Biblio

The rapid development of the Internet of Things (IoT) results in generating massive amounts of data. Significant portions of these data are sensitive since they reflect (directly or indirectly) peoples' behaviors, interests, lifestyles, etc. Protecting sensitive IoT data from privacy violations is a challenge since these data need to be communicated, processed, analyzed, and stored by public networks, servers, and clouds; most of them are untrusted parties for data owners. We propose a solution for protecting sensitive IoT data called Policy Enforcement Fog Module (PEFM). The major task of the PEFM solution is mandatory enforcement of privacy policies for sensitive IoT data-wherever these data are accessed throughout their entire lifecycle. The key feature of PEFM is its placement within the fog computing infrastructure, which assures that PEFM operates as closely as possible to data sources within the edge. PEFM enforces policies directly for local IoT applications. In contrast, for remote applications, PEFM provides a self-protecting mechanism based on creating and disseminating Active Data Bundles (ADBs). ADBs are software constructs bundling inseparably sensitive data, their privacy policies, and an execution engine able to enforce privacy policies. To prove effectiveness and efficiency of the proposed module, we developed a smart home proof-of-concept scenario. We investigate privacy threats for sensitive IoT data. We run simulation experiments, based on network calculus, for testing performance of the PEFM controls for different network configurations. The results of the simulation show that-even with using from 1 to 5 additional privacy policies for improved data privacy-penalties in terms of execution time and delay are reasonable (approx. 12-15% and 13-19%, respectively). The results also show that PEFM is scalable regarding the number of the real-time constraints for real-time IoT applications.

Some IoT data are time-sensitive and cannot be processed in clouds, which are too far away from IoT devices. Fog computing, located as close as possible to data sources at the edge of IoT systems, deals with this problem. Some IoT data are sensitive and require privacy controls. The proposed Policy Enforcement Fog Module (PEFM), running within a single fog, operates close to data sources connected to their fog, and enforces privacy policies for all sensitive IoT data generated by these data sources. PEFM distinguishes two kinds of fog data processing. First, fog nodes process data for local IoT applications, running within the local fog. All real-time data processing must be local to satisfy real-time constraints. Second, fog nodes disseminate data to nodes beyond the local fog (including remote fogs and clouds) for remote (and non-real-time) IoT applications. PEFM has two components for these two kinds of fog data processing. First, Local Policy Enforcement Module (LPEM), performs direct privacy policy enforcement for sensitive data accessed by local IoT applications. Second, Remote Policy Enforcement Module (RPEM), sets up a mechanism for indirectly enforcing privacy policies for sensitive data sent to remote IoT applications. RPEM is based on creating and disseminating Active Data Bundles-software constructs bundling inseparably sensitive data, their privacy policies, and an execution engine able to enforce privacy policies. To prove effectiveness and efficiency of the solution, we developed a proof-of-concept scenario for a smart home IoT application. We investigate privacy threats for sensitive IoT data and show a framework for using PEFM to overcome these threats.