Privacy-Preserving Data Analysis for the Federal Statistical Agencies
| Autor: | Abowd, John, Alvisi, Lorenzo, Dwork, Cynthia, Kannan, Sampath, Machanavajjhala, Ashwin, Reiter, Jerome |
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| Rok vydání: | 2017 |
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| Druh dokumentu: | Working Paper |
| Popis: | Government statistical agencies collect enormously valuable data on the nation's population and business activities. Wide access to these data enables evidence-based policy making, supports new research that improves society, facilitates training for students in data science, and provides resources for the public to better understand and participate in their society. These data also affect the private sector. For example, the Employment Situation in the United States, published by the Bureau of Labor Statistics, moves markets. Nonetheless, government agencies are under increasing pressure to limit access to data because of a growing understanding of the threats to data privacy and confidentiality. "De-identification" - stripping obvious identifiers like names, addresses, and identification numbers - has been found inadequate in the face of modern computational and informational resources. Unfortunately, the problem extends even to the release of aggregate data statistics. This counter-intuitive phenomenon has come to be known as the Fundamental Law of Information Recovery. It says that overly accurate estimates of too many statistics can completely destroy privacy. One may think of this as death by a thousand cuts. Every statistic computed from a data set leaks a small amount of information about each member of the data set - a tiny cut. This is true even if the exact value of the statistic is distorted a bit in order to preserve privacy. But while each statistical release is an almost harmless little cut in terms of privacy risk for any individual, the cumulative effect can be to completely compromise the privacy of some individuals. Comment: A Computing Community Consortium (CCC) white paper, 7 pages |
| Databáze: | arXiv |
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