Streamlined Randomized Response Model Designed to Estimate Extremely Confidential Attributes

  • Ahmad M. Aboalkhair Department of Applied Statistics and Insurance, Faculty of Commerce, Mansoura University, Egypt; Department of Quantitative Methods, College of Business, King Faisal University, Saudi Arabia
  • El-Emam El-Hosseiny Department of Insurance and Risk Management, College of Business, Imam Mohammad Ibn Saud Islamic University (IMSIU), Saudi Arabia
  • Mohammad A. Zayed Department of Mathematics and Statistics, College of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Saudi Arabia
  • Tamer Elbayoumi Department of Applied Statistics and Insurance, Faculty of Commerce, Mansoura University, Egypt; Mathematics and Statistics Department, North Carolina A&T State University, 1601 East Market Street, Greensboro, NC 27411, USA
  • Mohamed Ibrahim Department of Applied, Mathematical & Actuarial Statistics, Faculty of Commerce, Damietta University, Egypt; Department of Quantitative Methods, College of Business, King Faisal University, Saudi Arabia
  • A. M. Elshehawey Department of Applied, Mathematical & Actuarial Statistics, Faculty of Commerce, Damietta University, Egypt
DOI: https://doi.org/10.19139/soic-2310-5070-2644
Keywords: Randomized response technique, response error, privacy protection, confidential attributes, incomplete truthfulness

Abstract

When addressing highly sensitive topics, respondents may provide incomplete or untruthful disclosures, compromising data accuracy. To mitigate this issue, this study introduces an innovative and efficient randomized response framework designed to enhance the estimation of highly sensitive attributes. The proposed model refines Aboalkhair’s (2025) framework, which has been established as an effective alternative to Warner’s and Mangat’s models. This study evaluates the conditions under which the new model achieves greater efficiency than existing approaches. Through theoretical analysis and numerical simulations—accounting for partial truthful reporting—the results demonstrate the model’s superior efficiency. Additionally, the paper quantifies the privacy protection level afforded by the new approach.
Published
2025-07-14
How to Cite
Ahmad M. Aboalkhair, El-Hosseiny, E.-E., Mohammad A. Zayed, Tamer Elbayoumi, Mohamed Ibrahim, & A. M. Elshehawey. (2025). Streamlined Randomized Response Model Designed to Estimate Extremely Confidential Attributes. Statistics, Optimization & Information Computing, 14(5), 2200-2207. https://doi.org/10.19139/soic-2310-5070-2644
Issue
Vol 14 No 5 (2025)
Section
Research Articles
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