In the era of Fourth Industrial Revolution, increasing demand for data collection and sharing analysis purposes has raised concerns regarding privacy violations. Protecting individual during dissemination sensitive information emerged as a critical concern. this paper, we propose privacy-preserving framework collecting users’ medical microdata, utilizing geo-indistinguishability (Geo-I), concep...

The web is transforming from traditional web to web of data, where information is presented in such a way that it is readable by machines as well as human. As a part of this transformation, every day more and more websites implant structured data, e.g. product, person, organization, place etc., into the HTML pages. To implant the structured data different encoding vocabularies, such as RDFa, mi...

There is an ever increasing demand from researchers for access to useful microdata files. However, there are also growing concerns regarding the privacy of the individuals contained in the microdata. Ideally, microdata could be released in such a way that a balance between usefulness of the data and privacy is struck. This paper presents a review of proposed methods of statistical disclosure co...

Inference control in databases, also known as Statistical Disclosure Control (SDC), is about protecting data so they can be published without revealing confidential information that can be linked to specific individuals among those to which the data correspond. This is an important application in several areas, such as official statistics, health statistics, e-commerce (sharing of consumer data...

When tables of quantitative data are generated from a datafile, the release of those tables should not reveal information concerning individual respondents. This disclosure of individual respondents in the microdata file can be prevented by applying disclosure control methods at the table level, but this may create inconsistencies across tables. Alternatively, disclosure control methods can be ...

Publishing individual specific microdata has serious privacy implications. The k-anonymity model has been proposed to prevent identity disclosure from microdata, and the work on -diversity and t-closeness attempt to address attribute disclosure. However, most current work only deal with publishing microdata with a single sensitive attribute (SA), whereas real life scenarios often involve microd...

When releasing data to the public, data disseminators typically are required to protect the confidentiality of survey respondents’ identities and attribute values. Removing direct identifiers such as names and addresses generally is not sufficient to eliminate disclosure risks, so that data must be altered before release to limit the risks of unintended disclosures. When intense data alteration...

Disclaimer: This report is released to inform interested parties of ongoing research and to encourage discussion of work in progress. The views expressed are those of the author and not necessarily those of the U.S. Census Bureau. A public-use microdata file should be analytically valid. For a very small number of uses, the microdata should yield analytic results that are approximately the same...

We present in this paper the first empirical comparison of SDC methods for microdata which encompasses both continuous and categorical microdata. Based on re-identification experiments, we try to optimize the tradeoff between information loss and disclosure risk. First, relevant SDC methods for continuous and categorical microdata are identified. Then generic information loss measures (not targ...