TY  - CONF
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85045838865&doi=10.1109%2fISPCC.2017.8269712&partnerID=40&md5=f659ea6fde510a7e316ae2b8e7c102fa
A1  - Singh, N.
A1  - Jangra, A.
A1  - Elamvazuthi, I.
A1  - Kashyap, K.
VL  - 2017-J
EP  - 407
Y1  - 2017///
SN  - 9781509058389
PB  - Institute of Electrical and Electronics Engineers Inc.
N1  - cited By 1; Conference of 4th IEEE International Conference on Signal Processing, Computing and Control, ISPCC 2017 ; Conference Date: 21 September 2017 Through 23 September 2017; Conference Code:134073
N2  - Information technology has great potential to improve healthcare quality and efficiency, and thus, has been a major focus of recent healthcare reform efforts. Today, data are usually tackled by leveraging existing encryption cryptographic methods, such that only outsourced data are encrypted and is inaccessible by cloud servers that enables to protect the confidentiality of the data. In this paper, various healthcare data privacy measures are studied and analyzed. It divides the measures on the basis of three major platforms, i.e., Architectural Measures, Technique-based Measures and Algorithmic Measures. Here, a detailed view of wide variety of proposals are beaded together to help researchers to get the best out of all traditional and conventional healthcare data privacy preserving schemes. A comprehensive comparison of the privacy-preserving approaches from the angle of the privacy-preserving requirements' satisfaction is presented. © 2017 IEEE.
KW  - Cloud computing; Cryptography; Health care; Signal processing
KW  -  Algorithmic measures; Cloud uncertainties; Comprehensive comparisons; Cryptographic methods; Data Science; Data-privacy preserving; Healthcare reforms; Privacy preserving
KW  -  Data privacy
TI  - Healthcare data privacy measures to cure & care cloud uncertainties
SP  - 402
ID  - scholars8370
AV  - none
ER  -