A Deep Learning Model for Information Loss Prevention from Multi-Page Digital Documents

Title

A Deep Learning Model for Information Loss Prevention from Multi-Page Digital Documents

Creator

Guha A.; Samanta D.; Banerjee A.; Agarwal D.

Description

World Wide Web has redefined almost all the business models in the past twenty-five to thirty years. IoT, Big Data, AI are some of the comparatively recent technologies which brought in a revolution in the digitization and management of data. Along with the revolution arose the need for data security and consumer privacy protection, primarily concerning financial institutions. The data breach of Equifax in 2017 and personal information leaks from Facebook in 2021 led to general skepticism among the customers of large corporations. The GLBA, 1999, also known as the Financial Modernization Act, was implemented by US federal law to enforce the financial institutions to protect their private information. Built upon the GLBA, guidelines are paved by FTC for all financial institutions of the United States of America, including TI companies. In this paper, an ANN-based content classification technique using MLP architecture in combination with n-gram TF-IDF feature descriptor is proposed to detect and protect the customers' sensitive information of a reputed TI company securing it's one of the digital image-document stores. The proposed technique is compared with other state-of-the-art strategies. Data samples from the digital document store of the company have been taken into consideration in the study, and the prediction accuracy metrics obtained are found to be substantially better and within the acceptable range defined by the organization's information security monitoring team. 2013 IEEE.

Source

IEEE Access, Vol-9, pp. 80451-80465.

Date

2021-01-01

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

artificial neural network; binary classification; data loss prevention; Information security; multi-layer perceptron; title insurance

Coverage

Guha A., Department of Data Science, CHRIST (Deemed to Be University), Bengaluru, 560029, India, First American India Private Ltd., Bengaluru, 560038, India; Samanta D., Department of Computer Science, CHRIST (Deemed to Be University), Bengaluru, 560029, India; Banerjee A., Department of Electrical and Computer Engineering, Microelectronic Technologies and Devices, National University of Singapore, Singapore, 119077, Singapore; Agarwal D., Department of Materials Science and Engineering, University of Pennsylvania, Philadelphia, 19104, PA, United States, Lam Research Corporation, Fremont, 94538, CA, United States

Rights

All Open Access; Gold Open Access

Relation

ISSN: 21693536

Format

Online

Language

English

Type

Article

Identifier

https://doi.org/10.1109/ACCESS.2021.3084841

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107197542&doi=10.1109%2fACCESS.2021.3084841&partnerID=40&md5=6547c4373370da8e56e5c19b9c78d5df