Retail data are one of the most requested commodities by commercial companies. Unfortunately, from this data it is possible to retrieve highly sensitive information about individuals. Thus, there exists the need for accurate individual privacy risk evaluation. In this paper, we propose a methodology for assessing privacy risk in retail data. We define the data formats for representing retail data, the privacy framework for calculating privacy risk and some possible privacy attacks for this kind of data. We perform experiments in a real-world retail dataset, and show the distribution of privacy risk for the various attacks.

Assessing privacy risk in retail data

Pellungrini, Roberto
;
Pratesi, Francesca;
2017

Abstract

Retail data are one of the most requested commodities by commercial companies. Unfortunately, from this data it is possible to retrieve highly sensitive information about individuals. Thus, there exists the need for accurate individual privacy risk evaluation. In this paper, we propose a methodology for assessing privacy risk in retail data. We define the data formats for representing retail data, the privacy framework for calculating privacy risk and some possible privacy attacks for this kind of data. We perform experiments in a real-world retail dataset, and show the distribution of privacy risk for the various attacks.
2017
Settore INF/01 - Informatica
1st International Workshop on Personal Analytics and Privacy, PAP 2017, Held in Conjunction with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2017
Skopje (Macedonia)
2017
Personal Analytics and Privacy: An Individual and Collective Perspective : First International Workshop, PAP 2017, Held in Conjunction with ECML PKDD 2017, Skopje, Macedonia, September 18, 2017, Revised Selected Papers Lecture Notes in Bioinformatics)
Springer International
9783319719696
9783319719702
   SoBigData: Social Mining & Big Data Ecosystem”
   H2020
   654024
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11384/138312
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