“Quasi-identifier”的意思、由来-开放百科全书

Quasi-identifiers can thus, when combined, become personally identifying information. This process is called re-identification. As an example, Latanya Sweeney has shown that even though neither gender, birth dates nor postal codes uniquely identify an individual, the combination of all three is sufficient to identify 87% of individuals in the United States.^[2]

The term was introduced by Tore Dalenius in 1986.^[3] Since then, quasi-identifiers have been the basis of several attacks on released data. For instance, Sweeney linked health records to publicly available information to locate the then-governor of Massachusetts' hospital records using uniquely identifying quasi-identifiers,^[4]^[5] and Sweeney, Abu and Winn used public voter records to re-identify participants in the Personal Genome Project.^[6] Additionally, Arvind Narayanan and Vitaly Shmatikov made use of quasi-identifiers to de-anonymize data released by Netflix.^[7]

Motwani and Ying warn about potential privacy breaches being enabled by publication of large volumes of government and business data containing quasi-identifiers.^[8]

See also

References

1. ^{{cite web|url=http://stats.oecd.org/glossary/detail.asp?ID=6961|title=Glossary of Statistical Terms: Quasi-identifier|publisher=OECD|date=November 10, 2005|accessdate=29 September 2013}}
2. ^Sweeney, Latanya. Simple demographics often identify people uniquely. Carnegie Mellon University, 2000. http://dataprivacylab.org/projects/identifiability/paper1.pdf
3. ^Dalenius, Tore. Finding a Needle In a Haystack or Identifying Anonymous Census Records. Journal of Official Statistics, Vol.2, No.3, 1986. pp. 329–336. http://www.jos.nu/Articles/abstract.asp?article=23329
4. ^Anderson, Nate. ''Anonymized data really isn’t—and here’s why not. Ars Technica, 2009. https://arstechnica.com/tech-policy/2009/09/your-secrets-live-online-in-databases-of-ruin/
5. ^Barth-Jones, Daniel C. The're-identification'of Governor William Weld's medical information: a critical re-examination of health data identification risks and privacy protections, then and now. Then and Now (June 4, 2012) (2012).
6. ^ Sweeney, Latanya, Akua Abu, and Julia Winn. "Identifying participants in the personal genome project by name." Available at SSRN 2257732 (2013).
7. ^Narayanan, Arvind and Shmatikov, Vitaly. Robust De-anonymization of Large Sparse Datasets. The University of Texas at Austin, 2008. https://www.cs.utexas.edu/~shmat/shmat_oak08netflix.pdf
8. ^{{cite conference |title=Efficient Algorithms for Masking and Finding Quasi-Identifiers |author=Rajeev Motwani and Ying Xu |conference=Proceedings of SDM’08 International Workshop on Practical Privacy-Preserving Data Mining |url=https://www.csee.umbc.edu/~kunliu1/p3dm08/proceedings/2.pdf |year=2008}}