Preserving Privacy of Agents in Participatory-Sensing Schemes for Traffic Estimation

TL;DR

This paper introduces a privacy measure for agents in participatory-sensing traffic schemes and proposes an algorithm to optimize the trade-off between privacy infringement and estimation accuracy, considering various policies and network properties.

Contribution

It presents a novel privacy measure, an optimal policy algorithm, and explores the impact of network heterogeneity and centrality on privacy preservation.

Findings

Optimal policies depend on network distance thresholds.

Heterogeneous population densities influence privacy strategies.

Betweenness centrality correlates with privacy policy effectiveness.

Abstract

A measure of privacy infringement for agents (or participants) travelling across a transportation network in participatory-sensing schemes for traffic estimation is introduced. The measure is defined to be the conditional probability that an external observer assigns to the private nodes in the transportation network, e.g., location of home or office, given all the position measurements that it broadcasts over time. An algorithm for finding an optimal trade-off between the measure of privacy infringement and the expected estimation error, captured by the number of the nodes over which the participant stops broadcasting its position, is proposed. The algorithm searches over a family of policies in which an agent stops transmitting its position measurements if its distance (in terms of the number of hops) to the privacy sensitive node is smaller than a prescribed threshold. Employing such symmetric policies are advantageous in terms of the resources required for implementation and the ease of computation. The results are expanded to more general policies. Further, the effect of the heterogeneity of the population density on the optimal policy is explored. Finally, the relationship between the betweenness measure of centrality and the optimal privacy-preserving policy of the agents is numerically explored.