4 Search Results for "Hao, Yu"

Document
DOI: 10.4230/LIPIcs.AFT.2024.7
DeFiAligner: Leveraging Symbolic Analysis and Large Language Models for Inconsistency Detection in Decentralized Finance

Authors: Rundong Gan, Liyi Zhou, Le Wang, Kaihua Qin, and Xiaodong Lin

Published in: LIPIcs, Volume 316, 6th Conference on Advances in Financial Technologies (AFT 2024)

Abstract
Decentralized Finance (DeFi) has witnessed a monumental surge, reaching 53.039 billion USD in total value locked. As this sector continues to expand, ensuring the reliability of DeFi smart contracts becomes increasingly crucial. While some users are adept at reading code or the compiled bytecode to understand smart contracts, many rely on documentation. Therefore, discrepancies between the documentation and the deployed code can pose significant risks, whether these discrepancies are due to errors or intentional fraud. To tackle these challenges, we developed DeFiAligner, an end-to-end system to identify inconsistencies between documentation and smart contracts. DeFiAligner incorporates a symbolic execution tool, SEVM, which explores execution paths of on-chain binary code, recording memory and stack states. It automatically generates symbolic expressions for token balance changes and branch conditions, which, along with related project documents, are processed by LLMs. Using structured prompts, the LLMs evaluate the alignment between the symbolic expressions and the documentation. Our tests across three distinct scenarios demonstrate DeFiAligner’s capability to automate inconsistency detection in DeFi, achieving recall rates of 92% and 90% on two public datasets respectively.
Cite as

Rundong Gan, Liyi Zhou, Le Wang, Kaihua Qin, and Xiaodong Lin. DeFiAligner: Leveraging Symbolic Analysis and Large Language Models for Inconsistency Detection in Decentralized Finance. In 6th Conference on Advances in Financial Technologies (AFT 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 316, pp. 7:1-7:24, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{gan_et_al:LIPIcs.AFT.2024.7,
  author =	{Gan, Rundong and Zhou, Liyi and Wang, Le and Qin, Kaihua and Lin, Xiaodong},
  title =	{{DeFiAligner: Leveraging Symbolic Analysis and Large Language Models for Inconsistency Detection in Decentralized Finance}},
  booktitle =	{6th Conference on Advances in Financial Technologies (AFT 2024)},
  pages =	{7:1--7:24},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-345-4},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{316},
  editor =	{B\"{o}hme, Rainer and Kiffer, Lucianna},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.AFT.2024.7},
  URN =		{urn:nbn:de:0030-drops-209431},
  doi =		{10.4230/LIPIcs.AFT.2024.7},
  annote =	{Keywords: Decentralized Finance Security, Large Language Models, Project Review, Symbolic Analysis, Smart Contracts}
}
Document
DOI: 10.4230/LIPIcs.WABI.2024.23
AlfaPang: Alignment Free Algorithm for Pangenome Graph Construction

Authors: Adam Cicherski, Anna Lisiecka, and Norbert Dojer

Published in: LIPIcs, Volume 312, 24th International Workshop on Algorithms in Bioinformatics (WABI 2024)

Abstract
The success of pangenome-based approaches to genomics analysis depends largely on the existence of efficient methods for constructing pangenome graphs that are applicable to large genome collections. In the current paper we present AlfaPang, a new pangenome graph building algorithm. AlfaPang is based on a novel alignment-free approach that allows to construct pangenome graphs using significantly less computational resources than state-of-the-art tools. The code of AlfaPang is freely available at https://github.com/AdamCicherski/AlfaPang.
Cite as

Adam Cicherski, Anna Lisiecka, and Norbert Dojer. AlfaPang: Alignment Free Algorithm for Pangenome Graph Construction. In 24th International Workshop on Algorithms in Bioinformatics (WABI 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 312, pp. 23:1-23:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{cicherski_et_al:LIPIcs.WABI.2024.23,
  author =	{Cicherski, Adam and Lisiecka, Anna and Dojer, Norbert},
  title =	{{AlfaPang: Alignment Free Algorithm for Pangenome Graph Construction}},
  booktitle =	{24th International Workshop on Algorithms in Bioinformatics (WABI 2024)},
  pages =	{23:1--23:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-340-9},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{312},
  editor =	{Pissis, Solon P. and Sung, Wing-Kin},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.WABI.2024.23},
  URN =		{urn:nbn:de:0030-drops-206673},
  doi =		{10.4230/LIPIcs.WABI.2024.23},
  annote =	{Keywords: pangenome, variation graph, genome alignment, population genomics}
}
Document
DOI: 10.4230/LIPIcs.ECOOP.2021.8
Differential Privacy for Coverage Analysis of Software Traces

Authors: Yu Hao, Sufian Latif, Hailong Zhang, Raef Bassily, and Atanas Rountev

Published in: LIPIcs, Volume 194, 35th European Conference on Object-Oriented Programming (ECOOP 2021)

