  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 performance.
This paper presents a novel privacy-aware recommender system that combines
privacy-aware machine learning algorithms for practical scalability and
efficiency with cryptographic primitives like Homomorphic Encryption and
Multi-Party Computation - without assumptions like trusted-party or secure
hardware - for solid privacy guarantees. Experiments on standard benchmark
datasets show that our approach results in time and memory gains by three
orders of magnitude compared to using cryptographic primitives in a standalone
for constructing a privacy-aware recommender system. Furthermore, for the first
time our method makes it feasible to compute private recommendations for
datasets containing 100 million entries, even on memory-constrained low-power
SOC (System on Chip) devices.
