AI-based Re-identification of Behavioral Clickstream Data

Stefan Vamosi, Michael Platzer and Thomas Reutterer

Papers from arXiv.org

Abstract: AI-based face recognition, i.e., the re-identification of individuals within images, is an already well established technology for video surveillance, for user authentication, for tagging photos of friends, etc. This paper demonstrates that similar techniques can be applied to successfully re-identify individuals purely based on their behavioral patterns. In contrast to de-anonymization attacks based on record linkage, these methods do not require any overlap in data points between a released dataset and an identified auxiliary dataset. The mere resemblance of behavioral patterns between records is sufficient to correctly attribute behavioral data to identified individuals. Further, we can demonstrate that data perturbation does not provide protection, unless a significant share of data utility is being destroyed. These findings call for sincere cautions when sharing actual behavioral data with third parties, as modern-day privacy regulations, like the GDPR, define their scope based on the ability to re-identify. This has also strong implications for the Marketing domain, when dealing with potentially re-identify-able data sources like shopping behavior, clickstream data or cockies. We also demonstrate how synthetic data can offer a viable alternative, that is shown to be resilient against our introduced AI-based re-identification attacks.

Date: 2022-01
New Economics Papers: this item is included in nep-mkt and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://arxiv.org/pdf/2201.10351 Latest version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2201.10351

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().