I-Corps: Trustworthy Synthetic Data Generation

这个i-Corps项目的更广泛的影响/商业潜力是开发支持现代隐私增强技术的软件产品。该项目的核心技术为数据丰富的应用程序提供了一种新的、值得信赖的方式来维护数据效用和保护数据隐私。使用生成性人工智能(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，同时与现代隐私法保持一致。此外，这些软件产品有可能瞄准“数字权利沙漠”中的边缘化社区，在那里，客户和客户的数字权利受到企业和组织的能力、数据隐私意识和成本的严重限制。有了拟议的软件产品和随之而来的审计服务，更多的个人企业和公司可能会获得更多获得其数据利益的机会，并照顾其客户的数字权利，而无论他们的人口统计和社会经济背景如何。该i-Corps项目是基于用于表格数据合成的深度学习技术的开发。该项目利用生成性对抗性网络进行可靠的表格数据合成。跨学科的学术-产业协作经验提供了一个成熟的框架，可以将数据合成技术集成到现代机器学习工作流中，以支持和发展现代数字业务和服务。利用试点研究开发了人工智能算法的集成，以提高合成表格数据的审计质量和可信度，从而实现安全、可靠、跨部门的数据共享。此外，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，以便通过工业级机器学习管道为用户提供隐私保护指标和练习评估。这项拟议的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，并增加数字服务提供商和用户的数据利益，从而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智力优势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.