The development of artificial intelligence (AI) technologies has led to profound changes in all industries, but has also raised significant security concerns. AI systems are vulnerable to various attacks and security vulnerabilities that can compromise sensitive data and system integrity. In this paper, we present a research initiative that focuses on securing AI systems through the application of cryptographic techniques. The main goal of this research is to address the urgent need for robust security measures in the field of AI. We aim to explore and implement cryptographic methods as a means to protect AI systems. In doing so, we emphasize their critical importance at a time when AI is increasingly integrated into numerous areas of our daily lives. Our research takes a multi-faceted approach. We begin with a comprehensive survey of the existing literature on AI security and cryptographic techniques. We analyze the vulnerabilities and risks associated with AI systems and explore how cryptographic methods can mitigate these threats. We select AI models relevant to healthcare and finance applications, using diagnostic models like convolutional neural networks (CNNs) for medical image analysis and fraud detection models such as decision trees or neural networks. Datasets include publicly available medical image databases (e.g., X-ray or MRI images) and credit card fraud detection datasets. We implement cryptographic methods including the Paillier cryptosystem for homomorphic encryption. Our preliminary results demonstrate the effectiveness of this techniques in protecting sensitive data, algorithms, and AI-generated content from unauthorized access and tampering. Our findings highlight the potential role of blockchain technology in transparency, traceability, and trustworthiness of AI-generated content. The significance of this research lies in its potential to revolutionize the AI security landscape. By using cryptographic techniques, AI experts can protect their systems against privacy breaches, adversarial attacks, and misuse of AI-generated content. The findings not only contribute to the theoretical understanding of AI security but also offer practical solutions that can be applied across industries, including healthcare, finance, autonomous vehicles, and more. Ultimately, this research has the potential to increase public trust in AI technologies and foster innovation in a secure and reliable AI-driven world.
AI, Techniques, Homomorphic, Encryption, Privacy, Preser- vation Integrity and Trust
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