Microfiles as a Potential Source of Confidential Information Leakage

Chertov, Оleg; Tavrov, Dan

doi:10.1007/978-3-319-08624-8_4

Cited by 6 publications

(10 citation statements)

References 12 publications

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“…E. g., if "Place of Work" is selected as a parameter attribute, and "Military Service" is selected as a vital one, then removing the latter one will hypothetically disable detecting outliers that correspond to sites of military bases. However, as was shown before [7], this approach satisfies only the third condition. The first two conditions will not be satisfied in general, because it is sometimes possible to build a model of a group that takes into consideration values of other core microfile attributes (such as "Age," "Sex," etc.…”

Section: Literature Review and Problem Statementmentioning

confidence: 95%

Improving efficiency of providing data group anonymity by automating data modification quality evaluation

Chertov

Tavrov

2017

EEJET

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Section: Literature Review and Problem Statementmentioning

confidence: 95%

Improving efficiency of providing data group anonymity by automating data modification quality evaluation

Chertov

Tavrov

2017

EEJET

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“…Taking into consideration uncertain and imprecise nature of statistical datasets, it was proposed in Chertov and Tavrov ( 2015 ) to violate group anonymity with the help of a fuzzy model of a group.…”

Section: Related Workmentioning

confidence: 99%

“…This method enables us to modify the quantity (or concentration) signal in order to mask its outliers, and at the same time tries to minimize distortion introduced in the dataset. In Tavrov ( 2015 ), this algorithm was adapted to work with the fuzzy models proposed in Chertov and Tavrov ( 2015 ).…”

Section: Related Workmentioning

confidence: 99%

“…In Chertov and Tavrov ( 2015 ), there has been proposed an expert-based procedure for building fuzzy model of a given group to be protected in a form of a fuzzy inference system (Klir and Yuan 1995 ), i.e., a system which employs expert knowledge in the form of fuzzy rules for making inferences. Such a fuzzy model can be then thought of as a fuzzy classifier that assigns to a given respondent a certain grade of membership in the group.…”

Section: Related Workmentioning

confidence: 99%

“…However, we can try to restore group distributions by analyzing publicly available data such as statistical surveys, polls etc. (Chertov and Tavrov 2015 ). Using expert judgments about these data, we can build a fuzzy model of a group in a form of a fuzzy inference system (FIS) that, for each respondent, gives her membership grade in the group under consideration.…”

Section: Introductionmentioning

confidence: 97%

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Evolutionary approach to violating group anonymity using third-party data

Tavrov

Chertov

2016

SpringerPlus

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In the era of Big Data, it is almost impossible to completely restrict access to primary non-aggregated statistical data. However, risk of violating privacy of individual respondents and groups of respondents by analyzing primary data has not been reduced. There is a need in developing subtler methods of data protection to come to grips with these challenges. In some cases, individual and group privacy can be easily violated, because the primary data contain attributes that uniquely identify individuals and groups thereof. Removing such attributes from the dataset is a crude solution and does not guarantee complete privacy. In the field of providing individual data anonymity, this problem has been widely recognized, and various methods have been proposed to solve it. In the current work, we demonstrate that it is possible to violate group anonymity as well, even if those attributes that uniquely identify the group are removed. As it turns out, it is possible to use third-party data to build a fuzzy model of a group. Typically, such a model comes in a form of a set of fuzzy rules, which can be used to determine membership grades of respondents in the group with a level of certainty sufficient to violate group anonymity. In the work, we introduce an evolutionary computing based method to build such a model. We also discuss a memetic approach to protecting the data from group anonymity violation in this case.

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