Internet Timing for the Ages: Establishing the New Timekeeping System. All computers incorporate a software clock, essential to myriad software applications. An economic way to synchronize such clocks is over a network, however the approach the Internet currently depends upon is unreliable and vulnerable. This project aims to establish a new architecture for networked timekeeping, built on future-proofed fundamentals, that will for the first time address each of accuracy, reliability, and trust. ....Internet Timing for the Ages: Establishing the New Timekeeping System. All computers incorporate a software clock, essential to myriad software applications. An economic way to synchronize such clocks is over a network, however the approach the Internet currently depends upon is unreliable and vulnerable. This project aims to establish a new architecture for networked timekeeping, built on future-proofed fundamentals, that will for the first time address each of accuracy, reliability, and trust. The expected outcome is a national prototype, serving the public with accurate and trusted time, that will form the basis of the next generation timekeeping system for the Internet and the Internet of Things. Expected benefits include enhanced productivity across the digital economy, and resilience to GPS failures.Read moreRead less
Attribution of Machine-generated Code for Accountability. Machine-generated (or neural) code is usually produced by AI tools to speed up software development. However, such codes have recently raised serious security and privacy concerns. This project aims to attribute these codes to their generative models for accountability purposes. In the process, a series of new techniques are developed to differentiate between the codes generated by different models. The outcomes include analysis of neural ....Attribution of Machine-generated Code for Accountability. Machine-generated (or neural) code is usually produced by AI tools to speed up software development. However, such codes have recently raised serious security and privacy concerns. This project aims to attribute these codes to their generative models for accountability purposes. In the process, a series of new techniques are developed to differentiate between the codes generated by different models. The outcomes include analysis of neural code fingerprints, classification of neural codes, and theories to verify the correctness of code attribution. These will provide significant benefits, ranging from copyright protection to privacy preservation. This project is timely since currently the software community is pervasively using neural codes.Read moreRead less