Hierarchical neighborhood topology for privacy enhanced collaborative filtering

Privacy is an important challenge facing the growth of the Web and the propagation of various transaction models supported by it. Decentralized distributed models of computing are used to mitigate privacy breaches by
eliminating a single point of failure. However, end-users can still be attacked in order to discover their private information. This work proposes using distributed
hierarchical neighborhood formation in the CF algorithm to reduce this privacy hazard. It enables accurate CF recommendations, while allowing an attacker to learn at most the cumulative statistics of a large set of users. Our approach is evaluated on a number of widely-used CF datasets. Experimental results demonstrate that relatively large parts of the user profile can be obfuscated while a reasonable accuracy of the generated recommendations is still retained. Furthermore, only a small subset of users may be required for generating accurate recommendations, suggesting that the proposed approach is scalable.