Privacy Preservation in Edge Consumer Electronics by Combining Anomaly Detection with Dynamic Attribute-Based Re-Encryption
作者： Eunmok Yang , Velmurugan Subbiah Parvathy , P Pandi Selvi , K Shankar , Changho Seo
DOI: 10.3390/MATH8111871
关键词:
摘要: The expanding utilization of edge consumer electronic (ECE) components and other innovations allows medical devices to communicate with one another distribute sensitive clinical information. This information is used by health care authorities, specialists emergency clinics offer enhanced medication help. security client data a major concern, since modification hackers can be life-threatening. Therefore, we have developed privacy preservation approach protect the wearable sensor gathered from means an anomaly detection strategy using artificial intelligence combined novel dynamic attribute-based re-encryption (DABRE) method. Anomaly accomplished through modified neural network (MANN) based on gray wolf optimization (GWO) technique, where training speed classification accuracy are improved. Once removed, stored in cloud, secured proposed DABRE for future use doctors. Furthermore, method, biometric attributes, chosen dynamically, considered encryption. Moreover, if user wishes, unrecoverable true attributes cloud. A detailed experimental analysis takes place verify superior performance From results, it evident that GWO–MANN model attained maximum average rate (DR) 95.818% 95.092%. In addition, method required minimum encryption time 95.63 s decryption 108.7 s, respectively.
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