Semantic Disclosure Control:
semantics meets data privacy
Internet Interdisciplinary Institute (IN3), Universitat Oberta de Catalunya,
Barcelona, Spain, and
Department of Computer Science and Mathematics,
CYBERCAT –Center for Cybersecurity Research of Catalonia,
Universitat Rovira i Virgili, UNESCO Chair in Data Privacy, Tarragona, Spain
Purpose –To 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/approach –The 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.
Findings –So 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 implications –SeDC 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 implications –Individuals 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/value –On 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.
Keywords Semantics, Knowledge, Privacy, Personal data protection
Paper type Conceptual paper
In the current context of information societies, it is quite common to refer to electronic data as
“the new oil”of the twenty-first century (Rotella, April 2, 2012). On the one hand, the analysis of
personal data fuels many research efforts (e.g. the analysis of medical records is essential to
improve healthcare delivery). On the other hand, predictive market analytics derive value from
the huge amount of personal data being gathered; for example, the compilation, aggregation and
exploitation of data (e.g. social media) related to millions of internet users isa billionaire business
in which Data Brokers are the main providers of data and services, which include identity
verification, marketing products, personal profiling, etc. (US Federal Trade Commission, 2014).
Even though there is no question that those services are of great interest for companies and
consumers, at the same time, the confidential nature of many of the compiled data (e.g. census
data gathered from government sources, personal opinions and preferences posted in social
networks, medical records, etc.) may pose privacy risks to the subjects whom data refer to.
In order to guarantee the fundamental right to privacy of the individuals (The European
Parliament and the Council of the EU, 2016), responsible parties should undertake
Online Information Review
Vol. 42 No. 3, 2018
© Emerald PublishingLimited
Received 20 March 2017
Revised 13 June 2017
Accepted 30 August 2017
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