A serious threat to user privacy in new mobile and web2.0 applications stems from 'social inferences'. These unwanted inferences are related to the users' identity, current location and other personal information. We have previously introduced 'inference functions' to estimate the social inference risk based on information entropy. In this paper, after analyzing the problem and reviewing our risk estimation method, we create a decision tree to distinguish between high risk and normal situations. To evaluate our methodology, test and training datasets were collected during a large mobile-phone field study for a location-aware application. The classification tree employs our two inference functions, for the current and past situations, as internal nodes. Our results show that the achieved true classification rates are significantly better than approaches that employ other available features for the internal nodes of the trees. The results also suggest that common classification tools cannot accurately capture the information entropy for social applications. This is mostly due to the lack of enough training data for high-risk, low-entropy situations and outliers. Thus, we conclude that estimating the information entropy and the relevant inference risk using a pre-processor can yield a simpler and more accurate classification tree.