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        <identifier>oai:drops-oai.dagstuhl.de:20942</identifier>
        <datestamp>2024-09-16T10:19:08Z</datestamp>
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          <dc:title>Analyzing and Benchmarking ZK-Rollups</dc:title>
          <dc:creator>Chaliasos, Stefanos</dc:creator>
          <dc:creator>Reif, Itamar</dc:creator>
          <dc:creator>Torralba-Agell, Adrià</dc:creator>
          <dc:creator>Ernstberger, Jens</dc:creator>
          <dc:creator>Kattis, Assimakis</dc:creator>
          <dc:creator>Livshits, Benjamin</dc:creator>
          <dc:subject>Zero-Knowledge Proofs</dc:subject>
          <dc:subject>ZK-Rollups</dc:subject>
          <dc:subject>Benchmarking</dc:subject>
          <dc:subject>Blockchain Scalability</dc:subject>
          <dc:description>As blockchain technology continues to transform the realm of digital transactions, scalability has emerged as a critical issue. This challenge has spurred the creation of innovative solutions, particularly Layer 2 scalability techniques like rollups. Among these, ZK-Rollups are notable for employing Zero-Knowledge Proofs to facilitate prompt on-chain transaction verification, thereby improving scalability and efficiency without sacrificing security. Nevertheless, the intrinsic complexity of ZK-Rollups has hindered an exhaustive evaluation of their efficiency, economic impact, and performance.&#13;
This paper offers a theoretical and empirical examination aimed at comprehending and evaluating ZK-Rollups, with particular attention to ZK-EVMs. We conduct a qualitative analysis to break down the costs linked to ZK-Rollups and scrutinize the design choices of well-known implementations. Confronting the inherent difficulties in benchmarking such intricate systems, we introduce a systematic methodology for their assessment, applying our method to two prominent ZK-Rollups: Polygon zkEVM and zkSync Era. Our research provides initial findings that illuminate trade-offs and areas for enhancement in ZK-Rollup implementations, delivering valuable insights for future research, development, and deployment of these systems.</dc:description>
          <dc:publisher>Schloss Dagstuhl – Leibniz-Zentrum für Informatik</dc:publisher>
          <dc:contributor>Stefanos Chaliasos and Itamar Reif and Adrià Torralba-Agell and Jens Ernstberger and Assimakis Kattis and Benjamin Livshits</dc:contributor>
          <dc:date>2024</dc:date>
          <dc:relation>Is Part Of LIPIcs, Volume 316, 6th Conference on Advances in Financial Technologies (AFT 2024)</dc:relation>
          <dc:type>InProceedings</dc:type>
          <dc:type>Text</dc:type>
          <dc:type>doc-type:ResearchArticle</dc:type>
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          <dc:identifier>doi:10.4230/LIPIcs.AFT.2024.6</dc:identifier>
          <dc:identifier>urn:nbn:de:0030-drops-209420</dc:identifier>
          <dc:identifier>https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.AFT.2024.6</dc:identifier>
          <dc:language>eng</dc:language>
          <dc:rights>https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
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