The concept of differential privacy as a rigorous definition of privacy has emerged from the cryptographic community. However, further careful evaluation is needed before we can apply these theoretical results to privacy preservation in everyday data mining and statistical analysis. For example, many funding agencies and ethics boards frequently request that a power analysis be completed before a study is conducted, or before a study’s results are published. In this talk we present preliminary evaluation of how to integrate a differential privacy framework with the classical statistical hypothesis testing. We aim to develop concrete methodology that researchers can use. We derive rules for the sample size adjustment whereby both statistical efficiency and differential privacy can be achieved for the specific tests for binomial and normal random variables and in contingency tables.
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