The Locality Sensitive Hashing (LSH) technique of scalably finding nearest-neighbors can be adapted to enable discovering similar users while preserving their privacy. The key idea is to compute the user profile on the end-user device, apply LSH on the local profile, and use the LSH cluster identifier as the interest group identifier of a user. By properties of LSH, the interest group comprises other users with similar interests. The collective behavior of the members of the interest group is anonymously collected at some aggregation node to generate recommendations for the group members. The quality of recommendation depends on the efficiency of the LSH clustering algorithm, i.e. its capability of gathering similar users. In contrast, with conventional usage of LSH (for scalability and not privacy), in our framework one can not perform a linear search over the cluster members to identify the nearest neighbors and to prune away false positives. A good clustering quality is therefore of functional importance for our system. We report in this work how changing the nature of LSH inputs, which in our case corresponds to the user profile representations, impacts the performance of LSH-based clustering and the final quality of recommendations. We present extensive performance evaluations of the LSH-based privacypreserving recommender system using two large datasets of MovieLens ratings and Delicious bookmarks, respectively.
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