Privacy-Preserving Crowd-Monitoring using Bloom Filters and Homomorphic Encryption

This paper introduces an architecture for crowd-monitoring which allows statistical counting for pedestrian dynamics while considering privacy-preservation for the individuals being sensed. Monitoring crowds of pedestrians has been an interesting area of study for many years. The recent prevalence of mobile devices paved the way for wide-scale deployments of infrastructures which perform automated sensing. Suddenly, people could be discreetly monitored by leveraging radio signals such as Wi-Fi probe requests periodically sent by their devices. However, this monitoring process implies dealing with sensitive data which is prone to privacy infringement by nature. While routinely performing their tasks, parties involved in this process can try to infer private information about individuals from the data they handle. Following privacy by design principles, we envision a construction which protects the short-term storage and processing of the collected privacy-sensitive sensor readings with strong cryptographic guarantees such that only the end-result (i.e. a statistical count) becomes available in the clear. We combine Bloom filters, to facilitate set membership testing for counting, with homomorphic encryption, to allow the oblivious performance of operations under encryption. We carry out an implementation of our solution using a resource-constrained device as a sensor and perform experiments which demonstrate its feasibility in practice.