OAC Core: Small: Collaborative Research: Data Provenance Infrastructure towards Robust andReliable Data Sharing and Analytics

OAC 核心：小型：协作研究：数据来源基础设施实现稳健可靠的数据共享和分析

• 批准号: 1908021
• 负责人: Yonghwi Kwon
• 金额: $ 25万
• 依托单位: University of Virginia Main Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-15 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1908021&HistoricalAwards=false
• 关键词: OAC; Core; Small; Collaborative; Research

The rapidly increasing number of interconnected devices and systems brings unprecedented collaborative opportunities between researchers, business partners, and healthcare organizations that can extend discoveries beyond those derivable from any single study. For instance, data sharing between various medical organizations can enhance understanding of the results from an individual clinical by polling of results from various trials from multiple organizations, and thus it enables extending the analysis of treatment options and accelerating biomedical research. While a vast amount of data collected from various sources brings us benefits, it imposes, at the same time, an important challenge of ensuring trustworthiness and quality of data due to the integration of disparate data from various sources. Furthermore, faulty, improperly configured, or broken sensors, as well as buggy or compromised data processing units, can severely affect the quality of data and the analyzed results. This project proposes to develop an infrastructure that provides robust, fine-grain, and end-to-end provenance for collaborative data sharing and analytics. The outcome of this research will directly serve as the foundation of trustworthy data sharing and analytic infrastructures by providing robust and attack/fault-resilient fine-grain provenance of the shared data by creating fine-grain end-to-end data provenance framework for diverse communication infrastructures.In this project, the PIs will develop an end-to-end data provenance framework that provides robust and fine-grain data lineage for trustworthy data sharing and analytic infrastructures. First, they will develop a scalable and reliable infrastructure for collecting data provenance for distributed interconnected devices that can derive a concise provenance data in various environments of distributed devices (e.g., devices using diverse hardware and software platforms). Next, they will design and implement a framework to enable proper derivation and propagation of fine-grain provenance records for data sharing, processing, and analytics. It will provide services for provenance tracking as well as provenance record processing when the data are aggregated, analyzed, and processed (e.g., merge, split, duplicate, delete, extract, or statistical analytics). The PIs also plan to develop a system that can analyze and visualize the complete lineage of data to measure the quality of data and conduct various root cause analyses to identify the fundamental reasons behind data quality issues. The system will be capable of handling a large amount of data generated over a long period of time across multiple devices.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

互联设备和系统数量的迅速增加为研究人员、商业合作伙伴和医疗机构之间带来了前所未有的合作机会，这些合作机会可以将发现扩展到任何单一研究之外。例如，各种医疗机构之间的数据共享可以通过对来自多个组织的各种试验的结果进行轮询来增强对单个临床结果的理解，从而能够扩展对治疗方案的分析并加速生物医学研究。虽然从各种来源收集的大量数据为我们带来了好处，但由于整合了来自各种来源的不同数据，因此它同时也带来了确保数据可信度和质量的重要挑战。此外，有故障、配置不当或损坏的传感器，以及有缺陷或受损的数据处理单元，都可能严重影响数据质量和分析结果。该项目建议开发一个基础设施，为协作数据共享和分析提供健壮、细粒度和端到端的出处。这项研究的成果将直接作为可靠的数据共享和分析基础设施的基础，通过为不同的通信基础设施创建细粒度的端到端数据出处框架，为共享数据提供健壮的和攻击/故障弹性的细粒度出处。PI将开发一个端到端的数据起源框架，为可靠的数据共享和分析基础设施提供健壮和细粒度的数据沿袭。首先，他们将开发一个可扩展的和可靠的基础设施，用于收集分布式互联设备的数据出处，这些设备可以在分布式设备的各种环境中导出简明的出处数据（例如，使用不同硬件和软件平台的设备）。接下来，他们将设计和实现一个框架，以实现细粒度出处记录的正确推导和传播，以实现数据共享、处理和分析。它将在数据被聚合、分析和处理时提供来源跟踪和来源记录处理服务（例如，合并、拆分、复制、删除、提取或统计分析）。PI还计划开发一个系统，可以分析和可视化完整的数据谱系，以衡量数据质量，并进行各种根本原因分析，以确定数据质量问题背后的根本原因。该系统将能够处理在多个设备上长时间产生的大量数据。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

SWARMFLAWFINDER: Discovering and Exploiting Logic Flaws of Swarm Algorithms

• DOI: 10.1109/sp46214.2022.9833685
• 发表时间: 2022-05
• 期刊: 2022 IEEE Symposium on Security and Privacy (SP)
• 作者: Chi-Gon Jung;A. Ahad;Yuseok Jeon;Yonghwi Kwon

Security Analysis on Practices of Certificate Authorities in the HTTPS Phishing Ecosystem

HTTPS钓鱼生态系统中证书颁发机构的做法安全分析

• DOI: 10.1145/3433210.3453100
• 发表时间: 2021
• 期刊: The 2021 ACM Asia Conference on Computer and Communications Security (ASIA CCS'21
• 作者: Kim, Doowon;Cho, Haehyun;Kwon, Yonghwi;Doupé, Adam;Son, Sooel;Ahn, Gail-Joon;Dumitras, Tudor
• 通讯作者: Dumitras, Tudor

Spinner: Automated Dynamic Command Subsystem Perturbation

Spinner：自动动态命令子系统扰动

• 发表时间: 2021
• 期刊: ACM CCS (Accepted
• 作者: Wang, Meng;Jung, Chijung;Ahad, Ali;Kwon, Yonghwi
• 通讯作者: Kwon, Yonghwi

SoftMark: Software Watermarking via a Binary Function Relocation

• DOI: 10.1145/3485832.3488027
• 发表时间: 2021-12
• 期刊: Proceedings of the 37th Annual Computer Security Applications Conference
• 作者: Hong-Yol Kang;Yonghwi Kwon;Sangjin Lee;Hyungjoon Koo

BFTDETECTOR: Automatic Detection of Business Flow Tampering for Digital Content Service

• DOI: 10.1109/icse48619.2023.00048
• 发表时间: 2023-05
• 期刊: 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE)
• 作者: I. L. Kim;Weihang Wang;Yonghwi Kwon;X. Zhang