Streamlined Randomized Response Model Designed to Estimate Extremely Confidential Attributes

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.