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The Challenge of Privacy Protection for Statistical Agencies

Presented by: 
John Abowd U.S. Census Bureau, Cornell University
Wednesday 6th July 2016 - 14:30 to 15:30
INI Seminar Room 1
Since the field of statistical disclosure limitation (SDL) was first formalized by Ivan Fellegi in 1972, official statistical agencies have recognized that their publications posed confidentiality risks for the households and businesses who responded. For even longer, agencies have protected the source data for those publications by using secure storage methods and access authorization systems. In SDL, Dalenius (1977) and, in computer science, Goldwasser and Micali (1982) formalized what has become the modern approach to privacy protection in data publication: inferential disclosure limitation/semantic security. The modern approach to physical data security centers on firewall and encryption technologies. And the two sets of risks (disclosure through publication and disclosure through unauthorized access) have become increasingly inter-related. It is important to recognize the distinct issues, however. Secure multiparty computing and the stronger fully homomorphic encryption are formal solutions to the problem of permitting statistical computations without granting access to the decrypted data. Privacy-protected query publication is a formal solution to the problem of insuring that inferential disclosures are bounded and that the bound is respected in all published tables. There are now tractable systems that combine secure multi-party computing with formal privacy protection of the computed statistics (e.g., Shokri and Shmatikov 2015). The challenge to statistical agencies is to learn how these systems work, and move their own protection technologies in this direction. Private companies like Google and Microsoft already do this. Statistical agencies must be prepared to explain the differences in their publication requirements and security protocols that distinguish their chosen data storage methods and publications from those used by private companies.

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University of Cambridge Research Councils UK
    Clay Mathematics Institute London Mathematical Society NM Rothschild and Sons