Privacy Preserving Data Publishing for Recommender System

Driven by mutual benefits, exchange and publication of data among various parties is an inevitable trend. However, released data often contains sensitive information thus direct publication violates individual privacy. This undertaking is in the scope of privacy preserving data publishing (PPDP). Among many privacy models, K- anonymity framework is popular and well-studied, it protects data by constructing groups of anonymous records such that each record in the table released is covered by no fewer than k-1 other records. This thesis investigates different privacy models and focus on achieving k-anonymity for large scale and sparse databases, especially recommender systems. We present a general process for anonymization of large scale database. A preprocessing phase strategically extracts preference matrix from original data by Singular Value Decomposition (SVD) eliminates the high dimensionality and sparsity problem. A new clustering based k-anonymity heuristic named Bisecting K-Gather (BKG) is invented and proved to be efficient and accurate. To support customized user privacy assignments, we also proposed a new concept called customized k-anonymity along with a corresponding algorithm. Experiments on MovieLens database are assessed and also presented. The results show we can release anonymized data with low compromising privacy