Visible to the public Big Data Encryption Technology Based on ASCII And Application On Credit Supervision

TitleBig Data Encryption Technology Based on ASCII And Application On Credit Supervision
Publication TypeConference Paper
Year of Publication2020
AuthorsMeng, C., Zhou, L.
Conference Name2020 International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE)
Date PublishedJune 2020
PublisherIEEE
ISBN Number978-1-7281-6499-1
Keywordsartificial intelligence, ASCII, Big Data, Big Data encryption technology, big data privacy, BIGDATA, Conferences, credit construction, Credit Supervision, credit supervision application, Cross-Platform, cryptography, data desensitization technology, Data Governance, data leakage, data privacy, data utilization, Desensitization, electronic data interchange, Electronic mail, financial data processing, Human Behavior, Internet of Things, Licenses, market supervision business, Metrics, pubcrawl, resilience, Resiliency, Scalability, user payment information, user privacy
Abstract

Big Data Platform provides business units with data platforms, data products and data services by integrating all data to fully analyze and exploit the intrinsic value of data. Data accessed by big data platforms may include many users' privacy and sensitive information, such as the user's hotel stay history, user payment information, etc., which is at risk of leakage. This paper first analyzes the risks of data leakage, then introduces in detail the theoretical basis and common methods of data desensitization technology, and finally puts forward a set of effective market subject credit supervision application based on asccii, which is committed to solving the problems of insufficient breadth and depth of data utilization for enterprises involved, the problems of lagging regulatory laws and standards, the problems of separating credit construction and market supervision business, and the credit constraints of data governance.

URLhttps://ieeexplore.ieee.org/document/9196362
DOI10.1109/ICBAIE49996.2020.00023
Citation Keymeng_big_2020