27,229 research outputs found
The Random Oracle Methodology, Revisited
We take a critical look at the relationship between the security of
cryptographic schemes in the Random Oracle Model, and the security of the
schemes that result from implementing the random oracle by so called
"cryptographic hash functions". The main result of this paper is a negative
one: There exist signature and encryption schemes that are secure in the Random
Oracle Model, but for which any implementation of the random oracle results in
insecure schemes.
In the process of devising the above schemes, we consider possible
definitions for the notion of a "good implementation" of a random oracle,
pointing out limitations and challenges.Comment: 31 page
Solution of linear ill-posed problems using random dictionaries
In the present paper we consider application of overcomplete dictionaries to
solution of general ill-posed linear inverse problems. In the context of
regression problems, there has been enormous amount of effort to recover an
unknown function using such dictionaries. One of the most popular methods,
lasso and its versions, is based on minimizing empirical likelihood and
unfortunately, requires stringent assumptions on the dictionary, the, so
called, compatibility conditions. Though compatibility conditions are hard to
satisfy, it is well known that this can be accomplished by using random
dictionaries. In the present paper, we show how one can apply random
dictionaries to solution of ill-posed linear inverse problems. We put a
theoretical foundation under the suggested methodology and study its
performance via simulations
Phantom cascades: The effect of hidden nodes on information diffusion
Research on information diffusion generally assumes complete knowledge of the
underlying network. However, in the presence of factors such as increasing
privacy awareness, restrictions on application programming interfaces (APIs)
and sampling strategies, this assumption rarely holds in the real world which
in turn leads to an underestimation of the size of information cascades. In
this work we study the effect of hidden network structure on information
diffusion processes. We characterise information cascades through activation
paths traversing visible and hidden parts of the network. We quantify diffusion
estimation error while varying the amount of hidden structure in five empirical
and synthetic network datasets and demonstrate the effect of topological
properties on this error. Finally, we suggest practical recommendations for
practitioners and propose a model to predict the cascade size with minimal
information regarding the underlying network.Comment: Preprint submitted to Elsevier Computer Communication
- …