3 research outputs found
A clustering approach to anonymize locations during dataset de-identification
No full textCompanies increasingly rely on massive amounts of data for strategic decision making purposes. In order to optimize business intelligence, companies often try to enrich their models with datasets acquired from third parties. Datasets containing sensitive attributes must be anonymized before release. For large datasets containing microdata, an often applied anonymization technique is data generalization with the goal of achieving privacy metrics such as k-anonymity.
Location is an often recurring yet strategic attribute in many use cases. Multiple strategies can be employed to obfuscate precise coordinates. For example, the most significant digits can be dropped or their value can be replaced by a ZIP code. While these methods might be useful in some applications, these approaches often result in too much information loss, undermining strategic decision making. This paper proposes a novel approach to anonymize location by means of clustering. Its feasibility is evaluated and compared to traditional techniques.status: Published onlin
Dataset anonymization with purpose: a resource allocation use case
No full textNowadays, companies are collecting huge amounts of data. Applying the collected data to optimize the business activities can significantly improve profit margins. In this context, companies often want to enhance their models by enriching the data with data from external sources. Increasingly, companies are also considering selling data as an additional source of income. Governments are also willing to share citizen data with businesses. The GDPR regulation, introduced in May 2018 provides a framework for different parties (commercial, governmental, academic) to share and sell data provided that the data is anonymized. The effect of this anonymization step on the quality of the data (and the resulting business optimization conclusions) are still unclear. Utility and quality metrics that exist are purely theoretical, and do not grasp the purpose of the anonymized data, resulting in discrepancies between the expected and the actual utility of an anonymized dataset. This work studies the practical utility of anonymized datasets. It assesses the effect of applying the K-anonymity metric and dataset sampling on the utility of the data by conducting experiments on a resource allocation use case. Practical guidelines are presented for anonymizing datasets while maintaining a high degree of practical utility.status: Published onlin
A hybrid anonymization pipeline to improve the privacy-utility balance in sensitive datasets for ML purposes
No full textsponsorship: This research is partially funded by the VLAIO ICON project Co-CoNUT, the SOLIDLab Flanders initiative, and by the Flemish Re-search Programme Cybersecurity. (VLAIO ICON project, SOLIDLab Flanders initiative, Flemish Re-search Programme Cybersecurity)status: Published onlin
