Technical Solutions for the Processing, Management and Anonymisation of Personal Data in Databases According to EU Data Protection Regulations

The adoption of the European GDPR regulation created an obligation for operators to implement this regulation into their systems. One of its points mandates the immediate anonymization of personal data if an individual exercises their right to be forgotten. The goal of this thesis was to create a system that provides tools for anonymizing personal data in relational databases. The system allows for defining where in the database different types of personal data are located and how to search for them. Based on this configuration, the system can anonymize selected personal data of an individual or anonymize the data of all individuals across the database. The system was implemented as a web application and web server connected to a relational database. Tree traversal algorithms were used for searching data. Testing showed that the system can save on average 89% of the time needed for anonymizing an individual's data compared to manual anonymization, while eliminating errors and enabling oversight of the process through an audit. It also allows less technically skilled users to perform data anonymization. The main benefit of this system is its deployment in production environments, particularly in the information systems of state organizations of the Slovak Republic.

Authors

Institutions

Publication Details

Published
2024-10-24
DOI
https://doi.org/10.1109/iceta63795.2024.10850866
Primary Topic
Privacy-Preserving Technologies in Data
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Technical Solutions for the Processing, Management and Anonymisation of Personal Data in Databases According to EU Data Protection Regulations

O. Kainz, M. Murín, D. Cymbalák, S. Molčan et al.
Privacy-Preserving Technologies in Data
article

Technical Solutions for the Processing, Management and Anonymisation of Personal Data in Databases According to EU Data Protection Regulations

O. Kainz, M. Murín, D. Cymbalák, S. Molčan, M. Michalkc
article en

Abstract

The adoption of the European GDPR regulation created an obligation for operators to implement this regulation into their systems. One of its points mandates the immediate anonymization of personal data if an individual exercises their right to be forgotten. The goal of this thesis was to create a system that provides tools for anonymizing personal data in relational databases. The system allows for defining where in the database different types of personal data are located and how to search for them. Based on this configuration, the system can anonymize selected personal data of an individual or anonymize the data of all individuals across the database. The system was implemented as a web application and web server connected to a relational database. Tree traversal algorithms were used for searching data. Testing showed that the system can save on average 89% of the time needed for anonymizing an individual's data compared to manual anonymization, while eliminating errors and enabling oversight of the process through an audit. It also allows less technically skilled users to perform data anonymization. The main benefit of this system is its deployment in production environments, particularly in the information systems of state organizations of the Slovak Republic.

Technical University of Košice (SK), Slovak Centre of Scientific and Technical Information (SK)
Ministry of Education
Openalex Percentile: Top 76%
Privacy-Preserving Technologies in Data
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.