LEPA: Incentivizing Long-term Privacy-preserving Data Aggregation in Crowdsensing

TL;DR

LEPA is a novel incentive mechanism designed to promote long-term participation in real-time privacy-preserving data aggregation for crowdsensing, balancing privacy, accuracy, and compensation.

Contribution

It introduces a joint optimization framework and an efficient online auction that ensures truthful, rational participation while handling strategic users and NP-hard task selection.

Findings

LEPA achieves near-optimal performance in simulations.

The mechanism guarantees truthfulness and individual rationality.

It effectively balances privacy, accuracy, and incentives over time.

Abstract

In this paper, we study the incentive mechanism design for real-time data aggregation, which holds a large spectrum of crowdsensing applications. Despite extensive studies on static incentive mechanisms, none of these are applicable to real-time data aggregation due to their incapability of maintaining PUs' long-term participation. We emphasize that, to maintain PUs' long-term participation, it is of significant importance to protect their privacy as well as to provide them a desirable cumulative compensation. Thus motivated, in this paper, we propose LEPA, an efficient incentive mechanism to stimulate long-term participation in real-time data aggregation. Specifically, we allow PUs to preserve their privacy by reporting noisy data, the impact of which on the aggregation accuracy is quantified with proper privacy and accuracy measures. Then, we provide a framework that jointly optimizes the incentive schemes in different time slots to ensure desirable cumulative compensation for PUs and thereby prevent PUs from leaving the system halfway. Considering PUs' strategic behaviors and combinatorial nature of the sensing tasks, we propose a computationally efficient on-line auction with close-to-optimal performance in presence of NP-hardness of winner user selection. We further show that the proposed on-line auction satisfies desirable properties of truthfulness and individual rationality. The performance of LEPA is validated by both theoretical analysis and extensive simulations.