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Innovative Strategies for Ensuring Privacy in Machine Learning Environments
  • Elham Shammar
Elham Shammar
Department of Information Technology, Faculty of Computer and Information Technology (FCIT), Ministry of Education, School of Cyber Science and Engineering, Key Laboratory of Aerospace Information Security and Trusted Computing, Wuhan University

Corresponding Author:[email protected]

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This paper presents an in-depth examination of privacy-enhancing methodologies in machine learning. It highlights the integration of federated learning with cutting-edge encryption techniques and explores how blockchain architectures contribute to data privacy. A major focus is on federated learning, a decentralized model training strategy, and its combination with privacy-protecting technologies like Homomorphic Encryption, Differential Privacy, and Secure Multi-Party Computation. We emphasize that federated learning naturally improves data privacy and, when paired with cryptographic methods, increases resilience against data breaches and cyber-attacks. Additionally, this study explores the potential of blockchain in enhancing data privacy. Blockchain's immutable and transparent characteristics, supplemented with shuffling technology, zero-knowledge proofs, and ring signatures, improve the confidentiality and integrity of data transactions. The paper also emphasizes the critical need for transparency and explainability in machine learning, advocating for methods that demystify the decision-making processes of ML models. This transparency is crucial for building trust and is becoming a regulatory requirement in many industries. Furthermore, the paper discusses the importance of auditing in machine learning, highlighting the need for comprehensive model validation and ethical considerations. In conclusion, the paper argues that achieving a balance 1 between functionality and privacy in ML applications is essential. It suggests that a combination of federated learning, advanced cryptographic techniques, and explainable AI principles can create effective and privacy-respecting systems.
20 Jan 2024Submitted to TechRxiv
26 Jan 2024Published in TechRxiv