Privacy-Preserving Filtering for Event Streams

TL;DR

This paper develops methods to ensure differential privacy in real-time event stream systems, enabling data collection and analysis without compromising individual user privacy, especially in multi-sensor environments.

Contribution

It introduces optimized extensions of the zero-forcing equalization mechanism for multi-input multi-output systems, incorporating input signal models for enhanced privacy guarantees.

Findings

Effective privacy-preserving filters for event streams

Application to building occupancy monitoring

Enhanced privacy guarantees in multi-sensor systems

Abstract

Many large-scale information systems such as intelligent transportation systems, smart grids or smart buildings collect data about the activities of their users to optimize their operations. To encourage participation and adoption of these systems, it is becoming increasingly important that the design process take privacy issues into consideration. In a typical scenario, signals originate from many sensors capturing events involving the users, and several statistics of interest need to be continuously published in real-time. This paper considers the problem of providing differential privacy guarantees for such multi-input multi-output systems processing event streams. We show how to construct and optimize various extensions of the zero-forcing equalization mechanism, which we previously proposed for single-input single-output systems. Some of these extensions can take a model of the input signals into account. We illustrate our privacy-preserving filter design methodology through the problem of privately monitoring and forecasting occupancy in a building equipped with multiple motion detection sensors.