Privacy preserving framework for brute force attacks in cloud environment

Cloud model of computing will be widely adopted by different organisations if it can support a higher level of data privacy than currently supported. The higher level of data privacy is mandatory to store and query the sensitive data in cloud-based information system applications such as customer relationship management (CRM) systems. Identity-based homomorphic encryption and tokenisation has proved its efficiency in providing privacy and simultaneously querying encrypted data. However, in cloud-based software-as-a-service (SaaS) model, the adversary can run brute force attacks which can reveal the attribute values by colluding with the service provider. It is a significant challenge to detect and prevent such attacks. This paper presents a comprehensive solution using application-independent metrics consisting of different types of vulnerability measures. This paper also presents the detailed design of a system that uses application-independent metrics to prevent brute force attacks.

[1]  Teodor-Florin Fortis,et al.  A taxonomic view of cloud computing services , 2015, Int. J. Comput. Sci. Eng..

[2]  Preeti Mulay,et al.  Variant of COBWEB Clustering for Privacy Preservation in Cloud DB Querying , 2015 .

[3]  Jie Wu,et al.  Secure and privacy preserving keyword searching for cloud storage services , 2012, J. Netw. Comput. Appl..

[4]  Liqiong Ma.  A new construction of identity-based group signature , 2014, Int. J. Embed. Syst..

[5]  Thomas Hess,et al.  Drivers of SaaS-Adoption – An Empirical Study of Different Application Types , 2009, Bus. Inf. Syst. Eng..

[6]  Brent Waters,et al.  Attribute-based encryption for fine-grained access control of encrypted data , 2006, CCS '06.

[7]  Marten van Dijk,et al.  On the Impossibility of Cryptography Alone for Privacy-Preserving Cloud Computing , 2010, HotSec.

[8]  Yi Mu,et al.  Privacy enhanced data outsourcing in the cloud , 2012, J. Netw. Comput. Appl..

[9]  Li Xu,et al.  Batch public auditing for distributed mobile cloud computing , 2015, Int. J. High Perform. Comput. Netw..

[10]  Ambika Pawar,et al.  Enhancing Privacy-Preserving Cloud Database Querying by Preventing Brute Force Attacks , 2014 .

[11]  Gene Tsudik,et al.  Privacy-Preserving Cloud Database Querying , 2011, J. Internet Serv. Inf. Secur..

[12]  Brent Waters,et al.  Fuzzy Identity-Based Encryption , 2005, EUROCRYPT.

[13]  Adi Shamir,et al.  Identity-Based Cryptosystems and Signature Schemes , 1984, CRYPTO.

[14]  Douglas H. Fisher,et al.  Knowledge Acquisition Via Incremental Conceptual Clustering , 1987, Machine Learning.

[15]  Brent Waters,et al.  Ciphertext-Policy Attribute-Based Encryption , 2007, 2007 IEEE Symposium on Security and Privacy (SP '07).

[16]  Cong Wang,et al.  Achieving Secure, Scalable, and Fine-grained Data Access Control in Cloud Computing , 2010, 2010 Proceedings IEEE INFOCOM.

[17]  Gonzalo Navarro,et al.  A guided tour to approximate string matching , 2001, CSUR.

[18]  Yue Chen,et al.  Rating cloud storage service by collaborative remote data checking , 2015, Int. J. High Perform. Comput. Netw..

[19]  Jindong Li,et al.  A new certificate-based digital signature scheme in bilinear group , 2014, Int. J. Embed. Syst..

[20]  Jin-Young Choi,et al.  Incremental Conceptual Clustering Using a Modified Category Utility , 2005 .

[21]  Jianliang Xu,et al.  Processing private queries over untrusted data cloud through privacy homomorphism , 2011, 2011 IEEE 27th International Conference on Data Engineering.

[22]  Siani Pearson,et al.  A Privacy Manager for Cloud Computing , 2009, CloudCom.

[23]  M. Gluck,et al.  Explaining Basic Categories: Feature Predictability and Information , 1992 .

[24]  Ronald C. Taylor.  An overview of the Hadoop/MapReduce/HBase framework and its current applications in bioinformatics , 2010, BMC Bioinformatics.