An Enhanced ABE based Secure Access Control Scheme for E-health Clouds

Abstract

Abstract:The E-health clouds generate an enormous amount of data driven from PHR's, EHR's, EMR's, patient care, compliance and regulatory requirements. To preserve the privacy and confidentiality of the e-health data at untrusted servers various solutions on symmetric key-based access control has aroused. But the major drawback of these techniques is that it is patient centric and do not provide security and fine-grained access control. To solve these issues the paper defines a secure access control scheme for E-health clouds. First, an efficient architecture for E-health clouds is stated and then the access control scheme is defined. As the data stored at the cloud server is highly confidential first a three-factor mutual authentication is made between the cloud server and the data user. Further, the proposed technique makes use of Attribute based searchable encryption with trapdoor function that prevents unauthorised access to the cloud data in an efficient way. The experiment is conducted using charm crypto and the results show that the proposed system provides comparatively better results than the existing techniques.

Towards Smart Healthcare: Patient Data Privacy and Security in Sensor‐Cloud Infrastructure

“…In 2017, Shynu et al [51] proposed an e-health cloud storage system to handle multiple users for sensitive data sharing. The system consists of four major entities: CS, key management centre (KMC), DO, data user (DU), a nonpatient-centric approach adopted in which the health service provider (HSP) plays the role of DO.…”

“…The average time for health data request is assumed (m HC , m BS , or m D ) with X.509 certificate with 8192 bits and with 245760 bits. The treatment phase, with 1,230,316 bits as 3G telecommunication cost is 2Mbps/384 kbps/144 kbps, is evaluated and as the worst in terms of communication cost while, in the simulation of experiments in the ABE scheme [51], the computation time for encryption process is 28ms (milliseconds), quite low compared to other existing techniques (39ms). However, a complexity comparison with traditional encryption schemes [46] and the timing cost of operations used in ESPAC [42], evaluated by varying number of attributes [42], are commonly used to calculate time and cost complexity.…”

“…One study also used the SPSS tool to calculate function complexity, whereby an experimental setup created in Ubuntu 14.04 LTS 64-bit system [51] and ABE is compared to other existing encryption techniques in terms of computational time. Another study evaluated data sharing time with cloudlet, based on a trust model, analysed and categorized at three different levels as 'bad, average, and good. '…”

“…Most of the studies have randomly utilized some common PPPs such as ECG, EGG, blood pressure, and pulse rate [10,43,47] as their dataset. Others just refer to it as medical data [51] or medical images [46], without specifying particular PPPs. Therefore, there is a great need to adopt some standard or 'golden' dataset.…”

Securing E-health Data using Ciphertext-Policy Attribute-Based Encryption with Dynamic User Revocation

“…User revocation process is made by revoking user's attributes from the cipher-text itself, which makes revoked user's attribute removed from the list of users who can access and decrypt that file. Additionally, a method called the rekeying mechanism was proposed by authors of [21]. This method achieves revocation via a key update, in which if a user (i.e., Nurse) is revoked from the system, the key updated for other users' while keeping the revoked user key without an update.…”