A Risk Assessment and Legal Compliance Framework for Supporting Personal Data Sharing with Privacy Preservation for Scientific Research
Authors: Christos Baloukas Lazaros Papadopoulos Kostas Demestichas Axel Weissenfeld Sven Schlarb Thomas Marquenie Ezgi Eren Irmak Erdogan Peter
Date: 30.07.2024
Abstract
In order to perform cutting-edge research like AI model training, a large amount of data needs to be accessed. However, data providers are often reluctant to share their data with researchers as these might contain personal data and thereby sharing may introduce serious risks with significant personal, institutional or societal impacts. Apart from the need to control these risks, data providers must also comply with regulations like GDPR, which creates an additional overhead that makes data sharing even less appealing to data providers. Technologies like anonymization can play a critical role when sharing data that may contain personal information by offering privacy preservation measures like face or license plate anonymization. Therefore, we propose a framework to support data sharing of personal data for research by integrating anonymization, risk assessment and automatic licence agreement generation. The framework offers a practical and efficient solution for organisations seeking to enhance data-sharing practices without compromising information security.
BIB_text
title = {A Risk Assessment and Legal Compliance Framework for Supporting Personal Data Sharing with Privacy Preservation for Scientific Research},
pages = {184},
keywds = {
Personal Data Sharing, Risk Assessment, Privacy Preservation, License Agreement
}
abstract = {
In order to perform cutting-edge research like AI model training, a large amount of data needs to be accessed. However, data providers are often reluctant to share their data with researchers as these might contain personal data and thereby sharing may introduce serious risks with significant personal, institutional or societal impacts. Apart from the need to control these risks, data providers must also comply with regulations like GDPR, which creates an additional overhead that makes data sharing even less appealing to data providers. Technologies like anonymization can play a critical role when sharing data that may contain personal information by offering privacy preservation measures like face or license plate anonymization. Therefore, we propose a framework to support data sharing of personal data for research by integrating anonymization, risk assessment and automatic licence agreement generation. The framework offers a practical and efficient solution for organisations seeking to enhance data-sharing practices without compromising information security.
}
isbn = {979-8-4007-1718-5},
date = {2024-07-30},
}
