Incentivizing Verifiable Privacy-Protection Mechanisms for Offline Crowdsensing Applications

Area of Science:

• Computer Science
• Information Security
• Distributed Systems

Background:

• Existing crowdsensing incentive mechanisms prioritize economic goals like truthfulness and utility maximization.
• Real-world crowdsensing faces significant privacy and security challenges, including cost privacy concerns.
• There is a need for incentive mechanisms that address both economic efficiency and robust privacy protection.

Purpose of the Study:

• To investigate and develop offline verifiable privacy-protection incentive mechanisms for crowdsensing.
• To address privacy concerns for both users and the platform in crowdsensing environments.
• To ensure the verifiable correctness of payments exchanged between the platform and users.

Main Methods:

• Proposed a general verifiable privacy-protection incentive mechanism for offline homogeneous and heterogeneous sensing job models.
• Developed a more complex verifiable privacy-protection incentive mechanism tailored for offline submodular sensing job models.
• Utilized auction-based frameworks to integrate remuneration as the primary user incentive.

Main Results:

• The proposed mechanisms ensure user and platform privacy protection.
• Verifiable correctness of payments is guaranteed between the platform and users.
• The mechanisms achieve the same revenue as non-privacy-preserving alternatives.
• Experimental validation confirms the scalability, efficiency, and applicability for mobile crowdsensing.

Conclusions:

• Developed effective verifiable privacy-protection incentive mechanisms for offline crowdsensing.
• These mechanisms balance economic goals with critical privacy and security requirements.
• The solutions are practical for mobile devices and scalable for real-world crowdsensing applications.