Hiding Your Awful Online Choices Made More Efficient and Secure: A New Privacy-Aware Recommender System
Shibam Mukherjee, Roman Walch, Fredrik Meisingseth, Elisabeth Lex,, Christian Rechberger

TL;DR
This paper introduces a privacy-aware recommender system that significantly improves efficiency and scalability by combining machine learning with cryptographic techniques, enabling private recommendations on large datasets and low-power devices.
Contribution
It presents a novel hybrid approach that balances privacy and computational efficiency, allowing scalable private recommendations without trusted hardware assumptions.
Findings
Achieves three orders of magnitude reduction in time and memory usage.
Enables private recommendations on datasets with 100 million entries.
Operates efficiently on low-power SOC devices.
Abstract
Recommender systems are an integral part of online platforms that recommend new content to users with similar interests. However, they demand a considerable amount of user activity data where, if the data is not adequately protected, constitute a critical threat to the user privacy. Privacy-aware recommender systems enable protection of such sensitive user data while still maintaining a similar recommendation accuracy compared to the traditional non-private recommender systems. However, at present, the current privacy-aware recommender systems suffer from a significant trade-off between privacy and computational efficiency. For instance, it is well known that architectures that rely purely on cryptographic primitives offer the most robust privacy guarantees, however, they suffer from substantial computational and network overhead. Thus, it is crucial to improve this trade-off for better…
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Taxonomy
TopicsPrivacy, Security, and Data Protection
