Privacy-Preserving AI: Designing Secure Data Science Frameworks in the Age of Surveillance
DOI:
https://doi.org/10.64882/ijrt.v13.i2.1183Keywords:
Privacy-Preserving AI, Data Privacy, Differential Privacy, Federated Learning, Ethical AI, Surveillance, Secure Data Science, AI GovernanceAbstract
The rapid advancement of Artificial Intelligence (AI) and data science has significantly transformed modern society by enabling data-driven decision-making across sectors such as healthcare, finance, education, and governance. However, these advancements have simultaneously raised serious concerns about data privacy, surveillance, and ethical data usage. The extensive collection and processing of personal data expose individuals to risks such as data breaches, identity theft, and unauthorized surveillance (European Union, 2018). In this context, privacy-preserving AI has emerged as a critical approach to safeguarding sensitive information while maintaining analytical efficiency. This paper explores key privacy-preserving techniques, including differential privacy, federated learning, homomorphic encryption, and secure multi-party computation. It evaluates their effectiveness, limitations, and applicability in real-world scenarios. Furthermore, the study proposes a secure data science framework that integrates these technologies with governance mechanisms to ensure ethical AI deployment. The findings highlight that privacy preservation must be embedded into the design of AI systems rather than treated as an afterthought. The paper concludes by emphasizing the need for interdisciplinary collaboration to develop trustworthy and transparent AI ecosystems.
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