747 research outputs found
Dynamic Size Counting in Population Protocols
The population protocol model describes a network of anonymous agents that
interact asynchronously in pairs chosen at random. Each agent starts in the
same initial state . We introduce the *dynamic size counting* problem:
approximately counting the number of agents in the presence of an adversary who
at any time can remove any number of agents or add any number of new agents in
state . A valid solution requires that after each addition/removal event,
resulting in population size , with high probability each agent "quickly"
computes the same constant-factor estimate of the value (how quickly
is called the *convergence* time), which remains the output of every agent for
as long as possible (the *holding* time). Since the adversary can remove
agents, the holding time is necessarily finite: even after the adversary stops
altering the population, it is impossible to *stabilize* to an output that
never again changes.
We first show that a protocol solves the dynamic size counting problem if and
only if it solves the *loosely-stabilizing counting* problem: that of
estimating in a *fixed-size* population, but where the adversary can
initialize each agent in an arbitrary state, with the same convergence time and
holding time. We then show a protocol solving the loosely-stabilizing counting
problem with the following guarantees: if the population size is , is
the largest initial estimate of , and s is the maximum integer
initially stored in any field of the agents' memory, we have expected
convergence time , expected polynomial holding time, and
expected memory usage of bits. Interpreted as
a dynamic size counting protocol, when changing from population size
to , the convergence time is
Computer Aided Verification
This open access two-volume set LNCS 10980 and 10981 constitutes the refereed proceedings of the 30th International Conference on Computer Aided Verification, CAV 2018, held in Oxford, UK, in July 2018. The 52 full and 13 tool papers presented together with 3 invited papers and 2 tutorials were carefully reviewed and selected from 215 submissions. The papers cover a wide range of topics and techniques, from algorithmic and logical foundations of verification to practical applications in distributed, networked, cyber-physical, and autonomous systems. They are organized in topical sections on model checking, program analysis using polyhedra, synthesis, learning, runtime verification, hybrid and timed systems, tools, probabilistic systems, static analysis, theory and security, SAT, SMT and decisions procedures, concurrency, and CPS, hardware, industrial applications
Selecting a Leader in a Network of Finite State Machines
This paper studies a variant of the leader election problem under the stone age model (Emek and Wattenhofer, PODC 2013) that considers a network of n randomized finite automata with very weak communication capabilities (a multi-frequency asynchronous generalization of the beeping model\u27s communication scheme). Since solving the classic leader election problem is impossible even in more powerful models, we consider a relaxed variant, referred to as k-leader selection, in which a leader should be selected out of at most k initial candidates. Our main contribution is an algorithm that solves k-leader selection for bounded k in the aforementioned stone age model. On (general topology) graphs of diameter D, this algorithm runs in O~(D) time and succeeds with high probability. The assumption that k is bounded turns out to be unavoidable: we prove that if k = omega (1), then no algorithm in this model can solve k-leader selection with a (positive) constant probability
Reduce to the Max: A Simple Approach for Massive-Scale Privacy-Preserving Collaborative Network Measurements (Extended Version)
Privacy-preserving techniques for distributed computation have been proposed
recently as a promising framework in collaborative inter-domain network
monitoring. Several different approaches exist to solve such class of problems,
e.g., Homomorphic Encryption (HE) and Secure Multiparty Computation (SMC) based
on Shamir's Secret Sharing algorithm (SSS). Such techniques are complete from a
computation-theoretic perspective: given a set of private inputs, it is
possible to perform arbitrary computation tasks without revealing any of the
intermediate results. In fact, HE and SSS can operate also on secret inputs
and/or provide secret outputs. However, they are computationally expensive and
do not scale well in the number of players and/or in the rate of computation
tasks. In this paper we advocate the use of "elementary" (as opposite to
"complete") Secure Multiparty Computation (E-SMC) procedures for traffic
monitoring. E-SMC supports only simple computations with private input and
public output, i.e., it can not handle secret input nor secret (intermediate)
output. Such a simplification brings a dramatic reduction in complexity and
enables massive-scale implementation with acceptable delay and overhead.
Notwithstanding its simplicity, we claim that an E-SMC scheme is sufficient to
perform a great variety of computation tasks of practical relevance to
collaborative network monitoring, including, e.g., anonymous publishing and set
operations. This is achieved by combining a E-SMC scheme with data structures
like Bloom Filters and bitmap strings.Comment: This is an extended version of the paper presented at the Third
International Workshop on Traffic Monitoring and Analysis (TMA'11), Vienna,
27 April 201
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