Privacy-Preserving AI Computation on Blockchain: Enhancing Confidentiality in Decentralized AI Systems
Published 07-10-2024
Keywords
- Privacy-Preserving Computation,
- Artificial Intelligence
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
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Abstract
The integration of artificial intelligence (AI) with blockchain technology has the potential to revolutionize data privacy and security in decentralized systems. This paper discusses techniques for conducting privacy-preserving AI computations on blockchain networks, emphasizing methods such as homomorphic encryption and secure multi-party computation (SMPC). These techniques allow for the processing of data without exposing sensitive information, thereby ensuring confidentiality and data integrity. Homomorphic encryption enables computations to be performed on encrypted data, while SMPC allows multiple parties to collaboratively compute a function without revealing their private inputs. This paper also explores the operational challenges and implications of implementing these techniques in real-world applications, providing a comprehensive understanding of how they can enhance confidentiality in decentralized AI systems. Ultimately, by combining the strengths of AI and blockchain with privacy-preserving techniques, we can foster a new era of secure and trustworthy decentralized applications.
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