@article{scholars12311,
            year = {2019},
         journal = {Advances in Intelligent Systems and Computing},
       publisher = {Springer Verlag},
           pages = {557--565},
            note = {cited By 2; Conference of 3rd International Conference of Reliable Information and Communication Technology, IRICT 2018 ; Conference Date: 23 June 2018 Through 24 June 2018; Conference Code:218299},
          volume = {843},
             doi = {10.1007/978-3-319-99007-1{$_5$}{$_2$}},
           title = {Biometric based signature authentication scheme for cloud healthcare data security},
            issn = {21945357},
          author = {Thangarasu, G. and Dominic, P. D. D. and Subramanian, K. and Smiley, S.},
            isbn = {9783319990064},
        keywords = {Authentication; Biometrics; Health care; Neural networks; Security of data; Soft computing, Biometric sensors; Biometric signatures; Hadoop MapReduce; Health-care managements; Healthcare services; Medical record; Signature authentication; Statistical learning, Network security},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85053865407&doi=10.1007\%2f978-3-319-99007-1\%5f52&partnerID=40&md5=3c3618a1376bc3a8078f8f42ed4cbdaf},
        abstract = {Cloud Computing is a recent and fastest growing area of development in healthcare. Cloud-based healthcare management services are considered as an effective way to manage the data related to healthcare. However, the major problem associated with the healthcare management service is data security. It leads to tax, bank, insurance and medical fraudulence. Hence, retrieval of data onto more secured access is required to improve the security of medical records of healthcare services. In this research, proposed a biometric based signature authentication scheme with neural network for cloud healthcare data security. The neural network is used in this scheme for the accuracy of retrieving the clinical data onto the cloud. The neural network acquires biometric signature through biometric sensor processed with quality checker for effective authentication. This network also has support in terms of statistical learning of the clinical datasets. After various experiments, it is concluded that the proposed method provides faster results with higher sensitivity, specificity with accuracy. {\^A}{\copyright} Springer Nature Switzerland AG 2019.}
}