CryptoRec: Novel Collaborative Filtering Recommender Made Privacy-Preserving Easy

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 advance...

Full description

Saved in:
Bibliographic Details
Published inIEEE transactions on dependable and secure computing Vol. 19; no. 4; pp. 2622 - 2634
Main Authors Wang, Jun, Jin, Chao, Tang, Qiang, Liu, Zhe, Aung, Khin Mi Mi
Format Journal Article
LanguageEnglish
Published Washington IEEE 01.07.2022
IEEE Computer Society
Subjects
Algorithms
Computational modeling
Cryptography
Data models
Datasets
Differential privacy
Efficiency
Encryption
homomorphic encryption
Linear transformations
Neural networks
Privacy
Privacy-preserving
recommender system
Training
Online AccessGet full text

Cover

More Information
Summary: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-the-art. 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.
ISSN:1545-5971
1941-0018
DOI:10.1109/TDSC.2021.3065752