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.
In spring 2024, this course does not satisfy any Hub units.
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.
In spring 2024, this course does not satisfy any Hub units.
General Information
No information, yet. Stay tuned!
| Name | Office Hours | |
|---|---|---|
Mayank Varia | When? Where? | |
Jida Li | When? Where? | |
Shreyas Sudarsan | When? Where? |