De-identification policy and risk distribution framework for securing personal information.

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Title: De-identification policy and risk distribution framework for securing personal information.
Authors: Joo, Moon-Ho1, Yoon, Sang-Pil1, Kwon, Hun-Yeong1 khy0@korea.ac.kr., Lim, Jong-In1, Chun, Soon Ae, Adam, Nabil R., Noveck, Beth
Source: Information Polity: The International Journal of Government & Democracy in the Information Age. 2018, Vol. 23 Issue 2, p195-219. 25p. 3 Diagrams, 5 Charts.
Subject Terms: *Electronic data processing, Big data, Data mining, Personal information management, Privacy
Abstract: In the age of big data, many countries are implementing and establishing de-identification policies quite actively. There are many efforts to institutionalize de-identification of personal information to protect privacy and utilize the use of personal information. But even with such efforts, de-identification policy always has a potential risk that de-identified information can be re-identified by being combined with other information. Therefore, it is necessary to consider the management mechanism that manages these risks as well as a mechanism for distributing the responsibilities and liabilities in the event of incidents involving the invasion of privacy. So far, most countries implementing the de-identification policies are focusing on defining what de-identification is and the exemption requirements to allow free use of de-identified personal information. On the other hand, there is a lack of discussion and consideration on how to distribute the responsibility of the risks and liabilities involved in the process of de-identification of personal information. The purpose of this study is to compare the de-identification policies of the European Union, the United States, Japan, and Korea, all of which are now actively pursuing de-identification policies. Additionally, this study proposes to take a look at the various de-identification policies worldwide and contemplate on these policies in the perspective of risk society and risk-liability theory. The constituencies of the de-identification policies are identified in order to analyze the roles and responsibilities of each of these constituencies thereby providing the theoretical basis on which to initiate the discussions on the distribution of burden and responsibilities arising from the de-identification policies. [ABSTRACT FROM AUTHOR]
Copyright of Information Polity: The International Journal of Government & Democracy in the Information Age is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: De-identification policy and risk distribution framework for securing personal information.
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  Data: <searchLink fieldCode="JN" term="%22Information+Polity%3A+The+International+Journal+of+Government+%26+Democracy+in+the+Information+Age%22">Information Polity: The International Journal of Government & Democracy in the Information Age</searchLink>. 2018, Vol. 23 Issue 2, p195-219. 25p. 3 Diagrams, 5 Charts.
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  Data: *<searchLink fieldCode="DE" term="%22Electronic+data+processing%22">Electronic data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Big+data%22">Big data</searchLink><br /><searchLink fieldCode="DE" term="%22Data+mining%22">Data mining</searchLink><br /><searchLink fieldCode="DE" term="%22Personal+information+management%22">Personal information management</searchLink><br /><searchLink fieldCode="DE" term="%22Privacy%22">Privacy</searchLink>
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  Data: In the age of big data, many countries are implementing and establishing de-identification policies quite actively. There are many efforts to institutionalize de-identification of personal information to protect privacy and utilize the use of personal information. But even with such efforts, de-identification policy always has a potential risk that de-identified information can be re-identified by being combined with other information. Therefore, it is necessary to consider the management mechanism that manages these risks as well as a mechanism for distributing the responsibilities and liabilities in the event of incidents involving the invasion of privacy. So far, most countries implementing the de-identification policies are focusing on defining what de-identification is and the exemption requirements to allow free use of de-identified personal information. On the other hand, there is a lack of discussion and consideration on how to distribute the responsibility of the risks and liabilities involved in the process of de-identification of personal information. The purpose of this study is to compare the de-identification policies of the European Union, the United States, Japan, and Korea, all of which are now actively pursuing de-identification policies. Additionally, this study proposes to take a look at the various de-identification policies worldwide and contemplate on these policies in the perspective of risk society and risk-liability theory. The constituencies of the de-identification policies are identified in order to analyze the roles and responsibilities of each of these constituencies thereby providing the theoretical basis on which to initiate the discussions on the distribution of burden and responsibilities arising from the de-identification policies. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Information Polity: The International Journal of Government & Democracy in the Information Age is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.3233/IP-170057
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        Text: English
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      – SubjectFull: Big data
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      – SubjectFull: Data mining
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      – SubjectFull: Personal information management
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      – SubjectFull: Privacy
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      – TitleFull: De-identification policy and risk distribution framework for securing personal information.
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              Text: 2018
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