Privacy Preservation in Edge Consumer Electronics by Combining Anomaly Detection with Dynamic Attribute-Based Re-Encryption

Eunmok Yang, Velmurugan Subbiah Parvathy, P. Pandi Selvi, K. Shankar, Changho Seo, Gyanendra Prasad Joshi and Okyeon Yi
Additional contact information
Eunmok Yang: Department of Financial Information Security, Kookmin University, Seoul 02707, Korea
Velmurugan Subbiah Parvathy: Department of Electronics and Communication Engineering, Kalasalingam Academy of Research and Education, Srivilliputhur, Tamil Nadu 626128, India
P. Pandi Selvi: Department of Computer Science, Mangayarkarasi College of Arts and Science for Women, Madurai, Tamil Nadu 625018, India
K. Shankar: Department of Computer Applications, Alagappa University, Karaikudi, Tamil Nadu 630003, India
Changho Seo: Department of Convergence Science, Kongju National University, Gongju 32588, Korea
Gyanendra Prasad Joshi: Department of Computer Science and Engineering, Sejong University, Seoul 05006, Korea
Okyeon Yi: Department of Information Security, Cryptology, and Mathematics, Kookmin University, Seoul 02707, Korea

Mathematics, 2020, vol. 8, issue 11, 1-13

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

Keywords: edge consumer electronics (ECE); dynamic attribute-based re-encryption (DABRE); modified artificial neural network (MANN); Internet of medical things (IoMT); anomaly detection (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/2227-7390/8/11/1871/pdf (application/pdf)
https://www.mdpi.com/2227-7390/8/11/1871/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:8:y:2020:i:11:p:1871-:d:436502

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().