Publication

Abstract : The aim of this work is to introduce a new secure system for Medical Healthcare records. The proposed design influences cloud infrastructures to give cost competence, fast exploitation, scalability, and flexibility to have room for unstable workloads. The research system could be utilized to safeguard diverse medical health care records. The proposed Secure Medical Healthcare Content Protection System system contains of two major steps: (i) Hybrid Key Generation and (ii) storage management. In the initial step of the work, keys values are created for medical healthcare records by using two encryption techniques such as Advanced Encoding Standard cryptography and Message Digest algorithm for safe multiparty computation. This proposed HKG algorithm is different from usual key generation algorithm, since the proposed HKG algorithm adds the key values of AES and MD5. New key value is created from these two algorithms, thus increases the security level of the system. The HKG algorithm creates dynamic and representative keys of medical healthcare records, it is computationally resourceful to calculate and contrast in addition it needs small storage. In the second step of the work, Compressive Sensing based framework for storage management of medical healthcare records. The distributed matching engine achieves greater adaptability and it is created to among different healthcare records gathered from UCI machine learning vault for 100 patients. The applicability is further examined for the applicability of sparse coding-based frameworks including the various numbers of medical healthcare records which influences the ability of Multi Cloud Computing environment. The experimentation work are performed by using Miscellaneous database for watermark pattern and any medical healthcare records are taken as input in MCC environment results outcomes prove that safe watermark recognition in the CS domain is possible for medical healthcare records are implemented in JAVA environment. The proposed work provides a CS based watermark recognition which influences safe MCC environment. The results are measured as in terms of Peak Signal to Noise Ratio , the Mean Square Error and the Structural Similarity Index, security and communication cost.