Examining Log File Data for GDPR Compliance: A Preliminary Study

This Zenodo page contains the dataset and results for the analysis of log data with a focus on GDPR compliance. The study investigates the presence of personal data, particularly IP addresses, in log files and assesses their compliance with GDPR principles. The dataset and results provide valuable insights into the challenges and considerations related to log data management and GDPR compliance.

The page includes the following folders:

1. raw_dataset: This folder contains the raw log dataset used in the study. The dataset comprises a comprehensive collection of log lines obtained from diverse systems.

2. sample_dataset: In this folder, you will find a sample dataset extracted from the raw log dataset. It consists of 2,000 randomly selected log lines. The sample dataset provides a representative subset for analysis and evaluation purposes.

3. parsed_dataset: The parsed_dataset folder contains the results of parsing the log data using the Universal Log Parser (ULP). The parsed dataset organizes the log lines into structured formats, enabling further analysis and processing.

4. data_analysis: This folder contains the analysis results related to IP address detection and identification of other personal data in the log files. The analysis focuses on assessing the occurrences of IP addresses and their association with personal information, such as usernames and domain names. The findings shed light on the potential risks and compliance challenges regarding the collection and processing of personal data in log files.

The dataset and results offered on this Zenodo page aim to contribute to the understanding of log data management, GDPR compliance, and the impact of personal data collection in log files. Researchers, practitioners, and organizations interested in log data analysis, data protection, and compliance can benefit from accessing and utilizing these resources.

Please note that appropriate acknowledgments and citations are appreciated when using or referring to this dataset and the associated results.