New Regression Estimator Optimizes Privacy and Accuracy in Sensitive Surveys

This study addresses the challenge of collecting reliable data on sensitive topics by proposing a novel regression estimator that balances privacy protection and estimation accuracy. The approach integrates non-sensitive auxiliary variables within randomized response techniques to improve the estimation of sensitive population means. The estimator is theoretically unbiased and evaluated using data from existing randomized response models.

Originally published by gnews