A Machine Learning Algorithm TsF K-NN Based on Automated Data Classification for Securing Mobile Cloud Computing Model
Anunaya Inani, Chakradhar Verma, Suvrat Jain · 2019 IEEE 4th International Conference on Computer and Communication Systems (ICCCS) · 2019
Mobile cloud computing (MCC) is fastest growing technology era in which the research society has recently embarked. Today, Mobile data can include financial transactions such as electronic payments, M-wallets and sensitive multimedia contents. The explosive volumes of mobile devices personal data, bring-up more attention to securely data storage rather than consideration on data privacy and confidentiality levels. In this scenario Machine Leaning (ML) brings an important role in the electronic data management. It is always expensive and hard to manage the data manually without adopting machine learning techniques using metadata. Many Machine Learning algorithms have been proposed to comprehend diverse data management issues, yet the forecast of the top secret data and public data in a document is as yet a challenging exploration task. The contribution of this research article is to demonstrate a securing mobile data storage secrecy and privacy in cloud communication framework in terms of automatic data classification using mobile training datasets with help of Training dataset Filtration Key Nearest Neighbor (TsF-KNN) classifier which classifies the data based on the confidentiality level of the record with higher accuracy and powerful timelines as compared to the traditional K-NN algorithms and securing such confidential data category afterwards by applying various existing cryptographic solutions to assuring data privacy and confidentiality levels and simulation results demonstrates that reducing the overall cost and minimize procedural time, increasing system performance and sustainability.