# Privacy Issues and Data Protection in Big Data: A Case Study Analysis under GDPR

arXiv: https://arxiv.org/abs/1811.08531 · HTML: https://mlsanity.com/paper/1811.08531/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Nov 2018.

## Built on by (most cited first, 6 of 6)

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- [GANonymization: A GAN-based Face Anonymization Framework for Preserving Emotional Expressions](https://mlsanity.com/paper/2305.02143/) (2023)
- [A Marketplace for Trading AI Models based on Blockchain and Incentives for IoT Data](https://mlsanity.com/paper/2112.02870/) (2021)
- [A Privacy-Preserving Unsupervised Domain Adaptation Framework for Clinical Text Analysis](https://mlsanity.com/paper/2201.07317/) (2022)
- [ARFED: Attack-Resistant Federated averaging based on outlier elimination](https://mlsanity.com/paper/2111.04550/) (2021)
- [PABAU: Privacy Analysis of Biometric API Usage](https://mlsanity.com/paper/2212.10861/) (2022)

## Abstract (arXiv)

Big data has become a great asset for many organizations, promising improved operations and new business opportunities. However, big data has increased access to sensitive information that when processed can directly jeopardize the privacy of individuals and violate data protection laws. As a consequence, data controllers and data processors may be imposed tough penalties for non-compliance that can result even to bankruptcy. In this paper, we discuss the current state of the legal regulations and analyze different data protection and privacy-preserving techniques in the context of big data analysis. In addition, we present and analyze two real-life research projects as case studies dealing with sensitive data and actions for complying with the data regulation laws. We show which types of information might become a privacy risk, the employed privacy-preserving techniques in accordance with the legal requirements, and the influence of these techniques on the data processing phase and the research results.