Abstract: Cloud computing is an internet based new age computer technology which can store one’s applications and databases from distance through network and various devices in one location and provide authorised customers with on demand accessibility. Parallel dataflow programs generate enormous amounts of distributed data that are short lived, yet are critical for completion of job and for good run time performance. Preserving secrecy of these datasets has become risky as adversaries may recover confidential information by analyzing such multiple resultant datasets. In cloud storage service users upload their datasets together with authentication information to cloud storage server. Highly scalable computing resources are supplied as an outer service through Internet on pay for what you use basis. Encryption works well for preserving the privacy of resultant datasets, but encrypting all the datasets in cloud is widely adopted in existing approaches which is time consuming and cost in-effective. We propose a novel upper-bound privacy leakage constraint based approach that identifies all functionally encrypt able sensitive data, so that privacy preserving cost can be saved while simultaneously satisfying privacy requirements of data owners. Evaluation results demonstrate that this upper-bound approach is better than existing ones where all datasets are encrypted.
Keywords: sensitive-data leakage, resultant datasets, upper-limit constraint, privacy saving cost.
Title: Efficiently Preserving Of Sensitive Resultant Cloud Datasets
Author: Smt. Sandhya, Prof S.M Joshi
International Journal of Computer Science and Information Technology Research
ISSN 2348-1196 (print), ISSN 2348-120X (online)
Research Publish Journals
