How to Use Information Theory to Mitigate Unfair Rating Attacks
Tim Muller, Dongxia Wang, Yang Liu, and Jie Zhang
In rating systems, users want to construct accurate opinions based on ratings. However, the accuracy is bounded by the amount of information transmitted (leaked) by ratings. Rating systems are susceptible to unfair rating attacks. These attacks may decrease the amount of leaked information, by introducing noise. A robust trust system attempts to mitigate the effects of these attacks on the information leakage. Defenders cannot influence the actual ratings: being honest or from attackers. There are other ways for the defenders to keep the information leakage high: blocking/selecting the right advisors, observing transactions and offering more choices. Blocking suspicious advisors can only decrease robustness. If only a limited number of ratings can be used, however, then less suspicious advisors are better, and in case of a tie, newer advisors are better. Observing transactions increases robustness. Offering more choices may increase robustness.