Generating K-anonymous logs of people-tracing systems in surveilled environments

In surveilled environments, physical access of individuals can be achieved by a human through mechanical means such as locks and keys, or through technological means such as access control systems based on magnetic stripe, barcode, smart cards, biometric devices, RFID, cameras, and so on. Besides the importance of monitoring people accessing these places, another relevant issue concerns the possibility of tracking them inside the environment. Indeed, in this way, we can have information about the movements of people at any time and, in case of an incident, the analysis of these logs can be decisive to have a complete and fast reconstruction of this event. However, privacy right typically makes this solution unrealizable. In this paper, we discuss this topic and propose a technique to generate logs that allows us to trace people with a certain degree of uncertainty, in such a way that privacy is fully preserved. From this point of view, logs are generated according to a new k-anonymity property, for which we are able to guess the location of an individual, at a given time, with probability κ-1. A number of experiments show that the proposed method reaches the target in a good way, thus validating the approach. An important aspect of our technique is that it is implementable via very cheap devices, which is a relevant issue in pervasive environments where wireless devices with limited processing capability and power have to be utilized.