анонімізація даних

Analysis of Effectiveness and Vulnerabilities of Privacy-Preserving Methods Using K-Anonymity, L-Diversity, and T-Closeness as Examples

The article analyzes and compares personal data anonymization methods using k-anonymity, ℓ- diversity, and t-closeness as examples. The aim of the research is to evaluate the effectiveness of these methods in ensuring data privacy and identifying their vulnerabilities to re-identification attacks. The study was performed using the ARX Anonymization Tool on a test dataset containing personal income information.

Anonymization of Data Using Blockchain Technology: a Model for Data Lifecycle Management to Ensure Transparency and Compliance With Gdpr

The rapid growth in the volume of personal data collected and processed by various organizations poses significant challenges for ensuring information privacy and security. The General Data Protection Regulation (GDPR) of the European Union sets strict requirements for the processing, storage, and deletion of personal data, including the right to be forgotten, which entails the complete and irreversible deletion of information upon user request.