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Reasoning under uncertainty and multi-criteria decision making in data privacy

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Abstract

By means of an integration of decision theory and probabilistic models, we explore and develop methods for improving data privacy. Our work encompasses disclosure control tools in statistical databases and privacy requirements prioritization; in particular we propose a Bayesian approach for the on-line auditing in Statistical Databases and Pairwise Comparison Matrices for privacy requirements prioritization. The first approach is illustrated by means of examples in the context of statistical analysis on the census and medical data, where no salary (resp. no medical information), that could be related to a specific employee (resp. patient), must be released; the second approach is illustrated by means of examples, such as an e-voting system and an e-banking service that have to satisfy privacy requirements in addition to functional and security ones. Several fields in the social sciences, economics and engineering will benefit from the advances in this research area: e-voting, e-government, e-commerce, e-banking, e-health, cloud computing and risk management are a few examples of applications for the findings of this research.

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Authors and Affiliations

  1. Department of Architecture, University of Naples “Federico II”, Via Toledo 402, 80134 , Naples, Italy

    Bice Cavallo & Livia D’Apuzzo

  2. Department of Engineering, University of Sannio, Viale Traiano 1, 82100 , Benevento, Italy

    Gerardo Canfora

  3. Department of Economic, Legal and Social Studies, University of Sannio, Via delle Puglie 82, 82100 , Benevento, Italy

    Massimo Squillante

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  1. Bice Cavallo
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  2. Gerardo Canfora
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  3. Livia D’Apuzzo
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  4. Massimo Squillante
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Correspondence to Bice Cavallo.

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Cavallo, B., Canfora, G., D’Apuzzo, L. et al. Reasoning under uncertainty and multi-criteria decision making in data privacy. Qual Quant 48, 1957–1972 (2014). https://doi.org/10.1007/s11135-013-9859-8

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