Abstract
This work considers software execution traces, where a trace is a sequence of run-time events. Each user of a software system collects the set of traces covered by her execution of the software, and reports this set to an analysis server. Our goal is to report the local data of each user in a privacy-preserving manner by employing local differential privacy, a powerful theoretical framework for designing privacy-preserving data analysis. A significant advantage of such analysis is that it offers principled "built-in" privacy with clearly-defined and quantifiable privacy protections. In local differential privacy, the data of an individual user is modified using a local randomizer before being sent to the untrusted analysis server. Based on the randomized information from all users, the analysis server computes, for each trace, an estimate of how many users have covered it. Such analysis requires that the domain of possible traces be defined ahead of time. Unlike in prior related work, here the domain is either infinite or, at best, restricted to many billions of elements. Further, the traces in this domain typically have structure defined by the static properties of the software. To capture these novel aspects, we define the trace domain with the help of context-free grammars. We illustrate this approach with two exemplars: a call chain analysis in which traces are described through a regular language, and an enter/exit trace analysis in which traces are described by a balanced-parentheses context-free language. Randomization over such domains is challenging due to their large size, which makes it impossible to use prior randomization techniques. To solve this problem, we propose to use count sketch, a fixed-size hashing data structure for summarizing frequent items. We develop a version of count sketch for trace analysis and demonstrate its suitability for software execution data. In addition, instead of randomizing separately each contribution to the sketch, we develop a much-faster one-shot randomization of the accumulated sketch data. One important client of the collected information is the identification of high-frequency ("hot") traces. We develop a novel approach to identify hot traces from the collected randomized sketches. A key insight is that the very large domain of possible traces can be efficiently explored for hot traces by using the frequency estimates of a visited trace and its prefixes and suffixes. Our experimental study of both call chain analysis and enter/exit trace analysis indicates that the frequency estimates, as well as the identification of hot traces, achieve high accuracy and high privacy.
Cite as

Yu Hao, Sufian Latif, Hailong Zhang, Raef Bassily, and Atanas Rountev. Differential Privacy for Coverage Analysis of Software Traces. In 35th European Conference on Object-Oriented Programming (ECOOP 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 194, pp. 8:1-8:25, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{hao_et_al:LIPIcs.ECOOP.2021.8,
  author =	{Hao, Yu and Latif, Sufian and Zhang, Hailong and Bassily, Raef and Rountev, Atanas},
  title =	{{Differential Privacy for Coverage Analysis of Software Traces}},
  booktitle =	{35th European Conference on Object-Oriented Programming (ECOOP 2021)},
  pages =	{8:1--8:25},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-190-0},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{194},
  editor =	{M{\o}ller, Anders and Sridharan, Manu},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECOOP.2021.8},
  URN =		{urn:nbn:de:0030-drops-140513},
  doi =		{10.4230/LIPIcs.ECOOP.2021.8},
  annote =	{Keywords: Trace Profiling, Differential Privacy, Program Analysis}
}
Document
Artifact
DOI: 10.4230/DARTS.7.2.7
Differential Privacy for Coverage Analysis of Software Traces (Artifact)

Authors: Yu Hao, Sufian Latif, Hailong Zhang, Raef Bassily, and Atanas Rountev

Published in: DARTS, Volume 7, Issue 2, Special Issue of the 35th European Conference on Object-Oriented Programming (ECOOP 2021)

Abstract
We propose a differentially private coverage analysis for software traces. To demonstrate that it achieves low error and high precision while preserving privacy, we evaluate the analysis on simulated traces for 15 Android apps. The open source implementation of the analysis, which is in Java, and the dataset used in the experiments are released as an artifact. We also provide specific guidance on reproducing the experimental results.
Cite as

Yu Hao, Sufian Latif, Hailong Zhang, Raef Bassily, and Atanas Rountev. Differential Privacy for Coverage Analysis of Software Traces (Artifact). In Special Issue of the 35th European Conference on Object-Oriented Programming (ECOOP 2021). Dagstuhl Artifacts Series (DARTS), Volume 7, Issue 2, pp. 7:1-7:3, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@Article{hao_et_al:DARTS.7.2.7,
  author =	{Hao, Yu and Latif, Sufian and Zhang, Hailong and Bassily, Raef and Rountev, Atanas},
  title =	{{Differential Privacy for Coverage Analysis of Software Traces (Artifact)}},
  pages =	{7:1--7:3},
  journal =	{Dagstuhl Artifacts Series},
  ISSN =	{2509-8195},
  year =	{2021},
  volume =	{7},
  number =	{2},
  editor =	{Hao, Yu and Latif, Sufian and Zhang, Hailong and Bassily, Raef and Rountev, Atanas},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DARTS.7.2.7},
  URN =		{urn:nbn:de:0030-drops-140319},
  doi =		{10.4230/DARTS.7.2.7},
  annote =	{Keywords: Trace Profiling, Differential Privacy, Program Analysis}
}
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