SciFest National Final 2024

Stand 6

Stand 6

Enhancing Data Security in AI Training using Zero-Knowledge Proofs for Secure Methodology Development

Student Addison Carey
School Celbridge Community School, Maynooth Road, Celbridge, Co. Kildare
Teacher Zita Murphy
Venue TU Dublin Tallaght
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Abstract

Ensuring the security and privacy of sensitive data during AI and machine learning model training has become a critical challenge. Training AI models on datasets containing financial, medical, or other regulated information requires robust encryption methods and strict adherence to privacy regulations. As AI becomes increasingly integrated into essential services, the need for secure data-handling methodologies is more urgent than ever. My project aims to address these concerns by integrating Zero-Knowledge Proofs (ZKPs) into the AI training process. ZKPs are cryptographic protocols that allow one party to prove possession of certain information to another party without revealing the information itself. By applying ZKPs, I aim to develop an algorithm that enables AI models to process encrypted data without exposing the raw data. This approach minimizes data exposure risks, ensuring privacy while maintaining model performance.

I started this project in February 2024 and developed a basic ZKP algorithm that encrypts input data, which is then used through a simple linear regression model. My project also incorporates data visualization through a Flask-based interface. While my current model uses a small dataset and basic encryption, I am planning to scale to larger datasets and to refine the algorithm using stronger encryption and cryptographic hash functions to enhance the zero- knowledge aspect further.

While ZKPs can be difficult to integrate into the world of AI, I believe my project demonstrates a novel application of ZKPs in AI, offering a potential solution to one of the biggest challenges in secure AI model training.

Poster

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SciFest National Final 2024
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