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Big data collection practices using Internet of Things (IoT) pervasivetechnologies are often privacy-intrusive and result in surveillance, profiling,and discriminatory actions over citizens that in turn undermine theparticipation of citizens to the development of sustainable smart cities.Nevertheless, real-time data analytics and aggregate information from IoTdevices open up tremendous opportunities for managing smart cityinfrastructures. The privacy-enhancing aggregation of distributed sensor data,such as residential energy consumption or traffic information, is the researchfocus of this paper. Citizens have the option to choose their privacy level byreducing the quality of the shared data at a cost of a lower accuracy in dataanalytics services. A baseline scenario is considered in which IoT sensor dataare shared directly with an untrustworthy central aggregator. A groupingmechanism is introduced that improves privacy by sharing data aggregated firstat a group level compared as opposed to sharing data directly to the centralaggregator. Group-level aggregation obfuscates sensor data of individuals, in asimilar fashion as differential privacy and homomorphic encryption schemes,thus inference of privacy-sensitive information from single sensors becomescomputationally harder compared to the baseline scenario. The proposed systemis evaluated using real-world data from two smart city pilot projects. Privacyunder grouping increases, while preserving the accuracy of the baselinescenario. Intra-group influences of privacy by one group member on the otherones are measured and fairness on privacy is found to be maximized betweengroup members with similar privacy choices. Several grouping strategies arecompared. Grouping by proximity of privacy choices provides the highest privacygains. The implications of the strategy on the design of incentives mechanismsare discussed.

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