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Generalization-based privacy preservation and discrimination prevention in data publishing and mining

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Abstract

Living in the information society facilitates the automatic collection of huge amounts of data on individuals, organizations, etc. Publishing such data for secondary analysis (e.g. learning models and finding patterns) may be extremely useful to policy makers, planners, marketing analysts, researchers and others. Yet, data publishing and mining do not come without dangers, namely privacy invasion and also potential discrimination of the individuals whose data are published. Discrimination may ensue from training data mining models (e.g. classifiers) on data which are biased against certain protected groups (ethnicity, gender, political preferences, etc.). The objective of this paper is to describe how to obtain data sets for publication that are: (i) privacy-preserving; (ii) unbiased regarding discrimination; and (iii) as useful as possible for learning models and finding patterns. We present the first generalization-based approach to simultaneously offer privacy preservation and discrimination prevention. We formally define the problem, give an optimal algorithm to tackle it and evaluate the algorithm in terms of both general and specific data analysis metrics (i.e. various types of classifiers and rule induction algorithms). It turns out that the impact of our transformation on the quality of data is the same or only slightly higher than the impact of achieving just privacy preservation. In addition, we show how to extend our approach to different privacy models and anti-discrimination legal concepts.

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Notes

  1. The use of PD (resp., PND) attributes in decision making does not necessarily lead to (or exclude) discriminatory decisions (Ruggieri et al. 2010).

  2. In full-domain generalization if a value is generalized, all its instances are generalized. There are alternative generalization schemes, such as multi-dimensional generalization or cell generalization, in which some instances of a value may remain ungeneralized while other instances are generalized.

  3. Although algorithms using multi-dimensional or cell generalizations (e.g. the Mondrian algorithm, Lefevre et al. 2006) cause less information loss than algorithms using full-domain generalization, the former suffer from the problem of data exploration (Fung et al. 2010). This problem is caused by the co-existence of specific and generalized values in the generalized data set, which make data exploration and interpretation difficult for the data analyst.

  4. On the legal side, different measures are adopted worldwide; see Pedreschi et al. (2013) for parallels between different measures and anti-discrimination acts.

  5. Discrimination occurs when a group is treated “less favorably” than others.

  6. Discrimination of a group occurs when a higher proportion of people not in the group is able to comply with a qualifying criterion.

  7. \(\alpha \) states an acceptable level of discrimination according to laws and regulations. For example, the U.S. Equal Pay Act (United States Congress 1963) states that “a selection rate for any race, sex, or ethnic group which is less than four-fifths of the rate for the group with the highest rate will generally be regarded as evidence of adverse impact”. This amounts to using clift with \(\alpha =1.25\).

  8. http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=CELEX:62011CJ0385:EN:Not

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Acknowledgments

The authors wish to thank Kristen LeFevre for providing the implementation of the Incognito algorithm and Guillem Rufian-Torrell for helping in the implementation of the algorithm proposed in this paper. This work was partly supported by the Government of Catalonia under Grant 2009 SGR 1135, by the Spanish Government through projects TIN2011-27076-C03-01 “CO-PRIVACY”, TIN2012-32757 “ICWT” and CONSOLIDER INGENIO 2010 CSD2007-00004 “ARES”, and by the European Comission under FP7 projects “DwB” and “INTER-TRUST”. The second author is partially supported as an ICREA Acadèmia researcher by the Government of Catalonia. The authors are with the UNESCO Chair in Data Privacy, but they are solely responsible for the views expressed in this paper, which do not necessarily reflect the position of UNESCO nor commit that organization.

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  1. Department of Computer Engineering and Maths, UNESCO Chair in Data Privacy, Universitat Rovira i Virgili, Av. Països Catalans 26, 43007, Tarragona, Catalonia

    Sara Hajian, Josep Domingo-Ferrer & Oriol Farràs

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  1. Sara Hajian
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  2. Josep Domingo-Ferrer
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  3. Oriol Farràs
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Correspondence to Sara Hajian.

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Responsible editor: Guest Editors of PKDD 2014 (Dr. Toon Calders, Prof. Floriana Esposito, Prof. Eyke Hüllermeier and Dr. Rosa Meo).

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Hajian, S., Domingo-Ferrer, J. & Farràs, O. Generalization-based privacy preservation and discrimination prevention in data publishing and mining. Data Min Knowl Disc 28, 1158–1188 (2014). https://doi.org/10.1007/s10618-014-0346-1

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