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Privacy and Security of Big Data in AI Systems: A Research and Standards Perspective
Open accessAbstract
The huge volume, variety, and velocity of big data have empowered Machine Learning (ML) techniques and Artificial Intelligence (AI) systems. However, the vast portion of data used to train AI systems is sensitive information. Hence, any vulnerability has a potentially disastrous impact on privacy aspects and security issues. Nevertheless, the increased demands for high-quality AI from governments and companies require the utilization of big data in the systems. Several studies have highlighted the threats of big data on different platforms and the countermeasures to reduce the risks caused by attacks. In this paper, we provide an overview of the existing threats which violate privacy aspects and security issues inflicted by big data as a primary driving force within the AI/ML workflow. We define an adversarial model to investigate the attacks. Additionally, we analyze and summarize the defense strategies and countermeasures of these attacks. Furthermore, due to the impact of AI systems in the market and the vast majority of business sectors, we also investigate Standards Developing Organizations (SDOs) that are actively involved in providing guidelines to protect the privacy and ensure the security of big data and AI systems. Our far-reaching goal is to bridge the research and standardization frame to increase the consistency and efficiency of AI systems developments guaranteeing customer satisfaction while transferring a high degree of trustworthiness.
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“Its two primary approaches are end-to-end fine-tuning, which adjusts all weights of the encoder and classifier [260], [261], and feature-extraction fine-tuning, where the encoder weights are frozen to extract features for a downstream classifier [262], [263], [264].”
From From Google Gemini to OpenAI Q* (Q-Star): A Survey on Reshaping the Generative Artificial Intelligence (AI) Research Landscape (2025) · cites this paper as [261]
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Metadata
- Authors
- Dilmaghani, S., Brust, M. R., Danoy, G., Cassagnes, N., Pecero, J., & Bouvry, P.
- Affiliations
- University of Luxembourg (LU)
- Venue
- 2019 IEEE International Conference on Big Data (Big Data)
- Publisher
- IEEE
- Published
- 2019-12
- Pages
- 5737-5743
- OpenAlex
- W3008060249
- Cited by
- 111
- References
- 71
- Citations by year
- 2026: 12 · 2025: 31 · 2024: 32 · 2023: 16 · 2022: 11 · 2021: 4 · 2020: 5
- FWCI
- 1.91
- Type
- conference paper
- Language
- en
- Open access
- green
- orbilu.uni.lu
- Licence
- Locations
- IEEE International Conference on Big Data (BigData) (publishedVersion)
- Open Repository and Bibliography (University of Luxembourg) (submittedVersion, cc-by-nc-sa)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Keywords
- big data privacy, big data security, artificial intelligence systems, machine learning, adversarial attacks, attack countermeasures, privacy-preserving machine learning, standards developing organizations, privacy standards, sensitive data, trustworthiness
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