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More Efficient Secure Matrix Multiplication for Unbalanced Recommender Systems
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DOI：10.1109/TDSC.2021.3139318.png)
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摘要
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With recent advances in homomorphic encryption (HE), it becomes feasible to run non-interactive machine learning (ML) algorithms on encrypted data without decryption. In this work, we propose novel encoding methods to pack matrix in a compact way and more efficient methods to perform matrix multiplication on homomorphically encrypted data, leading to a speed boost of 1:5 x -20x for slim rectangular matrix multiplication compared with state-of-the-art. Moreover, we integrate our optimized secure matrix arithmetic with the MPI distributed computing framework, achieving scalable parallel secure matrix computation. Equipped with the optimized matrix multiplication, we propose uSCORE, a privacy-preserving cross-domain recommendation system for the unbalanced scenario, where a big data owner provides recommendation as a service to a client who has less data and computation power. Our design delegates most of the computation to the service provider, and has a low communication cost, which previous works failed to achieve. For a client who has 16 million user-item pairs to update, it only needs about 3 minutes (in the LAN setting) to prepare the encrypted data. The server can finish the update process on the encrypted data in less than half an hour, effectively reducing the client's test error from 0.72 to 0.62.
Keyword:
Cryptography
Matrix decomposition
Machine learning
Sparse matrices
Encoding
Computational modeling
Training
Homomorphic encryption
secure matrix multiplication
collaborative filtering
sparse SVD optimization
期刊
IF:
7.5
论文数:
2.5K
被引数:
9.6K
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