Cryptography Techniques in Medical Data Privacy Protection: Applications and Challenges of Homomorphic Encryption, Differential Privacy, and Blockchain

With the rapid development of big data and artificial intelligence technologies, data security has become a critical bottleneck restricting the development of data science. This study systematically explores the innovative applications and implementation challenges of modern cryptographic techniques in the field of data science. The paper first reviews the fundamental theories of cryptography, such as symmetric encryption, asymmetric encryption, and hash functions. It then focuses on the cutting-edge applications of homomorphic encryption in privacy-preserving machine learning, differential privacy in user data analysis, and blockchain in data integrity verification. Through an in-depth analysis of typical cases such as medical data sharing and user behavior modeling, the study reveals the effectiveness and limitations of cryptographic techniques in practical deployment. The study further identifies the main challenges currently faced, including algorithmic computational efficiency, the transition to post-quantum cryptography, and the balance between data privacy and usability. Finally, this paper proposes future development directions for the deep integration of cryptography and data science from both technical evolution and policy-making perspectives. This study provides important theoretical references and methodological guidance for secure computing practices in the field of data science.