CryptoRec: Novel Collaborative Filtering Recommender Made Privacy-preserving Easy

CryptoRec: Novel Collaborative Filtering Recommender Made Privacy-preserving Easy
Title:
CryptoRec: Novel Collaborative Filtering Recommender Made Privacy-preserving Easy
Other Titles:
IEEE Transactions on Dependable and Secure Computing
Publication Date:
12 March 2021
Citation:
Wang, J., Chao, J., Tang, Q., Liu, Z., Khin, A. M. M. (2021). CryptoRec: Novel Collaborative Filtering Recommender Made Privacy-preserving Easy. IEEE Transactions on Dependable and Secure Computing, 1–1. doi:10.1109/tdsc.2021.3065752
Abstract:
With the explosive growth of user data, recommenders have become increasingly complicated. State-of-the-art algorithms often have high computational complexity and heavily use non-linear transformations. This fact makes the privacy-preserving problem more challenging, despite the significant advances in cryptography. To alleviate this problem, we propose a privacy-friendly recommender, CryptoRec. It only relies on additions and multiplications, which are efficiently supported by most cryptographic primitives. Different from others, in CryptoRec, the parameter space only contains item features (user features can be directly computed from the item features). This property allows CryptoRec to, (1) naturally achieve transferability if two datasets share the same item entries, which can benefit differential privacy protection; (2) directly estimate the preference of new users whose data is not included in the training set, drastically improving recommendation efficiency. We first evaluate CryptoRec on three real-world datasets. The evaluation results show that the accuracy is competitive with state-of-theart. Then, we build differential privacy into CryptoRec and leverage its transferability property to reduce the overall privacy loss. Lastly, we demonstrate the simplicity and efficiency of using CryptoRec to construct secure recommendation protocols based on homomorphic encryption schemes. Our results show that CryptoRec outperforms existing solutions in terms of both accuracy and efficiency.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the A*STAR - RIE2020 Advanced Manufacturing and Engineering (AME) Programmatic Programme
Grant Reference no. : A19E3b0099
Description:
© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISSN:
1545-5971
1941-0018
2160-9209
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