Detecting Inference Attack Accuracy: Using Federated Learning

The increasing adoption of smart health systems has revolutionized the healthcare industry, offering improved diagnosis, personalized However, the security and privacy of sensitive patient data remain one of the major concerns. Federated machine learning has emerged as a promising approach to address these challenges by enabling collaborative learning across multiple healthcare institutions without sharing raw data. This paper investigates and detects the inference attack accuracy of federated machine learning algorithms in the context of smart healthcare. The research examines popular algorithms such as multiple datasets specific to smart healthcare. The findings highlight the effectiveness of these algorithms in protecting sensitive healthcare data, with FedDP consistently achieving the highest accuracy. The study contributes to the field by providing evidence of effective methods for safeguarding patient privacy in smart healthcare. Furthermore, the paper explores the strengths, limitations, and trade-offs of different algorithms, enabling researchers and practitioners to make intelligent decisions in selecting appropriate algorithms for privacy-preserving analytics. The study also discusses the ethical considerations, the data collection process, and the experimental methodology employed. The results of this research enhance the understanding of federated machine learning in smart healthcare and contribute to the development of robust mechanisms for data privacy and security. The findings foster trust among patients, healthcare providers, and stakeholders, paving the way for the secure and responsible use of advanced technologies in healthcare.