386,048 research outputs found
Non-blind watermarking of network flows
Linking network flows is an important problem in intrusion detection as well
as anonymity. Passive traffic analysis can link flows but requires long periods
of observation to reduce errors. Active traffic analysis, also known as flow
watermarking, allows for better precision and is more scalable. Previous flow
watermarks introduce significant delays to the traffic flow as a side effect of
using a blind detection scheme; this enables attacks that detect and remove the
watermark, while at the same time slowing down legitimate traffic. We propose
the first non-blind approach for flow watermarking, called RAINBOW, that
improves watermark invisibility by inserting delays hundreds of times smaller
than previous blind watermarks, hence reduces the watermark interference on
network flows. We derive and analyze the optimum detectors for RAINBOW as well
as the passive traffic analysis under different traffic models by using
hypothesis testing. Comparing the detection performance of RAINBOW and the
passive approach we observe that both RAINBOW and passive traffic analysis
perform similarly good in the case of uncorrelated traffic, however, the
RAINBOW detector drastically outperforms the optimum passive detector in the
case of correlated network flows. This justifies the use of non-blind
watermarks over passive traffic analysis even though both approaches have
similar scalability constraints. We confirm our analysis by simulating the
detectors and testing them against large traces of real network flows
A rigorous and efficient asymptotic test for power-law cross-correlation
Podobnik and Stanley recently proposed a novel framework, Detrended
Cross-Correlation Analysis, for the analysis of power-law cross-correlation
between two time-series, a phenomenon which occurs widely in physical,
geophysical, financial and numerous additional applications. While highly
promising in these important application domains, to date no rigorous or
efficient statistical test has been proposed which uses the information
provided by DCCA across time-scales for the presence of this power-law
cross-correlation. In this paper we fill this gap by proposing a method based
on DCCA for testing the hypothesis of power-law cross-correlation; the method
synthesizes the information generated by DCCA across time-scales and returns
conservative but practically relevant p-values for the null hypothesis of zero
correlation, which may be efficiently calculated in software. Thus our
proposals generate confidence estimates for a DCCA analysis in a fully
probabilistic fashion
Restricted Likelihood Ratio Testing in Linear Mixed Models with General Error Covariance Structure
We consider the problem of testing for zero variance components in linear mixed models with correlated or heteroscedastic errors. In the case of independent and identically distributed errors, a valid test exists, which is based on the exact finite sample distribution of the restricted likelihood ratio test statistic under the null hypothesis. We propose to make use of a transformation to derive the (approximate) test distribution for the restricted likelihood ratio test statistic in the case of a general error covariance structure. The proposed test proves its value in simulations and is finally applied to an interesting question in the field of well-being economics
Converses for Secret Key Agreement and Secure Computing
We consider information theoretic secret key agreement and secure function
computation by multiple parties observing correlated data, with access to an
interactive public communication channel. Our main result is an upper bound on
the secret key length, which is derived using a reduction of binary hypothesis
testing to multiparty secret key agreement. Building on this basic result, we
derive new converses for multiparty secret key agreement. Furthermore, we
derive converse results for the oblivious transfer problem and the bit
commitment problem by relating them to secret key agreement. Finally, we derive
a necessary condition for the feasibility of secure computation by trusted
parties that seek to compute a function of their collective data, using an
interactive public communication that by itself does not give away the value of
the function. In many cases, we strengthen and improve upon previously known
converse bounds. Our results are single-shot and use only the given joint
distribution of the correlated observations. For the case when the correlated
observations consist of independent and identically distributed (in time)
sequences, we derive strong versions of previously known converses
- …