Privacy-Preserving Fair Learning of Support Vector Machine with Homomorphic Encryption

The rising adoption of machine learning (ML) across various industries has sparked concerns due to the sensitive nature of the data involved and the opacity surrounding its collection, aggregation, and sharing practices. To address these concerns, researchers are actively developing methods to mitigate privacy risks associated with ML applications. One such approach involves integrating privacy-preserving mechanisms into active learning techniques. By leveraging homomorphic encryption-based federated learning, which enables distributed computation across multiple clients while maintaining strong data privacy, researchers have proposed a scheme that safeguards user data privacy in active learning scenarios. Experimental results indicate that this approach effectively preserves privacy while maintaining model accuracy. Additionally, a comparison with other schemes highlights its superiority in mitigating gradient leakage, with the proposed scheme exhibiting no gradient leakage compared to alternatives that suffer from significant leakage rates exceeding 74%. The growing adoption of machine learning (ML) is prompting concerns due to the sensitive nature of the data involved and the lack of transparency in data collection, aggregation, and sharing. As a result, various approaches are being devised to mitigate privacy risks and enhance acceptability, particularly in sectors like healthcare where ML's potential remains largely untapped. This study delves into cryptographic and security techniques to develop novel confidentiality assurances for both data and ML models.