Limiting Data Exposure in Multi-Label Classification Processes

• Autorzy: Anciaux N. , Boutara D. , Nguyen B. , Vazirgiannis M.

Identyfikatory

DOI

10.3233/FI-2015-1176

• Języki publikacji: EN

Abstrakty

EN

Administrative services such social care, tax reduction, and many others using complex decision processes, request individuals to provide large amounts of private data items, in order to calibrate their proposal to the specific situation of the applicant. This data is subsequently processed and stored by the organization. However, all the requested information is not needed to reach the same decision. We have recently proposed an approach, termed Minimum Exposure, to reduce the quantity of information provided by the users, in order to protect her privacy, reduce processing costs for the organization, and financial lost in the case of a data breach. In this paper, we address the case of decision making processes based on sets of classifiers, typically multi-label classifiers. We propose a practical implementation using state of the art multi-label classifiers, and analyze the effectiveness of our solution on several real multi-label data sets.

Słowa kluczowe

EN

privacy; online forms; overdata disclosure; limited data collection; multilabel classification

• Wydawca: Polskie Towarzystwo Matematyczne
• Czasopismo: Fundamenta Informaticae
• Rocznik: 2015
• Tom: Vol. 137, nr 2
• Strony: 219--236
• Opis fizyczny: Bibliogr. 32 poz., rys., tab.

Twórcy

autor

Anciaux N.

• INRIA and University of Versailles SMIS team Domaine de Voluceau 78153 Le Chesnay, France

autor

Boutara D.

• INRIA and Ecole Polytechnique Laboratoire d’Informatique de l’Ecole Polytechnique 91128, Palaiseau, France

autor

Nguyen B.

• INRIA and University of Versailles SMIS team Domaine de Voluceau 78153 Le Chesnay, France

autor

Vazirgiannis M.

• Ecole Polytechnique and Athens University of Economics and Business Laboratoire d’Informatique de l’Ecole Polytechnique 91128, Palaiseau, France

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