349,163 research outputs found
An Access Control Model for Tree Data Structure
International audienceTrees are very often used to structure data. For instance, file systems are structured into trees and XML documents can be represented by trees. There are literally as many access control schemes as there are tree data structures. Consequently, an access control model which has been defined for a particular kind of tree cannot be easily adapted to another kind of tree. In this paper, we propose an access control model for generic tree data structures. This model can then be applied to any specific typed tree data structure
Improving the scalability of parallel N-body applications with an event driven constraint based execution model
The scalability and efficiency of graph applications are significantly
constrained by conventional systems and their supporting programming models.
Technology trends like multicore, manycore, and heterogeneous system
architectures are introducing further challenges and possibilities for emerging
application domains such as graph applications. This paper explores the space
of effective parallel execution of ephemeral graphs that are dynamically
generated using the Barnes-Hut algorithm to exemplify dynamic workloads. The
workloads are expressed using the semantics of an Exascale computing execution
model called ParalleX. For comparison, results using conventional execution
model semantics are also presented. We find improved load balancing during
runtime and automatic parallelism discovery improving efficiency using the
advanced semantics for Exascale computing.Comment: 11 figure
Runtime Optimizations for Prediction with Tree-Based Models
Tree-based models have proven to be an effective solution for web ranking as
well as other problems in diverse domains. This paper focuses on optimizing the
runtime performance of applying such models to make predictions, given an
already-trained model. Although exceedingly simple conceptually, most
implementations of tree-based models do not efficiently utilize modern
superscalar processor architectures. By laying out data structures in memory in
a more cache-conscious fashion, removing branches from the execution flow using
a technique called predication, and micro-batching predictions using a
technique called vectorization, we are able to better exploit modern processor
architectures and significantly improve the speed of tree-based models over
hard-coded if-else blocks. Our work contributes to the exploration of
architecture-conscious runtime implementations of machine learning algorithms
AnonyControl: Control Cloud Data Anonymously with Multi-Authority Attribute-Based Encryption
Cloud computing is a revolutionary computing paradigm which enables flexible,
on-demand and low-cost usage of computing resources. However, those advantages,
ironically, are the causes of security and privacy problems, which emerge
because the data owned by different users are stored in some cloud servers
instead of under their own control. To deal with security problems, various
schemes based on the Attribute- Based Encryption (ABE) have been proposed
recently. However, the privacy problem of cloud computing is yet to be solved.
This paper presents an anonymous privilege control scheme AnonyControl to
address the user and data privacy problem in a cloud. By using multiple
authorities in cloud computing system, our proposed scheme achieves anonymous
cloud data access, finegrained privilege control, and more importantly,
tolerance to up to (N -2) authority compromise. Our security and performance
analysis show that AnonyControl is both secure and efficient for cloud
computing environment.Comment: 9 pages, 6 figures, 3 tables, conference, IEEE INFOCOM 201
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