Efficiency Boosting of Secure Cross-platform Recommender Systems over Sparse Data

by   Hao Ren, et al.

Fueled by its successful commercialization, the recommender system (RS) has gained widespread attention. However, as the training data fed into the RS models are often highly sensitive, it ultimately leads to severe privacy concerns, especially when data are shared among different platforms. In this paper, we follow the tune of existing works to investigate the problem of secure sparse matrix multiplication for cross-platform RSs. Two fundamental while critical issues are addressed: preserving the training data privacy and breaking the data silo problem. Specifically, we propose two concrete constructions with significantly boosted efficiency. They are designed for the sparse location insensitive case and location sensitive case, respectively. State-of-the-art cryptography building blocks including homomorphic encryption (HE) and private information retrieval (PIR) are fused into our protocols with non-trivial optimizations. As a result, our schemes can enjoy the HE acceleration technique without privacy trade-offs. We give formal security proofs for the proposed schemes and conduct extensive experiments on both real and large-scale simulated datasets. Compared with state-of-the-art works, our two schemes compress the running time roughly by 10* and 2.8*. They also attain up to 15* and 2.3* communication reduction without accuracy loss.


page 13

page 14

page 17


Exploiting Data Sparsity in Secure Cross-Platform Social Recommendation

Social recommendation has shown promising improvements over traditional ...

Privacy and correctness trade-offs for information-theoretically secure quantum homomorphic encryption

Quantum homomorphic encryption, which allows computation by a server dir...

New Secure Sparse Inner Product with Applications to Machine Learning

Sparse inner product (SIP) has the attractive property of overhead being...

Data Privacy with Homomorphic Encryption in Neural Networks Training and Inference

The use of Neural Networks (NNs) for sensitive data processing is becomi...

Secure Social Recommendation based on Secret Sharing

Nowadays, privacy preserving machine learning has been drawing much atte...

CryptoRec: Secure Recommendations as a Service

Recommender systems rely on large datasets of historical data and entail...

Please sign up or login with your details

Forgot password? Click here to reset