Query-Based PUF for Disclosure-Safe Remote Analysis from Medicare Claims Microdata

• Activity Number: 638
• Type: Topic Contributed
• Date/Time: Thursday, August 8, 2013 : 8:30 AM to 10:20 AM
• Sponsor: Survey Research Methods Section
• Author(s): Avi Singh and Joshua M. Borton*+ and Micheal Davern and A. T-C. Yu
• Companies: NORC at the University of Chicago and NORC at the University of Chicago and NORC and NORC at the University of Chicago
• Keywords: Differencing Attacks ; Remote Analysis Servers ; Input vs. Output Disclosure Treatment ; Checklist of Pre-screened Analytic Variables

Abstract

Remote analysis servers (RAS) with output treatment of user queries, as an alternative to traditional input-treated PUFs, is an active research area. Success of RAS depends on protection from differencing attacks via repeated queries. We describe a new application of a recently proposed method of query-based (Q) PUF to Medicare claims data which is safe from differencing attacks. In Q-PUF, the user cannot arbitrarily define analysis domains but is required to choose from a checklist of pre-screened variables. The method is termed Q-PUF, despite being an output treatment, because the data producer controls the type and scope of allowed analytic variables just as with PUFs. There are three components of Q-PUF: first, construct a checklist of analysis domains that are deemed adequately large to provide estimating functions of corresponding parameters and safe for analysis; second, provide an interface for users to communicate with the microdata via queries; third, additional restrictions specific to analysis output. If needed the domain checklist may be updated. For Medicare claims data, we provide empirical examples of common interest including descriptive and model-based inference.