RSS feed source: National Science Foundation
Synopsis
In today’s hyperconnected and device-rich world, increasing computational power and the explosive growth of data present us with tremendous opportunities to enable data-driven, evidence-based decision-making capabilities to accelerate scientific discovery and innovation. However, to be able to responsibly leverage the insights from and power of data, such as for training powerful artificial intelligence (AI) models, it is important to have practically deployable and scalable technologies that allow data sharing in a privacy-preserving manner. While there has been significant research progress in privacy-related areas, privacy-preserving data sharing technologies remain at various levels of maturity in terms of practical deployment.
The goals of the PDaSP program are aligned with the Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence (AI EO), which emphasizes the role for privacy-enhancing technologies (PETs) in a responsible and safe AI future. The EO directs NSF to, “where feasible
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