Affiliations: [a] Center for Disclosure Avoidance Research, U.S. Census Bureau, Washington, DC, USA | [b] Department of Statistics, George Washington University, Washington, DC, USA | [c] U.S. Energy Information Administration, Washington, DC, USA
Correspondence:
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Corresponding author: Tapan K. Nayak, Center for Disclosure Avoidance Research, U.S. Census Bureau, Washington, DC and Department of Statistics, George Washington University, Washington, DC, USA. E-mail:tapan@gwu.edu
Abstract: Randomized response (RR) was introduced as a technique for protecting respondents' privacy in survey interviews regarding sensitive characteristics. In recent years, the basic RR ideas have been used and extended in other contexts. We discuss usage and recent advances of RR in confidentiality protection and in privacy preserving data mining. We discuss important differences between RR surveys and RR for confidentiality protection. In particular, for confidentiality protection, the data may be used to choose suitable randomization probabilities, but doing so renders well known inferences derived for RR surveys inapplicable. We examine one privacy breach criterion in data mining and propose a new privacy guarantee and a method for its achievement. We also discuss several new challenges and open problems for future research.
