Cloud BI: A Multi-party Authentication Framework for Securing Business Intelligence on the Cloud

Business intelligence (BI) has emerged as a key technology to be hosted on Cloud computing. BI offers a method to analyse data thereby enabling informed decision making to improve business performance and profitability. However, within the shared domains of Cloud computing, BI is exposed to increased security and privacy threats because an unauthorised user may be able to gain access to highly sensitive, consolidated business information. The business process contains collaborating services and users from multiple Cloud systems in different security realms which need to be engaged dynamically at runtime. If the heterogamous Cloud systems located in different security realms do not have direct authentication relationships then it is technically difficult to enable a secure collaboration. In order to address these security challenges, a new authentication framework is required to establish certain trust relationships among these BI service instances and users by distributing a common session secret to all participants of a session. The author addresses this challenge by designing and implementing a multiparty authentication framework for dynamic secure interactions when members of different security realms want to access services. The framework takes advantage of the trust relationship between session members in different security realms to enable a user to obtain security credentials to access Cloud resources in a remote realm. This mechanism can help Cloud session users authenticate their session membership to improve the authentication processes within multi-party sessions. The correctness of the proposed framework has been verified by using BAN Logics. The performance and the overhead have been evaluated via simulation in a dynamic environment. A prototype authentication system has been designed, implemented and tested based on the proposed framework. The research concludes that the proposed framework and its supporting protocols are an effective functional basis for practical implementation testing, as it achieves good scalability and imposes only minimal performance overhead which is comparable with other state-of-art methods

Towards a theory for privacy preserving distributed OLAP

Privacy Preserving Distributed OLAP identifies a collection of models, methodologies and algorithms devoted to ensuring the privacy of multidimensional OLAP data cubes in distributed environments. While there is noticeable research on practical and pragmatic aspects of Privacy Preserving OLAP, both in centralized and distributed environments, the active literature is lacking of contributions falling in the theory-side of this emerging research topic. Contrary to this, according to our vision, there is a significant need for theoretical results, which may involve in benefits for a wide spectrum of aspects, such as privacy preserving knowledge fruition schemes and query optimization. Inspired by these considerations, starting from our previous research result where the main privacy preserving distributed OLAP framework has been introduced, this paper proposes some theoretical results that nicely extend the capabilities and the potentialities of the framework above