Physical distancing between individuals is key to preventing the spread of a disease such as COVID-19. On the one hand, having access to information about physical interactions is critical for decision makers; on the other, this information is sensitive and can be used to track individuals. In this work, we design Poirot, a system to collect aggregate statistics about physical interactions in a privacy-preserving manner. We show a preliminary evaluation of our system that demonstrates the scalability of our approach even while maintaining strong privacy guarantees.

Sensor networks have emerged as a fundamentally new tool for monitoring inaccessible environments. They are distinguished from traditional sensors by strict limitations on system bandwidth and sensor energy resources. These constraints motivate the use of data compression at each sensor. Location finding is an important application of sensor networks, and estimation of the time delay between data from different sensors is a key step in localization. In this work, new quantizer designs specific to the time-delay estimation problem in sensor networks are presented. The goal for these new application-specific encoders is to achieve the best time delay estimate at a given bandwidth budget or latency bound, or minimize the rate required to reach an estimate with desired accuracy.