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Semantic Disclosure Control: semantics meets data privacy

Semantic Disclosure Control: semantics meets data privacy PurposeTo overcome the limitations of purely statistical approaches to data protection, the purpose of this paper is to propose Semantic Disclosure Control (SeDC): an inherently semantic privacy protection paradigm that, by relying on state of the art semantic technologies, rethinks privacy and data protection in terms of the meaning of the data.Design/methodology/approachThe need for data protection mechanisms able to manage data from a semantic perspective is discussed and the limitations of statistical approaches are highlighted. Then, SeDC is presented by detailing how it can be enforced to detect and protect sensitive data.FindingsSo far, data privacy has been tackled from a statistical perspective; that is, available solutions focus just on the distribution of the data values. This contrasts with the semantic way by which humans understand and manage (sensitive) data. As a result, current solutions present limitations both in preventing disclosure risks and in preserving the semantics (utility) of the protected data.Practical implicationsSeDC captures more general, realistic and intuitive notions of privacy and information disclosure than purely statistical methods. As a result, it is better suited to protect heterogenous and unstructured data, which are the most common in current data release scenarios. Moreover, SeDC preserves the semantics of the protected data better than statistical approaches, which is crucial when using protected data for research.Social implicationsIndividuals are increasingly aware of the privacy threats that the uncontrolled collection and exploitation of their personal data may produce. In this respect, SeDC offers an intuitive notion of privacy protection that users can easily understand. It also naturally captures the (non-quantitative) privacy notions stated in current legislations on personal data protection.Originality/valueOn the contrary to statistical approaches to data protection, SeDC assesses disclosure risks and enforces data protection from a semantic perspective. As a result, it offers more general, intuitive, robust and utility-preserving protection of data, regardless their type and structure. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Online Information Review Emerald Publishing

Semantic Disclosure Control: semantics meets data privacy

Online Information Review , Volume 42 (3): 14 – Jun 11, 2018

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References (29)

Publisher
Emerald Publishing
Copyright
Copyright © Emerald Group Publishing Limited
ISSN
1468-4527
DOI
10.1108/OIR-03-2017-0090
Publisher site
See Article on Publisher Site

Abstract

PurposeTo overcome the limitations of purely statistical approaches to data protection, the purpose of this paper is to propose Semantic Disclosure Control (SeDC): an inherently semantic privacy protection paradigm that, by relying on state of the art semantic technologies, rethinks privacy and data protection in terms of the meaning of the data.Design/methodology/approachThe need for data protection mechanisms able to manage data from a semantic perspective is discussed and the limitations of statistical approaches are highlighted. Then, SeDC is presented by detailing how it can be enforced to detect and protect sensitive data.FindingsSo far, data privacy has been tackled from a statistical perspective; that is, available solutions focus just on the distribution of the data values. This contrasts with the semantic way by which humans understand and manage (sensitive) data. As a result, current solutions present limitations both in preventing disclosure risks and in preserving the semantics (utility) of the protected data.Practical implicationsSeDC captures more general, realistic and intuitive notions of privacy and information disclosure than purely statistical methods. As a result, it is better suited to protect heterogenous and unstructured data, which are the most common in current data release scenarios. Moreover, SeDC preserves the semantics of the protected data better than statistical approaches, which is crucial when using protected data for research.Social implicationsIndividuals are increasingly aware of the privacy threats that the uncontrolled collection and exploitation of their personal data may produce. In this respect, SeDC offers an intuitive notion of privacy protection that users can easily understand. It also naturally captures the (non-quantitative) privacy notions stated in current legislations on personal data protection.Originality/valueOn the contrary to statistical approaches to data protection, SeDC assesses disclosure risks and enforces data protection from a semantic perspective. As a result, it offers more general, intuitive, robust and utility-preserving protection of data, regardless their type and structure.

Journal

Online Information ReviewEmerald Publishing

Published: Jun 11, 2018

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