Sensor selection for fine-grained behavior verification that respects privacy (extended version)

TL;DR

This paper introduces a novel sensor selection framework that balances fine-grained behavior verification with privacy preservation by using sensor ambiguity, providing computational insights and scalable solutions.

Contribution

It formulates a new approach to sensor selection that incorporates multiple behaviors and privacy constraints, connecting behavior verification with privacy guarantees in a unified model.

Findings

The problem is computationally intractable in general.

An optimization approach exploits inter-constraint relationships.

Case studies demonstrate scalability and effectiveness.

Abstract

A useful capability is that of classifying some agent's behavior using data from a sequence, or trace, of sensor measurements. The sensor selection problem involves choosing a subset of available sensors to ensure that, when generated, observation traces will contain enough information to determine whether the agent's activities match some pattern. In generalizing prior work, this paper studies a formulation in which multiple behavioral itineraries may be supplied, with sensors selected to distinguish between behaviors. This allows one to pose fine-grained questions, e.g., to position the agent's activity on a spectrum. In addition, with multiple itineraries, one can also ask about choices of sensors where some behavior is always plausibly concealed by (or mistaken for) another. Using sensor ambiguity to limit the acquisition of knowledge is a strong privacy guarantee, a form of guarantee which some earlier work examined under formulations distinct from our inter-itinerary conflation approach. By concretely formulating privacy requirements for sensor selection, this paper connects both lines of work in a novel fashion: privacy-where there is a bound from above, and behavior verification-where sensors choices are bounded from below. We examine the worst-case computational complexity that results from both types of bounds, proving that upper bounds are more challenging under standard computational complexity assumptions. The problem is intractable in general, but we introduce an approach to solving this problem that can exploit interrelationships between constraints, and identify opportunities for optimizations. Case studies are presented to demonstrate the usefulness and scalability of our proposed solution, and to assess the impact of the optimizations.