Students and Taxes: a Privacy-Preserving Study Using Secure Computation
| Autor: | Liina Kamm, Reimo Rebane, Dan Bogdanov, Ville Sokk, Baldur Kubo, Riivo Talviste |
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| Rok vydání: | 2016 |
| Předmět: |
Ethics
secure multi-party computation 021110 strategic defence & security studies Multimedia Computer science business.industry 0211 other engineering and technologies Information technology QA75.5-76.95 02 engineering and technology privacy BJ1-1725 computer.software_genre Privacy preserving case study statistics Electronic computers. Computer science 0202 electrical engineering electronic engineering information engineering Secure multi-party computation General Earth and Planetary Sciences 020201 artificial intelligence & image processing business computer General Environmental Science |
| Zdroj: | Proceedings on Privacy Enhancing Technologies, Vol 2016, Iss 3, Pp 117-135 (2016) |
| ISSN: | 2299-0984 |
| Popis: | 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 Share-mind 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. |
| Databáze: | OpenAIRE |
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