Authors - Satvik Taviti, Srreyasri Kurlagunda, Nandikanti Sri Gayatri, R M Krishna Sureddi, Raman Dugyala Abstract - Electronic Health Records (EHR) are considered to be amongst the most crucial elements for exchanging data in healthcare services. Thus, security for these records is the keystone of patient privacy and easy cooperation between the service providers. This review looks at four primary approaches to EHR security and predictive management: Blockchain, Attribute-Based Encryption (ABE), Deep Learning, and Access Control Models. Blockchain ensures data integrity, transparency, and traceability but scalability issues, high transaction costs, and interoperability challenges prevent its widespread adoption. ABE is appropriate for fine-grained access control in data sharing under the patient-centred approach but cumbersome and resource-intensive for managing encryption across the large healthcare network. Deep learning helps predictive analytics, personalized medicine, but with high computational demands that affect its real-time application in the clinical environment. While in terms of data confidentiality protection, models such as Role-Based or Attribute-Based Access Control may ensure proper restriction of authorized access, they might not suffice for dynamic, multi-provider health environments. Comparing the techniques will outline their relative merits, weaknesses, and security considerations, thus helping to understand how safe yet scalable systems for EHR storage could be built.
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