The increasing societal demand for data privacy has led researchers to develop methods to preserve privacy in data analysis. However, outlier analysis, a fundamental data analytics task with critical applications in medicine, finance, and national security, has only been analyzed for a few specialized cases of data privacy. This work is the first to provide a general framework for private outlier analysis, which is a two-step process. First, we show how to identify the relevant problem-specifications and then provide a practical solution that formally meets these specifications.
Keywords: anomaly; differential privacy; outliers; privacy; security; sensitive privacy.