Joshua has more than 10 years of experience applying various methods of statistical disclosure limitation to survey data as well as administrative data. Josh has used his statistical knowledge and coding skills to produce disclosure safe aggregate data products by applying complementary cell suppression to complex sets of tables. Josh has also used methods such as global recoding and local suppression to produce k-anonymous micro-data files. Josh also has experience with synthetic data and novel use of sampling to produce disclosure safe data products.

Josh applied the above methods to produce the DE-SynPUF, a realistic set of claims data in the public domain while providing the very highest degree of protection to the Medicare beneficiaries’ protected health information, for the Center Medicare and Medicaid Services. Josh worked with the Equal Employment Opportunity Council to apply complementary suppression multiple years of the EE01 data set. The EE01 describes the employee demographics of private employers with 100 or more employees and federal contractors with 50 or more employees. In addition, Josh manages a team of analysts that review analytic extracts exported from the NORC Data Enclave. This team applies pre-defined disclosure rules to ensure that analytic output from the Data Enclave meet disclosure requirements.