Privacy-enhancing Aggregation of Internet of Things Data via Sensors Grouping

Big data collection practices using Internet of Things (IoT) pervasive technologies are often privacy-intrusive and result in surveillance, profiling, and discriminatory actions over citizens that in turn undermine the participation of citizens to the development of sustainable smart cities. Nevertheless, real-time data analytics and aggregate information open up tremendous opportunities for managing and regulating smart city infrastructures in a more efficient and sustainable way. The privacy-enhancing aggregation of distributed sensor data such as residential energy consumption or traffic information, is the research focus and challenge tackled in this paper. A baseline scenario is considered in which IoT sensor data are shared directly with an untrustworthy central aggregator. Citizens have the option to choose their privacy level by reducing the quality of the shared data at a cost of a lower accuracy in data analytics services. A grouping mechanism is introduced that improves privacy by sharing data aggregated first at a group level compared to a baseline scenario in which each individual shares data directly to the central aggregator. Group-level aggregation obfuscates sensor data of individuals, in a similar fashion as differential privacy and homomorphic encryption schemes, thus inference of privacy-sensitive information from single sensors becomes computationally harder compared to the baseline scenario, while accuracy is preserved. Furthermore, if groups are large enough, privacy improves independently of the individual’s privacy choices. Inter-group effects such as the influence of individual choices on privacy of other group members are studied. Finally, several grouping strategies are evaluated and compared using real-world data from two smart city pilot projects. Implications for the design of incentive mechanisms are discussed.