Data security model for Cloud Computing using V-GRT methodology

Cloud Computing becomes the next generation architecture of IT Enterprise. In contrast to traditional solutions, Cloud computing moves the application software and databases to the large data centers, where the management of the data and services may not be fully trustworthy. This unique feature, however, raises many new security challenges which have not been well understood. In cloud computing, both data and software are fully not contained on the user's computer; Data Security concerns arising because both user data and program are residing in Provider Premises. Clouds typically have single security architecture but have many customers with different demands. Every cloud provider solves this problem by encrypting the data by using encryption algorithms. But there are also chances that the cloud service is not trust worthy, to overcome this problem. This paper introduces a new model called V-GRT methodology which overcomes the basic problem of cloud computing data security. We present the data security model of cloud computing with security vendor that eliminates the fear of misuse of data by the cloud service provider thereby improving data security.

[1]  Francesco Pagano,et al.  Using in-memory encrypted databases on the cloud , 2011, 2011 1st International Workshop on Securing Services on the Cloud (IWSSC).

[2]  A. Kakoli rao Centralized Database Security in Cloud , 2012 .

[3]  Amanjot Kaur,et al.  HYBRID ENCRYPTION FOR CLOUD DATABASE SECURITY , 2012 .

[4]  C Praveen Ram,et al.  Security as a Service (SasS): Securing user data by coprocessor and distributing the data , 2010, Trendz in Information Sciences & Computing(TISC2010).

[5]  Ramachandran Baskaran,et al.  QoS enhancements for global replication management in peer to peer networks , 2012, Future Gener. Comput. Syst..

[6]  P. Victer Paul,et al.  Improving Efficiency of Peer Network Applications by Formulating Distributed Spanning Tree , 2010, 2010 3rd International Conference on Emerging Trends in Engineering and Technology.

[7]  Anshu Parashar,et al.  Secure User Data in Cloud Computing Using Encryption Algorithms , 2013 .

[8]  Eric Pardede,et al.  MCDB: Using Multi-clouds to Ensure Security in Cloud Computing , 2011, 2011 IEEE Ninth International Conference on Dependable, Autonomic and Secure Computing.

[9]  Dan Boneh,et al.  The Decision Diffie-Hellman Problem , 1998, ANTS.

[10]  Andreas Willig,et al.  A Framework for Resource Allocation Strategies in Cloud Computing Environment , 2011, 2011 IEEE 35th Annual Computer Software and Applications Conference Workshops.

[11]  Raylin Tso,et al.  A commutative encryption scheme based on ElGamal encryption , 2012, 2012 International Conference on Information Security and Intelligent Control.

[12]  E. M. Mohamed,et al.  Enhanced data security model for cloud computing , 2012, 2012 8th International Conference on Informatics and Systems (INFOS).

[13]  Michel Riveill,et al.  Cloud computing, security and data concealment , 2011, 2011 IEEE Symposium on Computers and Communications (ISCC).

[14]  Cong Wang,et al.  Privacy-Preserving Multi-Keyword Ranked Search over Encrypted Cloud Data , 2014 .

[15]  Ramachandran Baskaran,et al.  Efficient service cache management in mobile P2P networks , 2013, Future Gener. Comput. Syst..

[16]  D. Manivannan,et al.  Light weight and secure database encryption using TSFS algorithm , 2010, 2010 Second International conference on Computing, Communication and Networking Technologies.

[17]  Yang Tang,et al.  FADE: Secure Overlay Cloud Storage with File Assured Deletion , 2010, SecureComm.

[18]  Craig Gentry,et al.  A fully homomorphic encryption scheme , 2009 .

[19]  Stefan Katzenbeisser,et al.  POSTER: Event-based isolation of critical data in the cloud , 2013, CCS.

[20]  N. Cao,et al.  Privacy-preserving multi-keyword ranked search over encrypted cloud data , 2011, 2011 Proceedings IEEE INFOCOM.