Differential privacy is becoming one gold standard for protecting the of publicly shared data. It has been widely used in social science, data public health, information technology, and U.S. decennial census. Nevertheless, to guarantee differential privacy, existing methods may unavoidably alter conclusion original analysis, as privatization often changes sample distribution. This phenomenon kn...

Differential privacy (DP) was introduced as a measure of confidentiality protection by Dwork et al. (2006b) and Dwork (2006). Designed to guarantee confidentiality even in a worst-case scenario, differential privacy protects the information of any individual in the database even against an adversary with complete knowledge of the rest of the dataset. This strong guarantee is achieved by limitin...

Differential privacy is one of the most popular technologies in growing area privacy-conscious data analytics. But differential privacy, along with other privacy-enhancing technologies, may enable theater. In implementations certain algorithm parameters control tradeoff between protection for individuals and utility collector; thus, collectors who do not provide transparency into these obscure ...