Retail data are of fundamental importance for businesses and enterprises that want to understand the purchasing behaviour of their customers. Such data is also useful to develop analytical services and for marketing purposes, often based on individual purchasing patterns. However, retail data and extracted models may also provide very sensitive information to possible malicious third parties. Therefore, in this paper we propose a methodology for empirically assessing privacy risk in the releasing of individual purchasing data. The experiments on real-world retail data show that although individual patterns describe a summary of the customer activity, they may be successful used for the customer re-identifiation.

Privacy risk for individual basket patterns

Pellungrini, Roberto
;
2019

Abstract

Retail data are of fundamental importance for businesses and enterprises that want to understand the purchasing behaviour of their customers. Such data is also useful to develop analytical services and for marketing purposes, often based on individual purchasing patterns. However, retail data and extracted models may also provide very sensitive information to possible malicious third parties. Therefore, in this paper we propose a methodology for empirically assessing privacy risk in the releasing of individual purchasing data. The experiments on real-world retail data show that although individual patterns describe a summary of the customer activity, they may be successful used for the customer re-identifiation.
2019
Settore INF/01 - Informatica
18th European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2018). 2nd International Workshop on Personal Analytics and Privacy, PAP 2018
Dublin, Ireland
September 10-14, 2018
ECML PKDD 2018 workshops : MIDAS 2018 and PAP 2018 Dublin, Ireland, September 10–14, 2018: proceedings
Springer Nature
9783030134624
9783030134631
Learning systems; Risk assessment; Sales; Privacy risks; Real-world; Sensitive informations; Third parties; Data privacy
   SoBigData Research Infrastructure
   SoBigData
   European Commission
   Horizon 2020 Framework Programme
   654024
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11384/138308
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