Optimized Query Forgery (OQF) [18] provides a theoretical approach to generate fake queries by measuring the Kullback-Leibler divergence between the user profile and the population distribution.
Contrary to the previous attack, the adversary knows the identity of the user and thus tries to pinpoint fake queries by analyzing the similarity between queries and the user profile.
As described in Section 4.1, the smoothing parameter α of SimAttack influences the similarity distance between a query and the user profile.
The adversary is able to distinguish between fake queries and real ones by comparing the similarity between the query q + and the user profile P id (i.e., s i m(q +,P id )) with the threshold δ.
If we consider first that the adversary does not have this external knowledge, it evaluates if the similarity between the query q + and the user profile P u is greater than a given threshold δ.
It first compares the similarity between the query q + and the user profile P u (i.e., s i m(q +,P u )) against the similarity between the query q + and the profile of fake queries P FQ (i.e., s i m(q +,P FQ )).
The algorithm determines if the similarity distance between the query q + and the user profile P id (i.e., s i m(q +,P id )) is greater than the similarity metric between the query q + and the profile of fake queries P FQ (i.e., s i m(q +,P FQ )).
The problem of detecting the similarity or the difference between users is not only based on the user profile on the social network, but also based on the data about user behavior such as posting entries, commenting, and liking.
Five different cases of matches between a concept A in the resource (or item) profile and a concept B in the user profile are considered.
