I-Corps: Trustworthy Synthetic Data Generation

这个I-Corps项目更广泛的影响/商业潜力是开发支持现代隐私增强技术的软件产品。该项目的核心技术提出了数据丰富的应用程序，以一种新的、值得信赖的方式来维护数据实用性和保护数据隐私。使用生成式人工智能（AI），拟议的技术为中小型企业和组织释放了更大的数据影响，同时符合现代隐私法。此外，软件产品有可能针对“数字权利沙漠”中的边缘化社区，在这些社区中，客户和客户的数字权利受到企业和组织能力、数据隐私意识和成本的极大限制。透过建议的软件产品及随附的审核服务，更多个别企业及公司可获得更多有关其数据利益的途径，并照顾其客户的数码权利，而不论其人口及社会经济背景为何。这个I-Corps项目是基于深度学习技术的开发，用于表格数据合成。该项目利用生成对抗网络进行可信的表格数据合成。跨学科的学术-工业合作经验提供了一个成熟的框架，将数据合成技术集成到现代机器学习工作流程中，以支持和开发现代数字业务和服务。试点研究用于开发人工智能算法的集成，以审计合成表格数据的质量和可信度，以解锁安全，可靠，跨部门的数据共享。此外，我们还与社交媒体平台领域的行业合作伙伴进行了研究，以评估和完善该技术，通过工业级机器学习管道为用户提供隐私保护指标和运动评估。该技术有可能通过显著增强安全性、保密性和匿名性，推进数字执法，并为数字服务提供商和用户增加数据利益，积极改变平台用户的数字权利。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。

The broader impact/commercial potential of this I-Corps project is to develop software products that support modern privacy enhancing technology. The project's core technology proposes data-rich applications for a new and trustworthy way to maintain data utility and protect data privacy. Using generative artificial intelligence (AI), the proposed technology unlocks greater data impact for small and medium sized businesses and organizations while aligning with modern privacy law. In addition, the software products have potential to target marginalized communities in "digital rights deserts," where clients and customers' digital rights are significantly limited by businesses and organizations capacity, data privacy awareness, and cost. With the proposed software products and accompanying auditing service, more individual businesses and companies may receive more access to their data benefits and care of their customers' digital rights regardless of their demographic and socio-economic background. This I-Corps project is based on the development of deep learning technology for tabular data synthesis. The project leverages the use of generative adversarial networks for trustworthy tabular data synthesis. The interdisciplinary academic-industrial collaboration experience provides a proven framework to integrate data synthesis technology into modern machine learning workflow to support and develop modern digital businesses and services. Pilot research was used to develop an integration of artificial intelligence algorithms towards audit quality and trustworthiness of synthesized tabular data to unlock safe, secure, cross sector data sharing. In addition, research has been performed with industrial partners in the social media platform sector to assess and polish the technology towards providing users privacy-preserving metrics and exercise evaluation through industrial-level machine learning pipelines. The proposed technology has the potential to positively change platform users' digital rights by significantly enhancing safety, security and anonymity, advancing digital law enforcement, and increasing data benefits for both digital service providers and users.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.