Cryptology ePrint Archive: Report 2015/1159
Students and Taxes: a Privacy-Preserving Social Study Using Secure Computation
Dan Bogdanov, Liina Kamm, Baldur Kubo, Reimo Rebane, Ville Sokk, Riivo Talviste
Abstract: We describe the use of secure multi-party computation for performing a large-scale privacy-preserving 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.
Category / Keywords: applications / secure multi-party computation, statistics, real-world, application
Original Publication (in the same form): Proceedings on Privacy Enhancing Technologies (PoPETs)
Date: received 30 Nov 2015, last revised 27 Feb 2016
Contact author: liina at cyber ee
Available format(s): PDF | BibTeX Citation
Note: Minor changes and clarifications to the text.
Version: 20160227:134434 (All versions of this report)
Short URL: ia.cr/2015/1159
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