Collaborative Filtering (CF) is an attractive and reliable recommendation technique. CF is typically implemented using a centralized storage of user profiles and this is a severe privacy danger, since an attack to this central repository can endanger the quality of the recommendations and result in a leak of personal data. This work investigates how a decentralized distributed storage of user profiles combined with data obfuscation techniques can mitigate the above dangers. In an experimental evaluation we initially show that relatively large parts of the profiles can be obfuscated with a minimal increase of Mean Average Error (MAE). This contradictory result motivates further experiments where we measured the increase in prediction error in two cases: a) when a more complex prediction task is considered, i.e., a data set containing more diverse (extreme) rating values; b) when only ratings with specific values are obfuscated. The results of these experiments clarify the roles of various rating values and will help to better implement an effective obfuscation policy.
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