731 research outputs found
On the Round Complexity of Randomized Byzantine Agreement
We prove lower bounds on the round complexity of randomized Byzantine agreement (BA) protocols, bounding the halting probability of such protocols after one and two rounds. In particular, we prove that:
1) BA protocols resilient against n/3 [resp., n/4] corruptions terminate (under attack) at the end of the first round with probability at most o(1) [resp., 1/2+ o(1)].
2) BA protocols resilient against n/4 corruptions terminate at the end of the second round with probability at most 1-Theta(1).
3) For a large class of protocols (including all BA protocols used in practice) and under a plausible combinatorial conjecture, BA protocols resilient against n/3 [resp., n/4] corruptions terminate at the end of the second round with probability at most o(1) [resp., 1/2 + o(1)].
The above bounds hold even when the parties use a trusted setup phase, e.g., a public-key infrastructure (PKI).
The third bound essentially matches the recent protocol of Micali (ITCS\u2717) that tolerates up to n/3 corruptions and terminates at the end of the third round with constant probability
Consensus of Multi-Agent Networks in the Presence of Adversaries Using Only Local Information
This paper addresses the problem of resilient consensus in the presence of
misbehaving nodes. Although it is typical to assume knowledge of at least some
nonlocal information when studying secure and fault-tolerant consensus
algorithms, this assumption is not suitable for large-scale dynamic networks.
To remedy this, we emphasize the use of local strategies to deal with
resilience to security breaches. We study a consensus protocol that uses only
local information and we consider worst-case security breaches, where the
compromised nodes have full knowledge of the network and the intentions of the
other nodes. We provide necessary and sufficient conditions for the normal
nodes to reach consensus despite the influence of the malicious nodes under
different threat assumptions. These conditions are stated in terms of a novel
graph-theoretic property referred to as network robustness.Comment: This report contains the proofs of the results presented at HiCoNS
201
Reliable Broadcast with Respect to Topology Knowledge
We study the Reliable Broadcast problem in incomplete networks against
a Byzantine adversary. We examine the problem under the locally bounded adversary model of Koo (2004) and the general adversary model of Hirt and Maurer (1997) and explore the tradeoff between the
level of topology knowledge and the solvability of the problem.
We refine the local pair-cut technique of Pelc and Peleg (2005) in order to obtain impossibility results for every level of topology knowledge and any type of corruption distribution. On the positive side we devise protocols that match the obtained bounds and thus, exactly characterize the classes of graphs in which Reliable Broadcast is possible.
Among others, we show that Koo\u27s Certified Propagation Algorithm
(CPA) is unique against locally bounded adversaries in ad hoc networks, that is, it can tolerate as many local corruptions as any other non-faulty algorithm; this settles an open question posed by Pelc and Peleg. We also provide an adaptation of CPA against general adversaries and show its uniqueness in this case too. To the best of our knowledge this is the first optimal algorithm for Reliable Broadcast in generic topology ad hoc networks against general adversaries
Peer-to-Peer Secure Multi-Party Numerical Computation Facing Malicious Adversaries
We propose an efficient framework for enabling secure multi-party numerical
computations in a Peer-to-Peer network. This problem arises in a range of
applications such as collaborative filtering, distributed computation of trust
and reputation, monitoring and other tasks, where the computing nodes is
expected to preserve the privacy of their inputs while performing a joint
computation of a certain function. Although there is a rich literature in the
field of distributed systems security concerning secure multi-party
computation, in practice it is hard to deploy those methods in very large scale
Peer-to-Peer networks. In this work, we try to bridge the gap between
theoretical algorithms in the security domain, and a practical Peer-to-Peer
deployment.
We consider two security models. The first is the semi-honest model where
peers correctly follow the protocol, but try to reveal private information. We
provide three possible schemes for secure multi-party numerical computation for
this model and identify a single light-weight scheme which outperforms the
others. Using extensive simulation results over real Internet topologies, we
demonstrate that our scheme is scalable to very large networks, with up to
millions of nodes. The second model we consider is the malicious peers model,
where peers can behave arbitrarily, deliberately trying to affect the results
of the computation as well as compromising the privacy of other peers. For this
model we provide a fourth scheme to defend the execution of the computation
against the malicious peers. The proposed scheme has a higher complexity
relative to the semi-honest model. Overall, we provide the Peer-to-Peer network
designer a set of tools to choose from, based on the desired level of security.Comment: Submitted to Peer-to-Peer Networking and Applications Journal (PPNA)
200
Crowd Vetting: Rejecting Adversaries via Collaboration--with Application to Multi-Robot Flocking
We characterize the advantage of using a robot's neighborhood to find and
eliminate adversarial robots in the presence of a Sybil attack. We show that by
leveraging the opinions of its neighbors on the trustworthiness of transmitted
data, robots can detect adversaries with high probability. We characterize a
number of communication rounds required to achieve this result to be a function
of the communication quality and the proportion of legitimate to malicious
robots. This result enables increased resiliency of many multi-robot
algorithms. Because our results are finite time and not asymptotic, they are
particularly well-suited for problems with a time critical nature. We develop
two algorithms, \emph{FindSpoofedRobots} that determines trusted neighbors with
high probability, and \emph{FindResilientAdjacencyMatrix} that enables
distributed computation of graph properties in an adversarial setting. We apply
our methods to a flocking problem where a team of robots must track a moving
target in the presence of adversarial robots. We show that by using our
algorithms, the team of robots are able to maintain tracking ability of the
dynamic target
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