Identifying GDPR Privacy Violations Using an Augmented LSTM: Toward an AI-based Violation Alert Systems

Ahmed Aleroud, Faten Masalha, Ahmad A. Saifan

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    After introducing the General Data Protection Regulation (GDPR), it becomes critical to preserve data privacy of individuals and organizations and detect any violations or data collection practices that do not comply with the GDPR articles. However, analyzing privacy incidents, then identifying the consequences and fines require significant effort and time from law enforcement authorities. Additionally, organization need systems that check whether data collections practices, and data processing mechanisms comply with diverse GDPR articles. In this paper, we proposed an approach to identify GDPR violations based on the recent privacy incidents and the semantic similarity of such incidents with the terminology used in different articles. Our approach is driven by both text summarization and deep learning techniques. We used Labeled Topic Modeling approach to identify topics associated with specific types of violations that correspond to different articles. We then used the identified feature to train and test a Long Short-Term Memory(LSTM) Deep Learner that identifies potential violations given textual descriptions. Our approach is compared to conventional text modeling techniques. The result demonstrates a promising accuracy of the proposed approach.

    Original languageEnglish (US)
    Title of host publication19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages1617-1624
    Number of pages8
    ISBN (Electronic)9781665435741
    DOIs
    StatePublished - 2021
    Event19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021 - New York, United States
    Duration: Sep 30 2021Oct 3 2021

    Publication series

    Name19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021

    Conference

    Conference19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021
    Country/TerritoryUnited States
    CityNew York
    Period9/30/2110/3/21

    Keywords

    • Compliance
    • Deep learning
    • GDPR
    • Privacy
    • Violation

    ASJC Scopus subject areas

    • Communication
    • Artificial Intelligence
    • Computer Networks and Communications
    • Computer Science Applications
    • Hardware and Architecture
    • Information Systems
    • Information Systems and Management
    • Renewable Energy, Sustainability and the Environment

    Fingerprint

    Dive into the research topics of 'Identifying GDPR Privacy Violations Using an Augmented LSTM: Toward an AI-based Violation Alert Systems'. Together they form a unique fingerprint.

    Cite this