18,020 research outputs found
Stopping time signatures for some algorithms in cryptography
We consider the normalized distribution of the overall running times of some
cryptographic algorithms, and what information they reveal about the
algorithms. Recent work of Deift, Menon, Olver, Pfrang, and Trogdon has shown
that certain numerical algorithms applied to large random matrices exhibit a
characteristic distribution of running times, which depends only on the
algorithm but are independent of the choice of probability distributions for
the matrices. Different algorithms often exhibit different running time
distributions, and so the histograms for these running time distributions
provide a time-signature for the algorithms, making it possible, in many cases,
to distinguish one algorithm from another. In this paper we extend this
analysis to cryptographic algorithms, and present examples of such algorithms
with time-signatures that are indistinguishable, and others with
time-signatures that are clearly distinct.Comment: 20 page
Strengthening measurements from the edges: application-level packet loss rate estimation
Network users know much less than ISPs, Internet exchanges and content providers about what happens inside the network. Consequently users cannot either easily detect network neutrality violations or readily exercise their market power by knowledgeably switching ISPs. This paper contributes to the ongoing efforts to empower users by proposing two models to estimate -- via application-level measurements -- a key network indicator, i.e., the packet loss rate (PLR) experienced by FTP-like TCP downloads. Controlled, testbed, and large-scale experiments show that the Inverse Mathis model is simpler and more consistent across the whole PLR range, but less accurate than the more advanced Likely Rexmit model for landline connections and moderate PL
A Byzantine Fault-Tolerant Ordering Service for the Hyperledger Fabric Blockchain Platform
Hyperledger Fabric (HLF) is a flexible permissioned blockchain platform
designed for business applications beyond the basic digital coin addressed by
Bitcoin and other existing networks. A key property of HLF is its
extensibility, and in particular the support for multiple ordering services for
building the blockchain. Nonetheless, the version 1.0 was launched in early
2017 without an implementation of a Byzantine fault-tolerant (BFT) ordering
service. To overcome this limitation, we designed, implemented, and evaluated a
BFT ordering service for HLF on top of the BFT-SMaRt state machine
replication/consensus library, implementing also optimizations for wide-area
deployment. Our results show that HLF with our ordering service can achieve up
to ten thousand transactions per second and write a transaction irrevocably in
the blockchain in half a second, even with peers spread in different
continents
Critical fluctuations and slowing down of chaos
Fluids cooled to the liquid-vapor critical point develop system-spanning fluctuations in density that transform their visual appearance. Despite a rich phenomenology, however, there is not currently an explanation of the mechanical instability in the molecular motion at this critical point. Here, we couple techniques from nonlinear dynamics and statistical physics to analyze the emergence of this singular state. Numerical simulations and analytical models show how the ordering mechanisms of critical dynamics are measurable through the hierarchy of spatiotemporal Lyapunov vectors. A subset of unstable vectors soften near the critical point, with a marked suppression in their characteristic exponents that reflects a weakened sensitivity to initial conditions. Finite-time fluctuations in these exponents exhibit sharply peaked dynamical timescales and power law signatures of the critical dynamics. Collectively, these results are symptomatic of a critical slowing down of chaos that sits at the root of our statistical understanding of the liquid-vapor critical point
Financial LPPL Bubbles with Mean-Reverting Noise in the Frequency Domain
The log-periodic power law (LPPL) is a model of asset prices during
endogenous bubbles. A major open issue is to verify the presence of LPPL in
price sequences and to estimate the LPPL parameters. Estimation is complicated
by the fact that daily LPPL returns are typically orders of magnitude smaller
than measured price returns, suggesting that noise obscures the underlying LPPL
dynamics. However, if noise is mean-reverting, it would quickly cancel out over
subsequent measurements. In this paper, we attempt to reject mean-reverting
noise from price sequences by exploiting frequency-domain properties of LPPL
and of mean reversion. First, we calculate the spectrum of mean-reverting \ou
noise and devise estimators for the noise's parameters. Then, we derive the
LPPL spectrum by breaking it down into its two main characteristics of power
law and of log-periodicity. We compare price spectra with noise spectra during
historical bubbles. In general, noise was strong also at low frequencies and,
even if LPPL underlied price dynamics, LPPL would be obscured by noise
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