Description
CDS DS 453 investigates techniques for performing trustworthy data analyses without a trusted party, and for conducting data science without data sharing.
The first half of the course investigates cryptocurrencies, the blockchain technology underpinning them, and the incentives for each participant. Students will learn how to create transactions, develop smart contracts, and participate in decentralized exchanges. Then, we take a deeper dive into consensus mechanisms, historical and modern, that maintain stability if a certain fraction of the participants or computing power behaves honestly.
The second half of the course focuses on privacy and anonymity using advanced tools from cryptography. We study zero knowledge proofs and their role in preventing re-identification attacks and increasing scalability of blockchains. We also study secure multiparty computation and its role in designing private contracts and atomic swaps. The course concludes with a broader exploration into the power of conducting data science without being able to see the underlying data.
Within the undergraduate Data Sciences major, this course satisfies the DS methodology elective in the “scalable & trustworthy DS” category.
The first half of the course investigates cryptocurrencies, the blockchain technology underpinning them, and the incentives for each participant. Students will learn how to create transactions, develop smart contracts, and participate in decentralized exchanges. Then, we take a deeper dive into consensus mechanisms, historical and modern, that maintain stability if a certain fraction of the participants or computing power behaves honestly.
The second half of the course focuses on privacy and anonymity using advanced tools from cryptography. We study zero knowledge proofs and their role in preventing re-identification attacks and increasing scalability of blockchains. We also study secure multiparty computation and its role in designing private contracts and atomic swaps. The course concludes with a broader exploration into the power of conducting data science without being able to see the underlying data.
Within the undergraduate Data Sciences major, this course satisfies the DS methodology elective in the “scalable & trustworthy DS” category.
General Information
Meeting times
Lectures are on Tuesdays and Thursdays at 9:30-10:45am in CCDS room 164. Discussion sections are on Mondays at 9:05-9:55am or 10:10-11:00am, also in CCDS 164. In case you miss a lecture or discussion section, we will post video links in the PIazza course schedule.
Gradescope link
Academic honesty policy
You must adhere to BU’s Academic Conduct Code at all times. Please be sure to read it here: https://www.bu.edu/academics/policies/academic-conduct-code. In particular: cheating on an exam, passing off another student’s work as your own, or plagiarism of writing or code will result in receiving a score of F on the current assignment, and may be grounds for a grade reduction in the course and referral to BU’s Academic Conduct Committee. If you have any questions about the policy, please ask me in person or via a private Piazza note before taking an action that might be a violation.
Collaboration policy
The goal of homework and project assignments is to learn. Hence, I encourage you to use any and all resources that can help you to learn the material: computers/calculators, Piazza, lecture notes, textbooks, other websites, and your fellow classmates. That said, please always obey the following rules:
- In your submission, you must list (a) names of classmates you worked with, (b) any websites you used besides the ones listed in the lecture notes or textbooks, and (c) any code that you used from other sources. Taking ideas without attribution will be considered plagiarism. You are graded on your original work.
- You cannot copy solutions from anyone else, or give your solutions to a classmate to copy.
The goal of the exams is for you to show me what you have learned. As a result, any collaboration is strictly prohibited; exams must reflect solo work. (But I encourage you to work with classmates in preparing for the exams.)
- In your submission, you must list (a) names of classmates you worked with, (b) any websites you used besides the ones listed in the lecture notes or textbooks, and (c) any code that you used from other sources. Taking ideas without attribution will be considered plagiarism. You are graded on your original work.
- You cannot copy solutions from anyone else, or give your solutions to a classmate to copy.
The goal of the exams is for you to show me what you have learned. As a result, any collaboration is strictly prohibited; exams must reflect solo work. (But I encourage you to work with classmates in preparing for the exams.)
| Name | Office Hours | |
|---|---|---|
Mayank Varia | When? Where? | |
Nicolas Alhaddad | When? Where? |
Lecture and Lab Notes
Lecture and Lab Notes
Lecture Date
May 2, 2023
May 1, 2023
Mar 28, 2023
Apr 24, 2023
Apr 20, 2023
Apr 18, 2023
Apr 13, 2023
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Mar 28, 2023
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Mar 2, 2023
Feb 27, 2023
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Feb 14, 2023
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Feb 6, 2023
Feb 2, 2023
Jan 31, 2023
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Jan 23, 2023
Jan 19, 2023