Incentives for Privacy Tradeoff in Community Sensing
This work addresses the challenge of balancing privacy and utility in community sensing systems, offering a practical solution for applications like environmental monitoring, though it is incremental in improving existing mechanisms.
The paper tackles the problem of recruiting participants with privacy concerns in community sensing by proposing SeqTGreedy, a budget-feasible and incentive-compatible mechanism, which achieves up to 30% cost reduction compared to state-of-the-art methods in an air quality monitoring case study.
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.