BALANCED AWARE FIREFLY OPTIMIZATION BASED COST-EFFECTIVE PRIVACY PRESERVING APPROACH OF INTERMEDIATE DATA SETS OVER CLOUD COMPUTING

Cloud computing is an embryonic archetype with remarkable impetus; however its exclusive facets intensify safety and privacy confronts. In the previous method, the privacy of intermediate data set problems is dealt with which is concentrated to regain privacy sensitive information. Alternatively the previous system contains problem with time and cost intricacy. As well it contains issue with dealing privacy conscious well-organized scheduling of intermediate data sets in cloud by considering privacy preserving. In order to surmount the above stated problems, in the existing system, enhanced balanced scheduling methodology is presented to get better the cost complexity and privacy preservation. Balanced aware FireFly Optimization (BFFO) is used for proficient privacy conscious data set scheduling. This technique is utilized to discover the resolution that carries out best on poise amongst a set of resolutions with similar execution time. Consequently the research system gives superior privacy preservation and enhanced scheduling cost more willingly than the previous method. The encryption technique is used to guarantee the security and end users decrypted the real information with improved privacy. The experimentation outcome show that the presented method confirms superior privacy, lesser cost, lesser time complexity and proficient storage metrics utilizing BFFO methodology compared to the previous Cost based Heuristic (C_HEU) algorithm

Cloud based privacy preserving collaborative business process management

Through the outstanding efficiency and the general success of cloud computing, cloud-innovations have become an everyday part of the industry. Almost every company has defined business processes to organize its business. The next logical step is to bring business processes to the cloud to exploit the benefits of the cloud and to enable the companies to collaborate with each other in one business process. Such collaborations demand for outstanding privacy and security that has to be provable easily at every time. Every company needs to be able to define its own privacy policies and wants to be able to monitor the compliance with these policies. In this paper we present an approach for privacy preserving cloud based collaborative business process management including an architecture and a privacy modeling approach. We evaluate the architecture and the presented privacy modelling approach by a use-case from the logistics domain and show their feasibility