Security and Privacy Measures to Protect Machine Learning in Medical Applications

Machine learning has become a crucial tool in the field of medicine, aiding in the development of predictive models, identifying patterns, and making precise diagnoses. However, the integration of ML in clinical programs raises concerns about safety and privacy. The increasing utilization of sensitive patient data and the potential for malicious attacks have underscored the importance of implementing security measures to safeguard ML models and data in healthcare environments. One of the major concerns in healthcare when it comes to machine learning is the potential for data breaches. It is crucial to recognize the high value and sensitivity of clinical data, as it makes it an attractive target for hackers. When working with ML, it becomes crucial to ensure the privacy and protection of data, as large datasets are necessary for training and testing models. Encryption techniques, such as holomorphic encryption and secure multiparty computation, provide a way to safeguard datasets while still enabling their use for training models. Another risk to consider in healthcare is the potential for malicious attacks. These attacks involve manipulating statistics or input versions to deceive the machine learning system into producing inaccurate predictions. This can have significant implications in medical applications, where incorrect diagnoses can harm patients. Various techniques are being used to mitigate the risk of adversarial assaults, including antagonistic training, robust version design, and outlier detection methods. The proposed model obtained 96.17% sensitivity, 94.31% Specificity, 94.41% scalability, and 94.36% Precision. This results will open new doors for research