Schlosser, Benjamin (2024)
Improving Scalability, Privacy, and Decentralization of Blockchains and their Applications via Multiparty Computation.
Technische Universität Darmstadt
doi: 10.26083/tuprints-00028820
Ph.D. Thesis, Primary publication, Publisher's Version
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Item Type: | Ph.D. Thesis | ||||
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Type of entry: | Primary publication | ||||
Title: | Improving Scalability, Privacy, and Decentralization of Blockchains and their Applications via Multiparty Computation | ||||
Language: | English | ||||
Referees: | Faust, Prof. Sebastian ; Hazay, Prof. Carmit | ||||
Date: | 10 December 2024 | ||||
Place of Publication: | Darmstadt | ||||
Collation: | 268 Seiten in verschiedenen Zählungen | ||||
Date of oral examination: | 27 August 2024 | ||||
DOI: | 10.26083/tuprints-00028820 | ||||
Abstract: | Since the advent of Bitcoin in 2008, a myriad of blockchain systems have emerged. Blockchains provide decentralized systems aiming to remove any trust in centralized parties. While Bitcoin provides simple money transfer and rudimentary scripting capabilities, other blockchains like Ethereum support the execution of complex smart contracts. Smart contrast sparked the invention of many new applications over blockchains, with decentralized finance (DeFi) being one of the most prominent. By moving financial services and products to decentralized and open blockchains, DeFi has the potential to democratize the financial market. While showing a promising feature, state-of-the-art blockchains still suffer from limitations and open problems. Limited scalability prevents mass adaption since the number of tolerable actions within the system is too low. Additionally, many systems lack strong privacy features, preventing their applicability to applications with high privacy requirements, like in the healthcare sector. Despite these open problems, blockchains are used in more and more new contexts due to their attractive features based on their decentralized nature. One example is the concept of self-sovereign identities (SSI), where blockchains provide decentralized storage of public metadata. In many new contexts, blockchains are paired with additional components, often not explicitly designed for blockchain applications. Hence, it remains an open problem to align these components with the fundamental idea of blockchains, i.e., removing trust in centralized parties. In this thesis, we significantly contribute to the design of new solutions to all three mentioned problems. More concretely, we tackle the scalability and privacy problem and mitigate the trust in centralized parties in a new component combined with blockchains. Our main building block in all our contributions is secure multiparty computation (MPC), which allows distrusting parties to compute on private data without leaking anything except the output of the computation. First, we present a new off-chain protocol that supports the execution of smart contracts. Since prior work suffers from different shortcomings, our solution addresses them all simultaneously. Second, we use MPC to facilitate private computation for blockchains. To do so, we consider a security model that provides a trade-off between efficiency and security. For this setting, we propose further efficiency improvements, present a compiler for enhancing security, and propose a protocol to combine MPC with blockchains. Our final result allows parties to perform computation privately, and the computation's result defines a distribution of coins. Third, we look at anonymous credentials, an essential component of self-sovereign identities. We present a distributed issuance protocol for anonymous credentials based on the BBS+ signature scheme. |
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Status: | Publisher's Version | ||||
URN: | urn:nbn:de:tuda-tuprints-288204 | ||||
Classification DDC: | 000 Generalities, computers, information > 004 Computer science | ||||
Divisions: | 20 Department of Computer Science > Angewandte Kryptographie | ||||
Date Deposited: | 10 Dec 2024 13:49 | ||||
Last Modified: | 11 Dec 2024 08:22 | ||||
URI: | https://tuprints.ulb.tu-darmstadt.de/id/eprint/28820 | ||||
PPN: | 524505640 | ||||
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