Students and Taxes: a Privacy-Preserving Study Using Secure Computation
Authors: Dan Bogdanov (Cybernetica, Estonia.), Liina Kamm (Cybernetica, Estonia.), Baldur Kubo (Cybernetica, Estonia.), Reimo Rebane (Cybernetica, Estonia.), Ville Sokk (Cybernetica, Estonia.), Riivo Talviste (Cybernetica, Estonia and University of Tartu, Estonia.)
Volume: 2016
Issue: 3
Pages: 117–135
DOI: https://doi.org/10.1515/popets-2016-0019
Abstract: We describe the use of secure multi-party computation for performing a large-scale privacypreserving statistical study on real government data. In 2015, statisticians from the Estonian Center of Applied Research (CentAR) conducted a big data study to look for correlations between working during university studies and failing to graduate in time. The study was conducted by linking the database of individual tax payments from the Estonian Tax and Customs Board and the database of higher education events from the Ministry of Education and Research. Data collection, preparation and analysis were conducted using the Sharemind secure multi-party computation system that provided end-to-end cryptographic protection to the analysis. Using ten million tax records and half a million education records in the analysis, this is the largest cryptographically private statistical study ever conducted on real data.
Keywords: privacy, statistics, secure multi-party computation, case study
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