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Privacy-Preserving AI: Techniques, Challenges, and Future Directions

Year: 2025

Type: article

Abstract: Modern AI systems rely heavily on vast datasets, often containing sensitive personal, financial, or medical information. This dependency raises critical concerns about data misuse, leakage, and regulatory compliance. Privacy-preserving AI aims to train and deploy models that retain high performance while ensuring that user data remains confidential. This paper presents a comprehensive analysis of current PPAI methods, their effectiveness, and practical implications for real-world deployment.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.15358212

Author: Ghritlahare, Akhilesh

Language: English

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W6911966682

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