RII Track-4:NSF: Federated Analytics Systems with Fine-grained Knowledge Comprehension: Achieving Accuracy with Privacy

RII Track-4：NSF：具有细粒度知识理解的联合分析系统：通过隐私实现准确性

• 批准号: 2327480
• 负责人: Hao Wang
• 金额: $ 30万
• 依托单位: Louisiana State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-02-01 至 2026-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2327480&HistoricalAwards=false
• 关键词: RII; Track; NSF; Federated; Analytics

In parallel with the rapid adoption of big data analytics techniques by various sectors, such as healthcare, advertising, finance, and public transportation, there has been growing awareness and concern about data privacy. Recent developments in the data regulation landscape have prompted a seismic shift towards privacy-preserving data analytics, leading to Federated Analytics (FA), the leading paradigm for collaborative data science without data collection. The core principles of this data analysis paradigm allow for breaking the limitations of deriving analytics from limited centralized data in terms of privacy concerns and operational costs. However, FA systems' distributed nature and non-data-sharing enforcement raise critical challenges in the accuracy and efficiency of data analytics. First, skewed data distribution across participating clients leads to severe bias and inconsistency. Second, privacy-preserving techniques applied to the entire FA process leave poor data utility and analysis efficiency. This project innovates accurate, efficient, and credible FA systems with fine-grained knowledge comprehension to optimize the entire lifecycle of the FA process. This project will establish a solid foundation for long-term collaboration with researchers at IBM T. J. Watson Research Center toward developing privacy-preserving FA solutions. Lastly, the project will train a privacy-preserving data science workforce urgently needed in Louisiana.This Research Infrastructure Improvement Track-4 EPSCoR Research Fellows (RII Track-4) proposal would provide a fellowship to an Assistant Professor and training for a graduate student at Louisiana State University. This project aims to investigate fine-grained knowledge comprehension to optimize the entire FA lifecycle holistically, including data skewness estimation, participant selection, and privacy-preserved analysis. Exploring fine-grained knowledge comprehension will offer new insights to better understand and explain data skewness and utility, privacy-preserved data representations, and analytics results in a distributed non-data-sharing scenario. It will catalyze new FA algorithms and system designs toward optimizing FA performance and security. We decouple our specific research activities into two synergistic aims: (1) Improving FA accuracy with fine-grained data skew awareness by data skewness estimation and adaptive refinement of query and client selection; and (2) Optimizing FA utility with fine-grained privacy preservation by separating common and personal feature representations. The proposed FA systems will be extensively evaluated on realistic large-scale testbeds with public datasets at LSU A&M and IBM's Maximo Application Suite and OpenShift Data Science platform. All datasets, benchmarks, and source code will be released on GitHub for a broader impact. By harnessing fine-grained knowledge comprehension for escalating FA efficiency and privacy, the proposed solutions will push the envelope of FA's capabilities and spur the landscape of FA applications in real-world scenarios.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.

随着医疗保健、广告、金融和公共交通等各个行业迅速采用大数据分析技术，人们对数据隐私的认识和担忧也日益增强。数据监管领域的最新发展促使隐私保护数据分析发生了巨大转变，从而催生了联合分析 (FA)，这是无需数据收集的协作数据科学的领先范例。这种数据分析范式的核心原则可以打破从有限的集中式数据中进行分析在隐私问题和运营成本方面的限制。然而，FA 系统的分布式特性和非数据共享执行对数据分析的准确性和效率提出了严峻的挑战。首先，参与客户之间的数据分布不均会导致严重的偏差和不一致。其次，应用于整个 FA 流程的隐私保护技术导致数据利用率和分析效率较差。该项目通过细粒度的知识理解创新准确、高效、可信的 FA 系统，以优化 FA 流程的整个生命周期。该项目将为与 IBM T. J. Watson 研究中心的研究人员的长期合作奠定坚实的基础，以开发保护隐私的 FA 解决方案。最后，该项目将培训路易斯安那州急需的隐私保护数据科学劳动力。这项研究基础设施改进 Track-4 EPSCoR 研究员 (RII Track-4) 提案将为路易斯安那州立大学的一名助理教授提供奖学金，并为一名研究生提供培训。该项目旨在研究细粒度的知识理解，以整体优化整个 FA 生命周期，包括数据偏度估计、参与者选择和隐私保护分析。探索细粒度的知识理解将为更好地理解和解释分布式非数据共享场景中的数据偏度和效用、隐私保护的数据表示以及分析结果提供新的见解。它将促进新的 FA 算法和系统设计，以优化 FA 性能和安全性。我们将具体的研究活动分解为两个协同目标：（1）通过数据偏斜估计以及查询和客户端选择的自适应细化，通过细粒度数据偏斜意识来提高 FA 准确性； (2)通过分离公共特征表示和个人特征表示，通过细粒度的隐私保护来优化 FA 实用性。拟议的 FA 系统将在 LSU A&M 的公共数据集以及 IBM 的 Maximo 应用套件和 OpenShift 数据科学平台的实际大规模测试台上进行广泛评估。所有数据集、基准测试和源代码都将在 GitHub 上发布，以产生更广泛的影响。通过利用细粒度的知识理解来提升 FA 效率和隐私，所提出的解决方案将突破 FA 能力的极限，并推动 FA 在现实场景中的应用前景。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。