Date of Award
7-2024
Document Type
Dissertation
Degree Name
Doctor of Business Administration (DBA)
First Advisor
Navid Sabbaghi
Abstract
There has been significant growth in the adoption of blockchain technology mainly due to its inherent advantages, such as decentralization, disintermediation, security, store of value, etc. These characteristics have enhanced the adoption of blockchain which can be noticed in the increasing popularity of its use cases such as cryptocurrencies (Bitcoin and Ether), Non-Fungible Token (NFTs), Decentralized Finance (DeFi), Initial Coin Offerings ICOs, etc. However, despite its promise, there are still some fundamental weaknesses in the workings of blockchain technology. A balanced tweaking of these weaknesses can further enable it to realize its full potential with the attendant multiplier effects on its adoption. Accordingly, it is pertinent to highlight and understand some of these weaknesses - specifically the issues of privacy and the use of blockchain for illicit activities, as well as offer solutions on how these weaknesses can be mitigated.
Extant literature suggests that the transparency and anonymity of blockchain technology are simultaneously the sources of its strengths and weaknesses. Some of this literature has highlighted problems associated with the issues of privacy; with blockchain transactions being potentially prone to worse privacy attacks when compared to traditional financial transactions. However, blockchain privacy issues are an understudied area and limited solutions have been proposed in this area of blockchain research. This thesis will contribute to closing these gaps by highlighting and providing a better understanding of these weaknesses, as well as offering possible solutions.
Specifically, we aim to contribute to the literature on privacy on blockchain through the experimentation of using homomorphic encryption techniques and zero-knowledge proof to enforce the confidentiality of blockchain transactions. We also intend to implement these techniques while upholding all the key characteristics of blockchain technology such as decentralization, tamper-proof, disintermediation, etc. We anticipate that upon enhancing and enforcing privacy on blockchain, bad actors might leverage this system to carry out illicit activities(transactions). As a result, the second part of this thesis will aim to solve this problem by using privacy-preserving machine-learning techniques on encrypted data to help decipher illicit activity. Several machine-learning experiments have been done using Bitcoin datasets provided by elliptic and open-source data from Etherscan with attendant varying results. However, these experiments were done on unencrypted datasets.
Most importantly, extant pieces of literature in this area of study have either looked at the problems associated with privacy issues on the blockchain or the problems of illicit activities (money laundering, malicious attacks, scams, human trafficking, etc.) on the blockchain using unencrypted data. None has looked at both cases simultaneously. Therefore, this thesis intends to contribute to the literature on the use of machine learning techniques to identify anti-money laundering activities on both encrypted and unencrypted datasets. Most importantly, this thesis proposes a system that is poised to simultaneously solve the problem of privacy while also creating a check to stop the use of these privacy enhancements for egregious activities.
Creative Commons License
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Recommended Citation
Ogunyemi, Musa Omobolanle, "Transaction Monitoring and Enforcing Confidentiality on Blockchain Transactions" (2024). Executive DBA Dissertations. 5.
https://digitalcommons.stmarys-ca.edu/executive-dba/5
