Biblio

Wireless sensor networks (WSNs) are continually exposed to many types of attacks. Among these, the attacks targeted at the base station are the most devastating ones since this essential device processes and analyses all traffic generated in the network. Moreover, this feature can be exploited by a passive adversary to determine its location based on traffic analysis. This receiver-location privacy problem can be reduced by altering the traffic pattern of the network but the adversary may still be able to reach the base station if he gains access to the routing tables of a number of sensor nodes. In this paper we present HISP-NC (Homogenous Injection for Sink Privacy with Node Compromise protection), a receiver-location privacy solution that consists of two complementary schemes which protect the location of the base station in the presence of traffic analysis and node compromise attacks. The HISP-NC data transmission protocol prevents traffic analysis by probabilistically hiding the flow of real traffic with moderate amounts of fake traffic. Moreover, HISP-NC includes a perturbation mechanism that modifies the routing tables of the nodes to introduce some level of uncertainty in attackers capable of retrieving the routing information from the nodes. Our scheme is validated both analytically and experimentally through extensive simulations.

This article introduces a privacy manager for IoT data based on Edge Computing. This poses the advantage that privacy is enforced before data leaves the control of the user, who is provided with a tool to express data sharing preferences based on a novel context-aware privacy language.

Software engineering and information security have traditionally followed divergent paths but lately some efforts have been made to consider security from the early phases of the Software Development Life Cycle (SDLC). This paper follows this line and concentrates on the incorporation of trust negotiations during the requirements engineering phase. More precisely, we provide an extension to the SI* modelling language, which is further formalised using answer set programming specifications to support the automatic verification of the model and the detection of privacy conflicts caused by trust negotiations.