Paper 2015/1159

Students and Taxes: a Privacy-Preserving Social Study Using Secure Computation

Dan Bogdanov, Liina Kamm, Baldur Kubo, Reimo Rebane, Ville Sokk, and Riivo Talviste


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.

Note: Minor changes and clarifications to the text.

Available format(s)
Publication info
Published elsewhere. Proceedings on Privacy Enhancing Technologies (PoPETs)
secure multi-party computationstatisticsreal-worldapplication
Contact author(s)
liina @ cyber ee
2016-02-27: revised
2015-12-02: received
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      author = {Dan Bogdanov and Liina Kamm and Baldur Kubo and Reimo Rebane and Ville Sokk and Riivo Talviste},
      title = {Students and Taxes: a Privacy-Preserving Social Study Using Secure Computation},
      howpublished = {Cryptology ePrint Archive, Paper 2015/1159},
      year = {2015},
      doi = {10.1515/popets-2016-0019},
      note = {\url{}},
      url = {}
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