Cloud users demands security to their data which are stored in data repositories of cloud service provider. Thus the concept of Network Security can be applied over the cloud network, where several encryption algorithms are applied to provide integrity on the data. Such algorithms include Symmetric encryptions, Asymmetric encryptions, Hashing algorithms and Digital signatures. MONOMI is a system for securely executing analytical workloads over sensitive data on an untrusted database server. MONOMI works by encrypting the entire database and running queries over the encrypted data. CryptDB is a MySQL proxy that allows SQL aware encryption inside existing database management systems. To offer the best possible protecting while enabling the greatest computational flexibility it relies on a new concept called onions, where different layers of encryption are wrapped around each other and are only revealed as necessary. While its concept to improve database security looks fresh and interesting from an academic standpoint we wanted to examine the usability in practical application to determine if a real world productive use is desirable.

Security, Internet of things, Crypt DB, Monomi

[1]. D. J. Abadi, S. R. Madden, and N. Hachem. Column-stores vs. rowstores: how different are they really? In Proc. of SIGMOD, pages 967–980, Vancouver, Canada, June 2008.
[2]. S. Agrawal, S. Chaudhuri, and V. R. Narasayya. Automated selection of materialized views and indexes in SQL databases. In Proc. of the 26th VLDB, pages 496–505, Cairo, Egypt, Sept. 2000.
[3]. A. Arasu, S. Blanas, K. Eguro, R. Kaushik, D. Kossmann, R. Ramamurthy, and R. Venkatesan. Orthogonal security with Cipherbase. In Proc. of the 6th CIDR, Asilomar, CA, Jan. 2013.
[4]. S. Bajaj and R. Sion, “TrustedDB: A Trusted Hardware Based Database with Privacy and Data Confidentiality,” Proc. ACM SIGMOD Int’l Conf. Management of Data (SIGMOD ’11), pp. 205-216, 2011.
[5]. S. Bajaj and R. Sion, “TrustedDB: A Trusted Hardware Based Outsourced Database Engine,” Proc. Int’l Conf. Very Large DataBases (VLDB), 2011.
[6]. D. Bogdanov et al. A universal toolkit for cryptographically secure privacy-preserving data mining. In PAISI, 2012.
[7]. F. Emek_ci and D. Agrawal et al. Privacy preserving query processing using third parties. In ICDE, 2006.
[8]. C. Gentry. Fully homomorphic encryption using ideal lattices. In STOC, 2009.
[9]. C. Gentry et al. Fully homomorphic encryption with polylog overhead. In EUROCRYPT, 2012.
[10]. O. Goldreich, S. Micali, and A. Wigderson. How to play any mental game. In STOC, 1987.
[11]. Stephen Tu, M. Frans Kaashoek, Samuel Madden, and Nickolai Zeldovich. 2013. Processing Analytical Queries Over Encrypted Data. In Proceedings of the Conference on Very Large Data Bases (VLDB).
[12]. Vlado Altmann, Jan Skodzik, Frank Golatowski, and Dirk Timmermann. “Investigation of the use of embedded Web Services in smart metering applications”. In: Conference on IEEE Industrial Electronics Society. IECON. Oct. 2012, pp. 6172–6177. DOI: 10.1109/IECON.2012.6389071.
[13]. Sven Bendel et al. “A service infrastructure for the Internet of Things based on XMPP”. In: Int. Conference on Pervasive Computing and Communications Workshops. PERCOM Workshops. Mar. 2013, pp. 385–388. DOI: 10.1109/PerComW.2013.6529522.
[14]. SHEN changxiang, ZHANG Huanguo and FENG Dengguo, “Literature Review of Information Security” Science in China (Series E: Information Sciences), vol.37, no.2, 2007, pp.129-150 WU chuankun, “A Preliminary Investigation on the Security Architecture of the Internet of Things,” Bulletin of Chinese Academy of Sciences, vol 25,no. 4, 2010, pp 411-419.