CryptoRec: Privacy-preserving Recommendation as a Service
Jun Wang, Afonso Arriaga, Qiang Tang, and Peter Y.A. Ryan

TL;DR
CryptoRec introduces a privacy-preserving recommendation protocol that enables secure, efficient, and accurate recommendations using cryptographic techniques, suitable for real-world applications without compromising user or provider privacy.
Contribution
It presents CryptoRec, a novel recommender system compatible with homomorphic encryption, modeling user-item interactions in an item-only latent space for privacy-preserving recommendations.
Findings
Achieves recommendation within seconds on large datasets
Maintains competitive accuracy with state-of-the-art models
Supports privacy-preserving recommendations without re-training
Abstract
Recommender systems rely on large datasets of historical data and entail serious privacy risks. A server offering Recommendation as a Service to a client might leak more information than necessary regarding its recommendation model and dataset. At the same time, the disclosure of the client's preferences to the server is also a matter of concern. Devising privacy-preserving protocols using general cryptographic primitives (e.g., secure multi-party computation or homomorphic encryption), is a typical approach to overcome privacy concerns, but in conjunction with state-of-the-art recommender systems often yields far-from-practical solutions. In this paper, we tackle this problem from the direction of constructing crypto-friendly machine learning algorithms. In particular, we propose CryptoRec, a secure two-party computation protocol for Recommendation as a Service, which encompasses a…
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Taxonomy
TopicsCryptography and Data Security · Privacy-Preserving Technologies in Data · Stochastic Gradient Optimization Techniques
