[Paper Review] Privacy-Preserving Verifiable Incentive Mechanism for Crowdsourcing Market Applications

Jiajun Sun|arXiv (Cornell University)|Nov 25, 2013

Mobile Crowdsensing and Crowdsourcing34 references3 citations

TL;DR

This paper proposes two privacy-preserving verifiable incentive mechanisms for crowd sensing that ensure truthful user participation while protecting the privacy of users and platforms. By leveraging order-preserving encryption and a trusted auction issuer, the mechanisms enable users to verify payment correctness without revealing sensitive data, achieving the same performance as non-private counterparts with minimal overhead on mobile devices.

ABSTRACT

Recently, a novel class of incentive mechanisms is proposed to attract extensive users to truthfully participate in crowd sensing applications with a given budget constraint. The class mechanisms also bring good service quality for the requesters in crowd sensing applications. Although it is so important, there still exists many verification and privacy challenges, including users' bids and subtask information privacy and identification privacy, winners' set privacy of the platform, and the security of the payment outcomes. In this paper, we present a privacy-preserving verifiable incentive mechanism for crowd sensing applications with the budget constraint, not only to explore how to protect the privacies of users and the platform, but also to make the verifiable payment correct between the platform and users for crowd sensing applications. Results indicate that our privacy-preserving verifiable incentive mechanism achieves the same results as the generic one without privacy preservation.

Motivation & Objective

• To address the critical challenge of privacy leakage in crowd sensing applications, particularly concerning user costs, sensing preferences, and platform payment behavior.
• To ensure verifiable correctness of payments from the platform to users, preventing malicious behavior such as false results or fictitious bids.
• To design incentive mechanisms that maintain truthfulness and budget feasibility while preserving privacy through cryptographic techniques.
• To enable practical deployment on mobile devices by minimizing communication and computation overhead.
• To balance the trade-off between privacy preservation and verifiability, which are often conflicting in incentive mechanisms.

Proposed method

• Utilizes order-preserving encryption (OPES) to allow the auction issuer to sort and compute threshold payments on encrypted user bids without revealing underlying values.
• Employs oblivious transfer (OT) and blind signatures to protect user identities and prevent bid repudiation during bid submission.
• Introduces a trusted auction issuer (AI) that selects winners and computes payments based on encrypted sensing profiles, ensuring privacy and correctness.
• Uses a bulletin board to store verification data, enabling users to cryptographically verify payments without exposing sensitive information.
• Applies a marginal utility per bid computation model to optimize winner selection and payment determination under budget constraints.
• Employs the Nyberg-Rueppel digital signature scheme to prevent repudiation and ensure authenticity of bids and payments.

Experimental results

Research questions

• RQ1 How can user and platform privacy be preserved in incentive mechanisms for crowd sensing without compromising payment verifiability?
• RQ2 Can order-preserving encryption be effectively used to enable correct threshold payment computation on encrypted data while maintaining privacy?
• RQ3 What is the communication and computation overhead of the proposed mechanisms, and can they be efficiently deployed on mobile devices?
• RQ4 How does the mechanism ensure that the platform cannot manipulate payments or misreport results?
• RQ5 To what extent do the privacy-preserving mechanisms preserve the same performance as non-private counterparts?

Key findings

• The proposed mechanisms achieve the same outcome as generic incentive mechanisms without privacy preservation, ensuring identical performance in terms of winner selection and payment computation.
• Communication overhead is significantly reduced, with the worst-case complexity being O(m²) for payment determination, and much lower in practice.
• Computation overhead per user remains minimal, with average run time for cryptographic operations (e.g., OT, sorting) at 28 milliseconds, making the scheme suitable for mobile devices.
• The system reaches a saturation point in computation overhead as the number of winners increases, stabilizing at a constant value, indicating scalability.
• Verification overhead is negligible due to pre-stored data on the bulletin board, enabling efficient and secure user verification.
• The mechanisms prevent bid repudiation and ensure that no party—including the platform—can access bid information before the mechanism closes.