8 research outputs found
Matrix Representation of Iterative Approximate Byzantine Consensus in Directed Graphs
This paper presents a proof of correctness of an iterative approximate
Byzantine consensus (IABC) algorithm for directed graphs. The iterative
algorithm allows fault- free nodes to reach approximate conensus despite the
presence of up to f Byzantine faults. Necessary conditions on the underlying
network graph for the existence of a correct IABC algorithm were shown in our
recent work [15, 16]. [15] also analyzed a specific IABC algorithm and showed
that it performs correctly in any network graph that satisfies the necessary
condition, proving that the necessary condition is also sufficient. In this
paper, we present an alternate proof of correctness of the IABC algorithm,
using a familiar technique based on transition matrices [9, 3, 17, 19].
The key contribution of this paper is to exploit the following observation:
for a given evolution of the state vector corresponding to the state of the
fault-free nodes, many alternate state transition matrices may be chosen to
model that evolution cor- rectly. For a given state evolution, we identify one
approach to suitably "design" the transition matrices so that the standard
tools for proving convergence can be applied to the Byzantine fault-tolerant
algorithm as well. In particular, the transition matrix for each iteration is
designed such that each row of the matrix contains a large enough number of
elements that are bounded away from 0
Reaching Approximate Byzantine Consensus in Partially-Connected Mobile Networks
We consider the problem of approximate consensus in mobile networks
containing Byzantine nodes. We assume that each correct node can communicate
only with its neighbors and has no knowledge of the global topology. As all
nodes have moving ability, the topology is dynamic. The number of Byzantine
nodes is bounded by f and known by all correct nodes. We first introduce an
approximate Byzantine consensus protocol which is based on the linear iteration
method. As nodes are allowed to collect information during several consecutive
rounds, moving gives them the opportunity to gather more values. We propose a
novel sufficient and necessary condition to guarantee the final convergence of
the consensus protocol. The requirement expressed by our condition is not
"universal": in each phase it affects only a single correct node. More
precisely, at least one correct node among those that propose either the
minimum or the maximum value which is present in the network, has to receive
enough messages (quantity constraint) with either higher or lower values
(quality constraint). Of course, nodes' motion should not prevent this
requirement to be fulfilled. Our conclusion shows that the proposed condition
can be satisfied if the total number of nodes is greater than 3f+1.Comment: No. RR-7985 (2012
Network Robustness: Diffusing Information Despite Adversaries
In this thesis, we consider the problem of diffusing information resiliently in networks that contain misbehaving nodes. Previous strategies to achieve resilient information diffusion typically require the normal nodes to hold some global information, such as the topology of the network and the identities of non-neighboring nodes. However, these assumptions are not suitable for large-scale networks and this necessitates our study of resilient algorithms based on only local information.
We propose a consensus algorithm where, at each time-step, each normal node removes the
extreme values in its neighborhood and updates its value as a weighted average of its own value and the remaining values. We show that traditional topological metrics (such as connectivity of the network) fail to capture such dynamics. Thus, we introduce a topological property termed as network robustness and show that this concept, together with its variants, is the key property to characterize the behavior of a class of resilient algorithms that use purely local information.
We then investigate the robustness properties of complex networks. Specifically, we consider common random graph models for complex networks, including the preferential attachment model, the Erdos-Renyi model, and the geometric random graph model, and compare the metrics of connectivity and robustness in these models. While connectivity and robustness are greatly different in general (i.e., there exist graphs which are highly connected but with poor robustness), we show that the notions of robustness and connectivity are equivalent in the preferential attachment model, cannot be very different in the geometric random graph model, and share the same threshold functions in the Erdos-Renyi model, which gives us more insight about the structure of complex networks. Finally, we provide a construction method for robust graphs