Search for: All records

We give a unified syntax, and a hierarchy of definitions of security of increasing strength, for non-interactive threshold signature schemes. These are schemes having a single-round signing protocol, possibly with one prior round of message-independent pre-processing. We fit FROST1 and BLS, which are leading practical schemes, into our hierarchy, in particular showing they meet stronger security definitions than they have been shown to meet so far. We also fit in our hierarchy a more efficient version FROST2 of FROST1 that we give. These definitions and results, for simplicity, all assume trusted key generation. Finally, we prove the security of FROST2 with key generation performed by an efficient distributed key generation protocol. 

We describe the results of a randomized controlled trial of video-streaming algorithms for bitrate selection and network prediction. Over the last year, we have streamed 38.6 years of video to 63,508 users across the Internet. Sessions are randomized in blinded fashion among algorithms. We found that in this real-world setting, it is difficult for sophisticated or machine-learned control schemes to outperform a "simple" scheme (buffer-based control), notwithstanding good performance in network emulators or simulators. We performed a statistical analysis and found that the heavy-tailed nature of network and user behavior, as well as the challenges of emulating diverse Internet paths during training, present obstacles for learned algorithms in this setting. We then developed an ABR algorithm that robustly outperformed other schemes, by leveraging data from its deployment and limiting the scope of machine learning only to making predictions that can be checked soon after. The system uses supervised learning in situ, with data from the real deployment environment, to train a probabilistic predictor of upcoming chunk transmission times. This module then informs a classical control policy (model predictive control). To support further investigation, we are publishing an archive of data and results each week, and will open our ongoing study to the community. We welcome other researchers to use this platform to develop and validate new algorithms for bitrate selection, network prediction, and congestion control.