  Recommender systems have become an indispensable component in online services
during recent years. Effective recommendation is essential for improving the
services of various online business applications. However, serious privacy
concerns have been raised on recommender systems requiring the collection of
users' private information for recommendation. At the same time, the success of
e-commerce has generated massive amounts of information, making scalability a
key challenge in the design of recommender systems. As such, it is desirable
for recommender systems to protect users' privacy while achieving high-quality
recommendations with low-complexity computations.
  This paper proposes a scalable privacy-preserving item-based top-N
recommendation solution, which can achieve high-quality recommendations with
reduced computation complexity while ensuring that users' private information
is protected. Furthermore, the computation complexity of the proposed method
increases slowly as the number of users increases, thus providing high
scalability for privacy-preserving recommender systems. More specifically, the
proposed approach consists of two key components: (1) MinHash-based similarity
estimation and (2) client-side privacy-preserving prediction generation. Our
theoretical and experimental analysis using real-world data demonstrates the
efficiency and effectiveness of the proposed approach.
