Enhancing Privacy of Information Brokering in Smart Districts by Adaptive Pseudonymization

Abstract

District-wide real-time information sharing provides new opportunities to optimize infrastructures and, for example, the energy consumption of smart cities. However, information collection introduces new privacy threats that must be addressed. Existing anonymization solutions are not sufficient for the brokering of streaming real-time measurements. Advanced adversaries may utilize information available from different sources and correlation analyses to reveal a measurement's actual source. We analyze security and privacy requirements and design a privacy-enhancing architecture for an information brokering platform. We propose an adaptive pseudonymization framework to make privacy attacks harder and to gain real-time awareness of the robustness of the privacy protection of platforms. Finally, we present an initial evaluation of the proposal using real-world energy consumption measurements.