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Privacy-Preserving Task-Matching and Multiple-Submissions Detection in Crowdsourcing.

Jie Xu1,2, Zhaowen Lin2,3, Jun Wu2,3

  • 1The School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, China.

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|April 30, 2021
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Summary

This study introduces a privacy-preserving crowdsourcing scheme using inner-product cryptography and proof of knowledge (PoK) to securely match workers to tasks and prevent duplicate submissions, enhancing data integrity and worker privacy.

Keywords:
anonymous multi-submission detectioninner-product encryptiontask-matchingzero-knowledge proof

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Area of Science:

  • Computer Science
  • Information Security
  • Human-Computer Interaction

Background:

  • Crowdsourcing offers efficient data aggregation but faces significant privacy risks.
  • Sensitive task content and worker attributes are vulnerable during task matching and submission.
  • Anonymity can lead to fraudulent multiple submissions for rewards.

Purpose of the Study:

  • To develop a privacy-preserving scheme for task matching and multiple-submission detection in crowdsourcing.
  • To protect task confidentiality and worker attribute privacy.
  • To ensure fair task distribution and prevent reward abuse.

Main Methods:

  • Utilized multi-authority inner-product encryption for fine-grained, attribute-based task matching.
  • Implemented a proof of knowledge (PoK) protocol to restrict workers to a single submission per task.
  • Ensured unlinkability between different tasks performed by the same worker.

Main Results:

  • The proposed scheme effectively protects task confidentiality and worker privacy.
  • Fine-grained task matching based on worker attributes is achieved.
  • Multiple submissions for a single task are successfully detected without revealing worker identity.

Conclusions:

  • The developed scheme is effective and feasible for enhancing privacy and security in crowdsourcing.
  • It addresses key challenges in sensitive data handling and reward integrity.
  • Enables secure and efficient crowdsourcing operations.