Abstract
In the present scenario, the proliferation of cloud computing services allows hospitals and institutions to move their healthcare data to the cloud, enabling global access to data and on-demand high-quality services at a lower cost. Healthcare data has sensitive attributes to be shielded from leakage due to inference attacks by a curious intruder, either directly or indirectly. A hybrid cloud is a mix of both private and public clouds proposed for the storage of health data. Carefully distributing data between private and public clouds to provide protection. While there has been ample work for the delivery of health data for some time now, it does not appear to be more effective in terms of both data retrieval and consideration for fine-grained access control of the data. This work suggests a cordial approach for a more reliable delivery of data using geometric data disruption of health data over hybrid clouds. It is focused on an in-depth review of the results. The distribution enforces fine-grained data access control using attribute-based encryption. In addition, the approach also addresses a method to effectively extract relevant information from hybrid clouds.
Highlights
Many organizations have been in the process of converting data management to cloud storage in the light of factors such as cost-effectiveness, affordability, redundancy, etc. [4]
We evaluate the privacy of the proposed Transmitted Team Key Management (TTKM) scheme and compare it to the current Scalable Attribute-Based Encryption (SABE) protection scheme for distributed data sharing
K-Means clustering is done on the original data as well as on the disrupted data produced by the proposed Repeated Gompertz (RG)+random project matrix (RP)
Summary
Many organizations have been in the process of converting data management to cloud storage in the light of factors such as cost-effectiveness, affordability, redundancy, etc. [4]. Data protection and privacy are the two most critical things to remember when it comes to cloud data storage. It is of utmost importance to ascertain the protection and privacy of data stored in the cloud. Through transmitting obfuscated information and parameters used for various data stores, the confidentiality of the obfuscated data is guaranteed even when it is leaked. In addition to data security, the index must be protected as private information may be accessed by inference. With these criteria, a stable geometric data disruption approach for health data using hybrid clouds is proposed in this work. The method proposes a fine-grained access control on perturbed data with efficient secure indexing and retrieval of information
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