2019/11/09 by Reginald Bryant, Bryant, Reginald, Celia Cintas +7
Computer Science · #Blockchain Technology Applications and Security #Cryptography and Security (cs.CR) #Databases (cs.DB) #Digital and Cyber Forensics #FOS: Computer and information sciences #Machine Learning (cs.LG) #Privacy-Preserving Technologies in Data
paper · pdf · doi:10.48550/arxiv.1911.03623
openalex publication_date 2019/11/09 · openalex created_date 2019/11/22 · openalex updated_date 2026/07/28
Bias in data can have unintended consequences that propagate to the design, development, and deployment of machine learning models. In the financial services sector, this can result in discrimination from certain financial instruments and services. At the same time, data privacy is of paramount importance, and recent data breaches have seen reputational damage for large institutions. Presented in this paper is a trusted model-lifecycle management platform that attempts to ensure consumer data protection, anonymization, and fairness. Specifically, we examine how datasets can be reproduced using deep learning techniques to effectively retain important statistical features in datasets whilst simultaneously protecting data privacy and enabling safe and secure sharing of sensitive personal information beyond the current state-of-practice.