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Register a new userSpores: Stateless Predictive Onion Routing for E-Squads
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Added by
Adrien Luxey
Publication date
2020
fields
Informatics Engineering
and research's language is
English
Authors
Daniel Bosk
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No Arabic abstract
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.
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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 mapping incoming messages (labels) to outgoing messages and output values. While seemingly too restrictive to be of interest, stateless computation encompasses both classical game-theoretic notions of strategic interaction and a broad range of practical applications (e.g., Internet protocols, circuits, diffusion of technologies in social networks). We embark on a holistic exploration of stateless computation. We tackle two important questions: (1) Under what conditions is self-stabilization, i.e., guaranteed convergence to a legitimate global configuration, achievable for stateless computation? and (2) What is the computational power of stateless computation? Our results for self-stabilization include a general necessary condition for self-stabilization and hardness results for verifying that a stateless protocol is self-stabilizing. Our main results for the power of stateless computation show that labels of logarithmic length in the number of processors yield substantial computational power even on ring topologies. We present a separation between unidirectional and bidirectional rings (L/poly vs. P/poly), reflecting the sequential nature of computation on a unidirectional ring, as opposed to the parallelism afforded by the bidirectional ring. We leave the reader with many exciting directions for future research.
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 routing scheme. Besides, our schemes are developed for low memory nodes, i.e., nodes need only $O(log^2 n)$ memory, and are thus, compact schemes. We introduce two algorithms of independent interest: The first is CompactFT, a novel compact version (using only $O(log n)$ local memory) of the self-healing algorithm Forgiving Tree of Hayes et al. [PODC 2008]. The second algorithm (CompactFTZ) combines CompactFT with Thorup-Zwicks tree-based compact routing scheme [SPAA 2001] to produce a fully compact self-healing routing scheme. In the self-healing model, the adversary deletes nodes one at a time with the affected nodes self-healing locally by adding few edges. CompactFT recovers from each attack in only $O(1)$ time and $Delta$ messages, with only +3 degree increase and $O(log Delta)$ graph diameter increase, over any sequence of deletions ($Delta$ is the initial maximum degree). Additionally, CompactFTZ guarantees delivery of a packet sent from sender s as long as the receiver t has not been deleted, with only an additional $O(y log Delta)$ latency, where $y$ is the number of nodes that have been deleted on the path between $s$ and $t$. If $t$ has been deleted, $s$ gets informed and the packet removed from the network.
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 that determine how information flows through the network. Different routing protocols exist each with its own benefits, but it is generally unclear which properties can be successfully combined within a given algorithm. We approach this problem from the axiomatic point of view, i.e., we try to establish what are the properties we would seek to see in such a system, and examine the different properties which uniquely define common routing algorithms used today. We examine several desirable properties, such as robustness, which ensures adding nodes and edges does not change the routing in a radical, unpredictable ways; and properties that depend on the operating environment, such as an economic model, where nodes choose their paths based on the cost they are charged to pass information to the next node. We proceed to fully characterize minimal spanning tree, shortest path, and weakest link routing algorithms, showing a tight set of axioms for each.
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 a counter that is increased by one modulo $C$ in each round for given $C>1$. In the self-stabilising firing squad problem, the task is to eventually guarantee that all non-faulty nodes have simultaneous responses to external inputs: if a subset of the correct nodes receive an external go signal as input, then all correct nodes should agree on a round (in the not-too-distant future) in which to jointly output a fire signal. Moreover, no node should generate a fire signal without some correct node having previously received a go signal as input. We present a framework reducing both tasks to binary consensus at very small cost. For example, we obtain a deterministic algorithm for self-stabilising Byzantine firing squads with optimal resilience $f<n/3$, asymptotically optimal stabilisation and response time $O(f)$, and message size $O(log f)$. As our framework does not restrict the type of consensus routines used, we also obtain efficient randomised solutions, and it is straightforward to adapt our framework for other types of permanent faults.
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 to limited power and bandwidth. Prior work has utilized various data compression tools such as quantization and sparsification to reduce the overhead. In this paper, we propose a predictive coding based communication scheme for federated learning. The scheme has shared prediction functions among all devices and allows each worker to transmit a compressed residual vector derived from the reference. In each communication round, we select the predictor and quantizer based on the rate-distortion cost, and further reduce the redundancy with entropy coding. Extensive simulations reveal that the communication cost can be reduced up to 99% with even better learning performance when compared with other baseline methods.
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