@inproceedings{scholars8370,
           title = {Healthcare data privacy measures to cure \& care cloud uncertainties},
          volume = {2017-J},
            note = {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},
             doi = {10.1109/ISPCC.2017.8269712},
       publisher = {Institute of Electrical and Electronics Engineers Inc.},
         journal = {4th IEEE International Conference on Signal Processing, Computing and Control, ISPCC 2017},
           pages = {402--407},
            year = {2017},
          author = {Singh, N. and Jangra, A. and Elamvazuthi, I. and Kashyap, K.},
            isbn = {9781509058389},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85045838865&doi=10.1109\%2fISPCC.2017.8269712&partnerID=40&md5=f659ea6fde510a7e316ae2b8e7c102fa},
        keywords = {Cloud computing; Cryptography; Health care; Signal processing, Algorithmic measures; Cloud uncertainties; Comprehensive comparisons; Cryptographic methods; Data Science; Data-privacy preserving; Healthcare reforms; Privacy preserving, Data privacy},
        abstract = {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. {\^A}{\copyright} 2017 IEEE.}
}