Incentives for Privacy Tradeoff in Community Sensing

TL;DR

This paper introduces SeqTGreedy, a novel incentive mechanism for community sensing that optimally recruits privacy-concerned participants, balancing utility and cost through adaptive submodular optimization.

Contribution

It presents a new mechanism for budgeted participant recruitment in community sensing, addressing privacy concerns and achieving near-optimal utility with cost reduction.

Findings

Achieves up to 30% cost reduction compared to existing methods.

Demonstrates effectiveness in air quality monitoring case study.

Models privacy tradeoffs as an adaptive submodular optimization problem.

Abstract

Community sensing, fusing information from populations of privately-held sensors, presents a great opportunity to create efficient and cost-effective sensing applications. Yet, reasonable privacy concerns often limit the access to such data streams. How should systems valuate and negotiate access to private information, for example in return for monetary incentives? How should they optimally choose the participants from a large population of strategic users with privacy concerns, and compensate them for information shared? In this paper, we address these questions and present a novel mechanism, SeqTGreedy, for budgeted recruitment of participants in community sensing. We first show that privacy tradeoffs in community sensing can be cast as an adaptive submodular optimization problem. We then design a budget feasible, incentive compatible (truthful) mechanism for adaptive submodular maximization, which achieves near-optimal utility for a large class of sensing applications. This mechanism is general, and of independent interest. We demonstrate the effectiveness of our approach in a case study of air quality monitoring, using data collected from the Mechanical Turk platform. Compared to the state of the art, our approach achieves up to 30% reduction in cost in order to achieve a desired level of utility.