Abhishek Das1, Ramya Ramachandran2, Imran Khan3, Om Goel4, Prof.(Dr.) Arpit Jain5, Dr. Lalit Kumar6
1Texas A&M University, North Bend, WA -98045, abdasoffice87@gmail.com
2University of Iowa, USA, ramyaramachandran.ai@gmail.com
3Visvesvaraya Technological University, MVJ College of Engineering Bangalore, India
4ABES Engineering College Ghaziabad, U.P., India, omgoeldec2@gmail.com
5Department of CSE, KL University, Guntur, A.P., India, dr.jainarpit@gmail.com
6Dept. of Computer Application, IILM University, Greater Noida, U.P., India
Abstract:
The General Data Protection Regulation (GDPR) has become a critical standard for ensuring data privacy and security across organizations handling personal data, regardless of scale. For petabyte-scale data systems, compliance becomes increasingly complex due to the massive volume, variety, and velocity of data being processed and stored. This paper explores effective resolution techniques for achieving GDPR compliance in such large-scale environments by addressing key architectural, technical, and legal challenges. First, the paper delves into the unique challenges posed by petabyte-scale data systems in maintaining GDPR compliance, such as ensuring data minimization, managing the right to erasure, handling cross-border data transfers, and fulfilling user requests for data access, rectification, and portability. Traditional compliance mechanisms, which work well for small-scale systems, are inadequate for petabyte-scale due to the exponential increase in data points, distributed storage, and complex data pipelines. This research provides a systematic approach to overcoming these challenges through the integration of advanced technologies, including distributed processing architectures, automated compliance tools, and real-time monitoring systems. The core focus of this research is on designing an architectural framework that not only meets GDPR requirements but also maintains high system performance and scalability. Key elements include encryption and anonymization for data protection, automated tagging for data classification, role-based access control for security, and data lineage tracking for traceability. Furthermore, the research presents various implementation strategies that leverage AI and machine learning for compliance automation, ensuring that compliance efforts scale proportionally with data growth.
A practical case study is presented to highlight the implementation of these techniques in a real-world petabyte-scale data warehouse. This case study provides insights into designing the data management architecture, managing large-scale data operations, and addressing compliance issues that arise in dynamic, data-intensive environments. The research demonstrates the effectiveness of differential privacy, federated learning, and synthetic data generation for safeguarding sensitive information while adhering to the right to privacy
Keywords: GDPR compliance, petabyte-scale data systems, data privacy, data management, encryption, anonymization, distributed processing, AI compliance tools, differential privacy, scalability, legal considerations, compliance automation.
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- Salunkhe, Vishwasrao, Shreyas Mahimkar, Sumit Shekhar, Prof. (Dr.) Arpit Jain, and Prof. (Dr.) Punit Goel. 2023. “The Role of IoT in Connected Health: Improving Patient Monitoring and Engagement in Kidney Dialysis.” SHODH SAGAR® Universal Research Reports 10(4):437. DOI: https://doi.org/10.36676/urr.v10.i4.1357.