Students and Taxes: a Privacy-Preserving Social Study Using Secure Computation
Students and Taxes: a Privacy-Preserving Social Study Using Secure Computation
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学生和税收：使用安全计算的隐私保护社会研究
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
Riivo Talviste
中科院分区：
文献类型：
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作者：
D. Bogdanov;Liina Kamm;Baldur Kubo;Reimo Rebane;Ville Sokk;Riivo Talviste
我们描述了使用安全多方计算进行大规模的隐私保护统计研究的真实的政府数据。2015年，爱沙尼亚应用研究中心（CentAR）的统计学家进行了一项大数据研究，以寻找在大学学习期间工作与未能及时毕业之间的相关性。这项研究是通过将爱沙尼亚税务和海关委员会的个人纳税数据库与教育和研究部的高等教育活动数据库联系起来进行的。使用为分析提供端到端加密保护的Sharemind安全多方计算系统进行数据收集、准备和分析。在分析中使用了1000万份税务记录和50万份教育记录，这是有史以来最大的加密私人统计研究。
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