Statistical Verification of Traffic Systems with Expected Differential Privacy

TL;DR

This paper introduces a statistical verification method for traffic systems that ensures privacy through expected differential privacy, balancing verification accuracy with data confidentiality in uncertain, multi-agent environments.

Contribution

It proposes the concept of expected differential privacy for statistical model checking and develops an exponential randomization mechanism to guarantee privacy in traffic system verification.

Findings

SMC accurately verifies traffic models without high computational cost.

EDP effectively bounds algorithm outputs to protect privacy.

Case study confirms the approach's practicality and efficiency.

Abstract

Traffic systems are multi-agent cyber-physical systems whose performance is closely related to human welfare. They work in open environments and are subject to uncertainties from various sources, making their performance hard to verify by traditional model-based approaches. Alternatively, statistical model checking (SMC) can verify their performance by sequentially drawing sample data until the correctness of a performance specification can be inferred with desired statistical accuracy. This work aims to verify traffic systems with privacy, motivated by the fact that the data used may include personal information (e.g., daily itinerary) and get leaked unintendedly by observing the execution of the SMC algorithm. To formally capture data privacy in SMC, we introduce the concept of expected differential privacy (EDP), which constrains how much the algorithm execution can change in the expectation sense when data change. Accordingly, we introduce an exponential randomization mechanism for the SMC algorithm to achieve the EDP. Our case study on traffic intersections by Vissim simulation shows the high accuracy of SMC in traffic model verification without significantly sacrificing computing efficiency. The case study also shows EDP successfully bounding the algorithm outputs to guarantee privacy.