Bibliographic record
Abstract
The celebrated Nakamoto consensus protocol, introduced in 2008, has ushered in severalnew consensus applications, most popularly cryptocurrencies like Bitcoin and Ethereum. There has since been a spark in this new area of study in both academia and industry, including many new systems which are now part of a multi-billion dollar industry. At their heart, these protocols implement a public and immutable record of transactions known as the blockchain. The main promise and appeal of such blockchain systems is decentralized governance in an open and distributed network. I present this thesis as a comprehensive study of whether or not these systems are living up to that promise. In the first part of this thesis, I focus on one of these systems: Ethereum, the second largest cryptocurrency by market capitalization. I present several measurement studies, focusing on several aspects of the network and its history. My work covers how protocol changes have historically impacted the network, how users are utilizing the blockchain in their transactions, and how the peer-to-peer network responsible for disseminating the messages in the network is operating. Across all of these studies, I observe centralizing behaviors in the network linked to peer behavior, barriers of entry into the system, and miner incentives. Based on these observations, in the second part of this thesis I ask the following questions: (1) What are the conditions under which blockchain consistency can be maintained? (2) Are there protocols that will enable clients to join the system in a lightweight manner without giving up trust? And (3) What role does the mining reward function play in incentivizing more decentralized miner participation? I address these foundational questions through rigorous theoretical analyses of existing protocols, and by developing new protocols with provable guarantees. My results include a new Markov framework for analyzing consistency guarantees for several blockchain models, a lightweight transaction verification protocol, and a class of mining reward functions that promote more decentralized miner participation. In summary, the main contributions of this thesis are extensive measurement studies of blockchain systems that expose certain security vulnerabilities and centralizing factors, and the development of analytical tools and protocols to mitigate them.--Author's abstract
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".