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You Zhou1, Yian Zhou1,2, Shigang Chen1

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This study introduces a strong privacy online survey scheme (SPS) using dual matrix masking (DM2) to protect sensitive data. A robust scheme (RSPS) further enhances security against collusion, ensuring accurate and private online surveys.

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Area of Science:

  • Computer Science
  • Information Security
  • Survey Methodology

Background:

  • Online surveys are increasingly used for data collection across various fields.
  • Traditional online survey methods face significant privacy challenges.
  • Existing privacy techniques lack the necessary strength for robust data protection.

Purpose of the Study:

  • To propose a novel online survey scheme ensuring strong privacy.
  • To address the limitations of current privacy protection methods in online surveys.
  • To develop a scheme that guarantees data correctness and verifiability.

Main Methods:

  • Introduction of a practical strong privacy online survey scheme (SPS).
  • Utilizing a novel data collection technique: dual matrix masking (DM2).
  • Development of a robust scheme (RSPS) with multiple distributed survey managers.

Main Results:

  • SPS guarantees correct tallying results with low computation overhead.
  • SPS achieves universal verifiability, robustness, and strong privacy.
  • RSPS provides robust strong privacy, even against joint collusion attacks.

Conclusions:

  • The proposed SPS and RSPS schemes offer efficient and secure solutions for online surveys.
  • These schemes ensure both accuracy and strong privacy in data collection.
  • DM2 technique provides a foundation for enhanced privacy in digital surveys.