With the technological revolution Healthcare \(-\) 4.0 strives to provide personalized quality of care at lowest reasonable cost. In this, the health data collected and processed by various Internet of Medical Things (IoMT) devices, communicated via open channel, stored at edge-cloud servers and shared among different stakeholders. It raises two primary concerns: efficiency and information leakage. The Attribute Based Access Control (ABAC) address these issues and widely accepted for data access management. It grants access to users based on the predefined policy. Healthcare information is highly sensitive and personal. The data owner wish to share it only with specific data users such as highly experienced cardiologist. But during a medical emergency, this information must be available to the caregiver, even though the caregiver may not be a cardiologist or have less experience. It requires an adaptive access control policy, based on the present health conditions of the data owner. The proposed adaptive access control framework bridge this gap by including some data attributes in the access policy construction. Another issue is, majority of data attributes are ranged value attributes. Such attribute can not be helpful in policy construction. It need to be converted as a sub-tree of all possible interval values. Each interval values now considered as simple attribute and participated in policy definition. It increases the number of attributes and creates performance bottleneck. To resolve it, this paper present an attribute exposition technique based on 0-encoding and 1-encoding. The present work also constructs a Garbled-circuit that anonymously compares the exposited attributes and strictly resists the attribute collusion.
