Modern healthcare systems, especially those using IoT technologies, must secure patient data. This paper offers a secure healthcare data format using One-Time Pad (OTP) encryption and the Isolation Forest technique for machine-learning anomaly detection. We seek to maintain information-theoretic secrecy and discover aberrant operational patterns with high statistical accuracy. In contrast to the classic signature-based or rule-based mechanisms of detecting anomalies, as they use a set of pre-defined patterns and can only be used when readable data is available, the suggested framework uses behavioral analysis and does not scan encrypted data. This method will increase the end-to-end confidentiality and allow the detection of anomalies to work in encrypted environments. Experiments show that the suggested framework has a 98.4% detection rate, 1.2% false positive rate, and 0.97 F1-score. Averaging 0.0002 seconds, the system has low processing latency. These results show that the framework is efficient, lightweight, and reliable for mission-critical real-time healthcare applications that need accuracy and data confidentiality.
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