23,087 research outputs found
Detecting Similarity of Rational Plane Curves
A novel and deterministic algorithm is presented to detect whether two given
rational plane curves are related by means of a similarity, which is a central
question in Pattern Recognition. As a by-product it finds all such
similarities, and the particular case of equal curves yields all symmetries. A
complete theoretical description of the method is provided, and the method has
been implemented and tested in the Sage system for curves of moderate degrees.Comment: 22 page
GPU Acceleration of Image Convolution using Spatially-varying Kernel
Image subtraction in astronomy is a tool for transient object discovery such
as asteroids, extra-solar planets and supernovae. To match point spread
functions (PSFs) between images of the same field taken at different times a
convolution technique is used. Particularly suitable for large-scale images is
a computationally intensive spatially-varying kernel. The underlying algorithm
is inherently massively parallel due to unique kernel generation at every pixel
location. The spatially-varying kernel cannot be efficiently computed through
the Convolution Theorem, and thus does not lend itself to acceleration by Fast
Fourier Transform (FFT). This work presents results of accelerated
implementation of the spatially-varying kernel image convolution in multi-cores
with OpenMP and graphic processing units (GPUs). Typical speedups over ANSI-C
were a factor of 50 and a factor of 1000 over the initial IDL implementation,
demonstrating that the techniques are a practical and high impact path to
terabyte-per-night image pipelines and petascale processing.Comment: 4 pages. Accepted to IEEE-ICIP 201
Entangled cloud storage
Entangled cloud storage (Aspnes et al., ESORICS 2004) enables a set of clients to “entangle” their files into a single clew to be stored by a (potentially malicious) cloud provider. The entanglement makes it impossible to modify or delete significant part of the clew without affecting all files encoded in the clew. A clew keeps the files in it private but still lets each client recover his own data by interacting with the cloud provider; no cooperation from other clients is needed. At the same time, the cloud provider is discouraged from altering or overwriting any significant part of the clew as this will imply that none of the clients can recover their files. We put forward the first simulation-based security definition for entangled cloud storage, in the framework of universal composability (Canetti, 2001). We then construct a protocol satisfying our security definition, relying on an entangled encoding scheme based on privacy-preserving polynomial interpolation; entangled encodings were originally proposed by Aspnes et al. as useful tools for the purpose of data entanglement. As a contribution of independent interest we revisit the security notions for entangled encodings, putting forward stronger definitions than previous work (that for instance did not consider collusion between clients and the cloud provider). Protocols for entangled cloud storage find application in the cloud setting, where clients store their files on a remote server and need to be ensured that the cloud provider will not modify or delete their data illegitimately. Current solutions, e.g., based on Provable Data Possession and Proof of Retrievability, require the server to be challenged regularly to provide evidence that the clients’ files are stored at a given time. Entangled cloud storage provides an alternative approach where any single client operates implicitly on behalf of all others, i.e., as long as one client's files are intact, the entire remote database continues to be safe and unblemishe
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)
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