ﻻ يوجد ملخص باللغة العربية
Mass surveillance of the population by state agencies and corporate parties is now a well-known fact. Journalists and whistle-blowers still lack means to circumvent global spying for the sake of their investigations. With Spores, we propose a way for journalists and their sources to plan a posteriori file exchanges when they physically meet. We leverage on the multiplication of personal devices per capita to provide a lightweight, robust and fully anonymous decentralised file transfer protocol between users. Spores hinges on our novel concept of e-squads: ones personal devices, rendered intelligent by gossip communication protocols, can provide private and dependable services to their user. Peoples e-squads are federated into a novel onion routing network, able to withstand the inherent unreliability of personal appliances while providing reliable routing. Spores performances are competitive, and its privacy properties of the communication outperform state of the art onion routing strategies.
We present and explore a model of stateless and self-stabilizing distributed computation, inspired by real-world applications such as routing on todays Internet. Processors in our model do not have an internal state, but rather interact by repeatedly
Existing compact routing schemes, e.g., Thorup and Zwick [SPAA 2001] and Chechik [PODC 2013], often have no means to tolerate failures, once the system has been setup and started. This paper presents, to our knowledge, the first self-healing compact
Information delivery in a network of agents is a key issue for large, complex systems that need to do so in a predictable, efficient manner. The delivery of information in such multi-agent systems is typically implemented through routing protocols th
Consider a fully-connected synchronous distributed system consisting of $n$ nodes, where up to $f$ nodes may be faulty and every node starts in an arbitrary initial state. In the synchronous $C$-counting problem, all nodes need to eventually agree on
Federated learning can enable remote workers to collaboratively train a shared machine learning model while allowing training data to be kept locally. In the use case of wireless mobile devices, the communication overhead is a critical bottleneck due