Privacy and Memory Concerned Intermediate Data Handling in Cloud Computing Environment

Sini S Nair and Dr.T. Santha

The main advantage of Cloud Computing is that it gives the user humongous storage capacity and computation power. Cloud computing helps in deploying applications that require high computation with relatively lesser investment. Cloud computing works efficiently as the data sets are stored efficiently and safely to avoid the cost of re-computing. The data sets are to be preserved privately for the effective usage of resources in cloud computing. They couldbecome a challengewhen adversaries try to recover privacy-sensitive information. The encryption of all the datasets would ensure that this challenge is mitigated effectively. Encrypting all the intermediate data sets are not economical due the duration of time and the humongous expenses involved. The time and cost constraints apply to the applications thatcarry out encryption or decryption in the data sets while carrying out different operations. In the existing research, aheuristic algorithm has been used to find the best intermediate dataset to enhance the privacy preservation. The problems found in the existing research methodologies areheuristic in nature and optimization algorithms can be used to determine a solution. As optimization algorithms do not guarantee the best solution, they are considered as just approximations. The current research does not focus on the memory and computation optimization withlarge volume of data sets. In the proposed research, a method for secure and reliable handling of sensitive data sets has been discussed extensively. The framework used is named as the Privacy and Memory concerned Intermediate Data Handling Framework (PMIDHF). An optimization algorithm called the Hybridized Particle Swarm Optimization with Krill Herd (HPSOKH) is introduced here for the selection of the intermediate data sets. Here, the time frame for getting the intermediate data set is considered as the fitness value initially. The discussed methodology ensures better privacy preservation. In the existing system, along with the privacy violation metrics, memory is also considered as a variable for optimizing the stored values. This is done by dividing the selected intermediate datasets into multiple segments and avoiding any form of repetition before the encryptionoccurs. The overall evaluation of the research method is finally carried out in the java simulation environment. The results indicate that the proposed method leads to optimal outcomes when compared toexisting methods of data handling in cloud environments.

Volume 12 | 01-Special Issue

Pages: 337-347

DOI: 10.5373/JARDCS/V12SP1/20201080