Proof systems for scaling blockchains
Abstract/Contents
- Abstract
- The integrity of record-keeping is essential to a functioning society. Our dependence on digital records has become absolute, from our personal wealth, commerce, and identities to sources of knowledge and news. In recent years, blockchains have arisen as a new form of digital record-keeping that is operated by decentralized sets of participating computers. While traditional record-keeping places trust in a centralized service, blockchains remain secure so long as the number of corrupt computers does not exceed a large threshold, an assumption that can also be strengthened via economic incentives. However, this robustness comes at a cost. Blockchains struggle to process updates at the same rate as standard transactional systems. As a result, rising demand has led to congestion and soaring transaction fees. In this dissertation, we construct several cryptographic proof systems that help scale the throughput of blockchains. The first set of tools we construct are authenticated data-structures (ADSs) and succinct non-interactive arguments of knowledge (SNARKs) that leverage new techniques based on groups of unknown order (GUOs) to achieve significantly lower communication than prior methods, without relying on a so-called trusted setup. These tools can be used to reduce the required bandwidth, computation, and storage of nodes participating in the blockchain protocol. However, it is challenging to apply these tools on their own to scale the throughput of blockchains while maintaining the guarantee that critical public data recorded in the blockchain remains available to users. To address this challenge we construct a new type of proof system called proofs of replication (PoReps), which help to ensure data is redundantly stored and available.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2022; ©2022 |
| Publication date | 2022; 2022 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Fisch, Benjamin Aaron |
|---|---|
| Degree supervisor | Boneh, Dan, 1969- |
| Thesis advisor | Boneh, Dan, 1969- |
| Thesis advisor | Reingold, Omer |
| Thesis advisor | Tan, Li-Yang |
| Thesis advisor | Wootters, Mary |
| Degree committee member | Reingold, Omer |
| Degree committee member | Tan, Li-Yang |
| Degree committee member | Wootters, Mary |
| Associated with | Stanford University, Computer Science Department |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Ben Fisch. |
|---|---|
| Note | Submitted to the Computer Science Department. |
| Thesis | Thesis Ph.D. Stanford University 2022. |
| Location | https://purl.stanford.edu/ft145rq9000 |
Access conditions
- Copyright
- © 2022 by Benjamin Aaron Fisch
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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