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| Version | Summary | Created by | Modification | Content Size | Created at | Operation |
|---|---|---|---|---|---|---|
| 1 | Dean Liu | -- | 1740 | 2022-10-28 01:39:53 |
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Data Re-Identification is the practice of matching anonymous data (also known as de-identified data) with publicly available information, or auxiliary data, in order to discover the individual to which the data belongs to. This is a concern because companies with privacy policies, health care providers, and financial institutions may release the data they collect after the data has gone through the de-identification process. The de-identification process involves masking, generalizing or deleting both direct and indirect identifiers; the definition of this process is not universal, however. Information in the public domain, even seemingly anonymized, may thus be re-identified in combination with other pieces of available data and basic computer science techniques. The Common Rule Agencies, a collection of multiple U.S. federal agencies and departments including the U.S. Department of Health and Human Services, speculate that re-identification is becoming gradually easier because of "big data" - the abundance and constant collection and analysis of information along the evolution of technologies and the advances of algorithms. However, others have claimed that de-identification is a safe and effective data liberation tool and do not view re-identification as a concern. A 2000 study found that 87 percent of the U.S. population can be identified using a combination of their gender, birthdate and zip code. Others do not think that re-identification is a serious threat, and call it a "myth"; they claim that the combination of zip code, date of birth and gender is rare or partially complete, such as only the year and month birth without the date, or the county name instead of the specific zip code, thus the risk of such re-identification is reduced in many instances.
References
- Porter, Christine. 2008 "CONSTITUTIONAL AND REGULATORY: De-Identified Data and Third Party Data Mining: The Risk of Re-Identification of Personal Information." University of Washington Shidler Journal of Law, Commerce & Technology. Retrieved March 26, 2017.
- Peltz, Richard. 2009. "Beyond the Final Frontier: A "Post-Racial" America?: The Responsibilities of Citizens: From the Ivory Tower to the Glass House: Access to "De-Identified" Public University Admission Records to Study Affirmative Action." Harvard Journal on Racial and Ethic Justice. Retrieved March 26, 2017.
- Hoffman, Sharona. 2015. "Citizen Science: The Law and Ethics of Public Access to Medical Big Data." Berkeley Technology Law Journal. Retrieved March 26, 2017.
- Greenberg, Yelena. 2016. "RECENT CASE DEVELOPMENTS: Increasing Recognition of "Risk of Harm" as an Injury Sufficient to Warrant Standing in Class Action Medical Data Breach Cases." American Journal of Law & Medicine. Retrieved March 26, 2017.
- Groden, Samantha, Summer Martin, and Rebecca Merrill. 2016. "Proposed Changes to the Common Rule: A Standoff Between Patient Rights and Scientific Advances?" Journal of Health & Life Sciences Law. Retrieved March 26, 2017.
- 24 C.F.R. § .104 2017.
- Ohm, Paul. 2010. “Broken Promises of Privacy: Responding to the Surprising Failure of Anonymization.” UCLA Law Review. Retrieved March 26, 2017.
- Sweeney L. Only You, Your Doctor and Many Others May Know. Technology Science. 2015092903. September 25, 2015.
- ibid.
- Lagos, Yianni. 2014. "SYMPOSIUM: TAKING THE PERSONAL OUT OF DATA: MAKING SENSE OF DE-IDENTIFICATION." Indiana Law Review. Retrieved March 26, 2017.
- Ahn, Sejin. 2015. “COMMENT: Whose Genome Is It Anyway?: Re-identification and Privacy Protection in Public and Participatory Genomics.” San Diego Law Review. Retrieved March 26, 2017.
- Rubinstein, Ira S, and Hartzog, Woodrow. 2016. “Anonymization and Risk” Washington Law Review. Retrieved March 26, 2017.
