising sophisticated secure protocols has made a remarkable achievement, but in conjunction with state-of-the-art recommender systems often yields far-from-practical solutions. We tackle this problem from the direction of machine learning. We aim to design crypto-friendly recommendation algorithms, thus to obtain efficient solutions by directly using existing cryptographic tools. In particular, we propose an HE-friendly recommender system, refer to as CryptoRec, which (1) decouples user features from latent feature space, avoiding training the recommendation model on encrypted data; (2) only relies on addition and multiplication operations, making the model straightforwardly compatible with HE schemes. The properties turn recommendation-computations into a simple matrix-multiplication operation. To further improve efficiency, we introduce a sparse-quantization-reuse method which reduces the recommendation-computation time by $9\times$ (compared to using CryptoRec directly), without compromising the accuracy. We demonstrate the efficiency and accuracy of CryptoRec on three real-world datasets. CryptoRec allows a server to estimate a user's preferences on thousands of items within a few seconds on a single PC, with the user's data homomorphically encrypted, while its prediction accuracy is still competitive with state-of-the-art recommender systems computing over clear data. Our solution enables Recommendation-as-a-Service on large datasets in a nearly real-time (seconds) level. | |
| Interdisciplinary Centre for Security, Reliability and Trust (SnT) > Applied Security and Information Assurance Group (APSIA) | |
| Researchers ; Professionals ; Students ; General public | |
| http://hdl.handle.net/10993/37324 | |
| 10.1145/3243734.3278504 | |
| FnR ; FNR5856658 > Qiang Tang > BRAIDS > Boosting Security And Efficiency In Recommender Systems > 15/04/2014 > 14/04/2017 > 2013 |
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