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Title: Privacy preserving data analytics in financial inclusion and crowd computing
Authors: Lim, Cheng Lock
Keywords: Engineering::Computer science and engineering::Data::Data encryption
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Lim, C. L. (2022). Privacy preserving data analytics in financial inclusion and crowd computing. Master's thesis, Nanyang Technological University, Singapore.
Abstract: Banks serve unbanked customers through financial inclusion, and other organizations use crowd computing to support their businesses. Personal and business data are being collected and used for such purposes, and data are usually processed in the clear on servers. Data owners do not have the means to protect their data from potential exposure beyond the intended purpose. This research aims to introduce new architectures and protocols for privacy-preserving data analytics which use Homomorphic encryption for data protection and computation. The first contribution optimizes financial inclusion by conducting a secured credit assessment with encrypted data on edge servers. It achieves reasonable prediction accuracy and response time. The second contribution adopts distributed crowd computing to train a logistic regression model with encrypted data and record activities on a blockchain. The generated prediction results are comparable to the predictive model trained using the full data set.
DOI: 10.32657/10356/158808
Schools: School of Computer Science and Engineering 
Rights: This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Theses

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