Preventing Unwanted Social Inferences with Classification Tree Analysis

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 classificatio...

• Type: Conference
• Year: 2009
• Where: ICTAI
• Authors: Sara Motahari, Sotirios G. Ziavras, Quentin Jones
• Keywords: Artificial Intelligence, ICTAI 2009, Inference Risk, Information Entropy, Internal Nodes