Multifactor Based Ensemble Assertion Framework for Privacy Preservation of Various Business Models
Saravanan M.S, M. Kiruthika · 2023
In large enterprises in India having many privacy and integrity issues to solve in the vast number of data communication over Business to Business(B2B) and Business to Consumer (B2C) models. At the same time the system has many notable and predicted privacy threats over different types of networks. This study has a focus on privacy preservation while transferring the data between and within enterprises. The balance between data utility and privacy loss could not be matched on a Bigdata set by the standard models K-anonymity and Differential Privacy. A new framework a Multifactor based Ensemble Assertion Framework (MEAF) for Privacy preservation on large-scale data handled by B2B and B2C data transmission models will be proposed. This “MEAF” framework using Private Aggregation of Teacher Ensembles (PATE) machine learning algorithm will preserve the privacy on various business environment with higher accuracy of 89.82%. Also, this framework will go through various considerations when privacy-preserving systems employ Artificial Intelligence (AI) techniques and set the Objectives and Key Results (OKRs) that are suitable for all client types and typical commercial uses. The research paper also addresses the observations of the suggested framework and associated problems.