Future Trends in Cloud Computing
Combination of locally intelligent devices with backend Cloud-based processing is giving rise to a new class of edge or fog Cloud Computing, which offers new usage models, but also raises potential of new vulnerabilities with possibility of widespread cyberattacks. There are additional concerns of user lock-ins if vendors don’t follow interoperability standards in their edge-based devices in proprietary Cloud solutions. Additional issues of user-data privacy and legal jurisdiction currently lag the fast evolution of edge computing domain with IoT-based solutions. This requires policy framework to be discussed by vendors and Cloud service providers with the users for avoiding any legal pitfalls.
We look at security issues in edge computing, an example of IoT-based Cloud service, hardware as the root of trust, and security in the multi-party cloud. New topics of privacy-preserving multi-party analytics in a Public Cloud, hardware-based security using Intel’s SGX technology, and homomorphic encryption topics are discussed. Lastly, contemporary topics of software patches and using machine learning for security improvements are presented.
Above trends are likely to continue as networks will become faster and machines will become more intelligent to recognize patterns of data to make decisions. In this evolution, it is important to develop standards for interoperability of computing devices on the edge and servers on the back end, to ensure a level playing field.
- 16.Pussewalage, H. S. G., Ranaweera, P. S., Oleshchuk, V. A., & Balapuwaduge, I. A. M. (2013). Secure multi-party based Cloud Computing framework for statistical data analysis of encrypted data. ICIN 2016, At Paris, http://dl.ifip.org/db/conf/icin/icin2016/1570221695.pdf
- 18.MHMD. My Health My Data (MHMD), 2018. [Online; Accessed 2018].Google Scholar
- 19.Alkhadhr, S. B., & Alkandari, M. A. (2017). Cryptography and randomization to dispose of data and boost system security. Cogent Engineering, 4, 1. https://www.cogentoa.com/article/10.1080/23311916.2017.1300049. Tao Song (Reviewing Editor).
- 20.Giannopoulos, M. (2018, September). Privacy preserving medical data analytics using secure multi party computation. An end-to-end use case. Masters Thesis, National and Kapodistrian University of Athens. https://www.researchgate.net/publication/328382220_Privacy_Preserving_Medical_Data_Analytics_using_Secure_Multi_Party_Computation_An_End-To-End_Use_Case/stats
- 21.Costan, V., & Devadas, S. Intel SGX explained. https://eprint.iacr.org/2016/086.pdf
- 27.Gahi, Y., Guennoun, M., & El-Khatib, K. (2015, December). A secure database system using homomorphic encryption schemes. https://arxiv.org/abs/1512.03498
- 29.Biometrics Open Protocol (BOPS) III. IEEE 2401-2018, IEEE Standards Association. 2018.Google Scholar
- 37.Anti-Phishing Working Group. Phishing and Fraud solutions. http://www.antiphishing.org/
- 39.Abu-Nimeh, S., Nappa, D., Wang, X., & Nair, S. (2007, October 4–5). A comparison of machine learning techniques for phishing detection. APWG eCrime Researchers Summit, Pittsburg.Google Scholar
- 40.Chellapilla, K., & Simard, P. Y. (2005). Using machine learning to break visual human interaction proofs (HIPs). Advances in Neural Information Processing Systems, 17, 265–272.Google Scholar
- 41.Ford, V., & Siraj, A. (2014, October). Applications of machine learning in cyber security. 27th International Conference on Computer Applications in Industry and Engineering. https://www.researchgate.net/publication/283083699_Applications_of_Machine_Learning_in_Cyber_Security