Non-randomized scrambling models for sensitive quantitative attribute using innocuous characteristics

Abstract

In this article, two non-randomized scrambling models have been presented for estimation of mean of a quantitative sensitive attribute. Information on two unrelated non-sensitive qualitative attributes have been utilized for the purpose. An extensive simulation study has been conducted to compare the performance of the proposed models with respect to a contemporary model in terms of both percentage relative efficiency and privacy protection for various values of the parameters. The encouraging results suggest that the proposed models may be useful for survey statisticians in their field work.

Keywords:

• Auxiliary information
• Benford law
• non-randomized response techniques
• quantitative sensitive variables
• scrambling
• survey sampling

Acknowledgments

We express our deepest gratitude to the anonymous reviewers whose diligent and valuable suggestions helped bring the manuscript to its current state.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

We are thankful to the Department of Science and Technology, Science & Engineering Research Board (DST-SERB) for providing financial assistance under Grant EMR/2017/000882.