Fall 2021, University of Manitoba, Canada

Over the last couple of years, Bitcoin cryptocurrency and the Blockchain technology that forms the basis of Bitcoin have witnessed an unprecedented attention. As Blockchain applications proliferate, so does the complexity and volume of data stored by Blockchains. Analyzing this data has emerged as an important research topic, already leading to methodological advancements in the information sciences. Although there is a vast quantity of information available, the consequent challenge is to develop tools and algorithms to analyze the large volumes of user-generated content and transactions on Blockchains, to glean meaningful insights from Blockchain data. The objective of the course is to train students in data collection, cleaning and modeling for Blockchain Data Analytics on public blockchains, such as Bitcoin and Ethereum. We will also cover the Libra blockchain of Facebook.

This class requires some reading. Students will read book chapters, more than 5 research papers, surveys and articles. In addition, there will be a Data Science project where students will implement algorithms. Please make sure that you have given enough thought to these requirements.

Students will work on a course-long research project. Each project will be presented in the form of a poster in the last class. The software code will be contributed to the open source Chainoba project on Github.

Students will be able to achieve the following learning objectives at the completion of the course:

• Learn the history of digital currencies and problems that prevented their adoption. What are the real-life use cases of Blockchain? How Blockchain differs from earlier solutions?
• Learn the concepts of consensus and proof-of-work in distributed computing to understand and describe how blockchain works.
• Learn data models for addresses, transactions and blocks in cryptocurrencies and Blockchain platforms.
• Use Python and R to extract Blockchain blocks and store the transaction network on Bitcoin, Ripple, IOTA and Ethereum Blockchains.
• Model weighted, directed multi-graph Blockchain networks and use graph mining algorithms to identify influential users and their transactions.
• Predict cryptocurrency and crypto-asset prices in real time.
• Extract and mine Smart Contracts on the Ethereum blockchain.

The book "Blockchain: Fundamentals, Data Structures and Algorithms for Data Science (Cambridge University Press)" by Cuneyt Gurcan Akcora, Yulia R. Gel and, Murat Kantarcioglu will be provided in e-copy and used as the course book.

Past research projects 2021

• Bitcoin darknet market analysis 1. https://github.com/youn0808/BlockchainDataAnalytics
• Bitcoin darknet market analysis 2. https://github.com/azad-p/BlockchainNetwork
• Bitcoin darknet market analysis 3. https://github.com/jeeerdin/COMP7570-Group-Project
• Bitcoin ransom analysis 1. https://github.com/japjeet26/BlockchainDataProject
• Bitcoin ransom analysis 2.

2020

• Ripple Payment Graph Analysis. https://github.com/monowar-1994/RippleProject/
• Ripple Trust Graph Analysis. https://github.com/akaler55/ripple_network_analysis
• MakerDAO Ecosystem Analysis. https://github.com/MCSH/dao-analysis-7570
• Bitcoin Darknet Market Transaction Fingerprinting. https://github.com/olayinkade/AdversarialMatching
• IOTA Transaction Graph Analysis. https://github.com/Arezoovina/IOTA-7570Blockchain

Week 1 Networks, linking and consensus

• Bitcoin's Academic Pedigree, Narayanan.
• BlockSci: Design and applications of a blockchain analysis platform, Kalodner.
• Blockchain Consensus Protocols in the Wild, Cachin.

Week 2-3 Bitcoin

• Bitcoin: A peer-to-peer electronic cash system, Nakamoto.
• Blockchain: a graph primer, Akcora.
• Core Concepts, Challenges, and Future Directions in Blockchain: A Centralized Tutorial, Kolb.

Week 4-7 Ethereum

• Ethereum: A Next-Generation Smart Contract and Decentralized Application platform, Butterin.

Week 8 IOTA, Ripple

• A Fistful of Bitcoins: Characterizing Payments Among Men with No Names, Meiklejohn.
• Tracing Transactions Across Cryptocurrency Ledgers, Meiklejohn.

Week 9 Privacy coins, Lightning Network

• BitcoinHeist: Topological data analysis for ransomware detection on the Bitcoin blockchain, Akcora.

Week 10 Ripple, IOTA

Week 11 Next generation blockchains, security/privacy.

Week 12-13 Project presentations

15% quizzes, 35% final exam, 15% paper summaries, 35% research project. Grades will be assigned as follows: A+ ≥ 92, A ≥ 85, B+ ≥ 75, C+ ≥ 65, C ≥ 60, D ≥ 50, B ≥ 70, F < 50

Course lectures will be driven by the contents of assigned papers. However, students are going to be required to

1. Turn in a 1 page summary of reading assigned each week
2. Participate in discussions of the paper content during each class
3. Write notes synthesizing the content of class for one class.

Hence, the students' ability to exhibit comprehension of papers is essential to a passing grade.

Late submissions will not be accepted. Students with legitimate reasons who contact the professor before the deadline may apply for an extension.

1. Students are encouraged to discuss course concepts and the general interpretation of project problems with other students in the class.
2. In the project, any code copied from the Internet must be properly cited in code, and explained in the project report. Code plagiarism without correct attribution must be avoided.
3. Following conventions for citing reference materials in scientific writing is mandatory. Submitting the work of another person as your own constitutes academic misconduct.
4. Students are to abide by the university's policies regarding academic dishonesty which can be found on this web site.