Integrating Cryptographic Techniques with Machine Learning Algorithms for Enhanced Data Privacy and Information Security: A Mathematical Framework

Abstract

Including machine learning in cryptographic schemes, notably homomorphic encryption(HE), is one prominent research direction that might hold the key to maintaining higher levels of data privacy and information security. However, almost all traditional data encryption models expect the payload to be decrypted first before any processing can take place. Such an additional decryption layer is a backdoor waiting to be breached — especially for industries, such as healthcare that have information safeguarding mandates related to sensitive data. The reports further point out that traditional cryptographic mechanisms are not adequate and hence suggest the use of lightweight cryptography in order to secure healthcare IoT enabled devices, as it trades off security with resource constraints associated with this class of small commodity sensors. HE allows encrypted data to be computed on without having to decrypt it, impede throughout processing from the sensitive. This is particularly the case in healthcare where Patient Health Records should naturally be kept private. While SVM and Random Forest are other family members of ML algorithms that can be used in HE area for a specific task like seizure detection or alcoholic predisposition from EEG signals as explored by this study. What these figures evidence are that the predictions on plaintext of data is almost as good and more often than not quicker, but computations involving encrypted date can be computationally expensive. This demonstrates the potential for privacy-preserving machine-learning applications, especially in healthcare systems such as implemented by this end-to-end so… Future work needs to look into integrating HE with DL methods for high-level data analysis and designing practical ML algorithms that can be deployed on resource limited IoT devices.