Reference : Novel Collaborative Filtering Recommender Friendly to Privacy Protection
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
Security, Reliability and Trust
Novel Collaborative Filtering Recommender Friendly to Privacy Protection
Wang, Jun mailto []
Tang, Qiang mailto []
Delerue Arriaga, Afonso mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > APSIA >]
Ryan, Peter Y A mailto [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS) >]
International Joint Conference on Artificial Intelligence (IJCAI 2019), Macao 10-16 August 2019
International Joint Conferences on Artificial Intelligence Organization
The 28th International Joint Conference on Artificial Intelligence (IJCAI 2019)
from 10-08-2019 to 16-08-2019
[en] Machine Learning ; Recommender Systems ; Homomorphic Encryption
[en] Nowadays, recommender system is an indispensable tool in many information services, and a large number of algorithms have been designed and implemented. However, fed with very large datasets, state-of-the-art recommendation algorithms often face an efficiency bottleneck, i.e., it takes huge amount of computing resources to train a recommendation model. In order to satisfy the needs of privacy-savvy users who do not want to disclose their information to the service provider, the complexity of most existing solutions becomes prohibitive. As such, it is an interesting research question to design simple and efficient recommendation algorithms that achieve reasonable accuracy and facilitate privacy protection at the same time. In this paper, we propose an efficient recommendation algorithm, named CryptoRec, which has two nice properties: (1) can estimate a new user's preferences by directly using a model pre-learned from an expert dataset, and the new user's data is not required to train the model; (2) can compute recommendations with only addition and multiplication operations. As to the evaluation, we first test the recommendation accuracy on three real-world datasets and show that CryptoRec is competitive with state-of-the-art recommenders. Then, we evaluate the performance of the privacy-preserving variants of CryptoRec and show that predictions can be computed in seconds on a PC. In contrast, existing solutions will need tens or hundreds of hours on more powerful computers.
Researchers ; Professionals ; Students ; General public
FnR ; FNR5856658 > Qiang Tang > BRAIDS > Boosting Security And Efficiency In Recommender Systems > 15/04/2014 > 14/04/2017 > 2013

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