A Differentially Private Incentive Method for Sensitive Survey

This paper explores the incentive mechanisms and privacy protection methods of questionnaire surveys based on differential privacy. With the advent of the big data era, user privacy protection has become a significant issue in data collection and analysis. This study combines differential privacy theory, utilizing the Laplace mechanism and the exponential mechanism to securely process questionnaire data and ensure that the privacy of each participant is not compromised. By designing an effective incentive mechanism, we aim to encourage more participants to actively engage in the questionnaire, thereby improving the effectiveness and representativeness of data collection. Additionally, we conduct an in-depth analysis of the balance between privacy protection and data quality to safeguard personal information while minimizing the loss of data validity. The research findings indicate that through the use of differential privacy methods, questionnaire surveys can obtain valuable research data while protecting participants' privacy, offering new ideas and solutions for research in related fields.