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.

与医疗保健，广告，财务和公共交通等各个部门迅速采用大数据分析技术的同时，人们对数据隐私的认识和关注程度越来越大。数据调节格局的最新发展促使人们向隐私保护数据分析进行了地震转变，从而导致联合分析（FA）是协作数据科学的领先范式，而无需数据科学。此数据分析范式的核心原则允许从有限的集中数据中，从隐私问题和运营成本中分析分析的局限性。但是，FA Systems的分布性质和非DATA共享执法提出了数据分析的准确性和效率的关键挑战。首先，跨参与客户偏斜的数据分布会导致严重的偏见和不一致。其次，应用于整个FA过程的隐私技术使数据实用程序和分析效率差。该项目以精细的知识理解来创新准确，高效和可信的FA系统，以优化FA过程的整个生命周期。该项目将与IBM T. J. Watson Research Center的研究人员长期合作建立坚实的基础，以开发保护隐私的FA解决方案。最后，该项目将在路易斯安那州急需培训一项迫切需要的隐私数据科学劳动力。这项研究基础设施改进Track-4 Epscor Research Fellows（RII Track-4）提案将为路易斯安那州立大学的研究生助理教授和培训提供奖学金。该项目旨在调查细粒度的知识理解，以整体上优化整个FA生命周期，包括数据偏度估计，参与者选择和保留隐私分析。探索细粒度的知识理解将提供新的见解，以更好地理解和解释数据偏斜和实用程序，保留隐私的数据表示以及分析导致分布式非数据共享场景。它将催化新的FA算法和系统设计，以优化FA性能和安全性。我们将特定的研究活动分解为两个协同的目的：（1）通过数据偏度估计和对查询和客户选择的适应性完善，通过细粒度的数据偏斜意识提高FA准确性； （2）通过分开共同的和个人功能表示形式来优化使用细粒度的隐私保护的FA实用程序。拟议的FA系统将在LSU A＆M和IBM的Maximo Application Suite和OpenShift Data Science Platform上的公共数据集中对现实的大型测试台进行广泛评估。所有数据集，基准和源代码都将在GitHub上发布，以产生更大的影响。通过利用良好的知识理解来提高FA效率和隐私，该建议的解决方案将推动FA的能力的信封，并在现实世界中的场景中刺激FA应用的景观。该奖项反映了NSF的法规任务，并认为通过基金会的知识效果和广泛的评论，并被认为是通过评估来进行评估的，并获得了广泛的综述。