255,012 research outputs found
Hybrid Information Flow Analysis for Programs with Arrays
Information flow analysis checks whether certain pieces of (confidential)
data may affect the results of computations in unwanted ways and thus leak
information. Dynamic information flow analysis adds instrumentation code to the
target software to track flows at run time and raise alarms if a flow policy is
violated; hybrid analyses combine this with preliminary static analysis.
Using a subset of C as the target language, we extend previous work on hybrid
information flow analysis that handled pointers to scalars. Our extended
formulation handles arrays, pointers to array elements, and pointer arithmetic.
Information flow through arrays of pointers is tracked precisely while arrays
of non-pointer types are summarized efficiently.
A prototype of our approach is implemented using the Frama-C program analysis
and transformation framework. Work on a full machine-checked proof of the
correctness of our approach using Isabelle/HOL is well underway; we present the
existing parts and sketch the rest of the correctness argument.Comment: In Proceedings VPT 2016, arXiv:1607.0183
Labeling Schemes with Queries
We study the question of ``how robust are the known lower bounds of labeling
schemes when one increases the number of consulted labels''. Let be a
function on pairs of vertices. An -labeling scheme for a family of graphs
\cF labels the vertices of all graphs in \cF such that for every graph
G\in\cF and every two vertices , the value can be inferred
by merely inspecting the labels of and .
This paper introduces a natural generalization: the notion of -labeling
schemes with queries, in which the value can be inferred by inspecting
not only the labels of and but possibly the labels of some additional
vertices. We show that inspecting the label of a single additional vertex (one
{\em query}) enables us to reduce the label size of many labeling schemes
significantly
Renormalization of Discrete Models without Background
Conventional renormalization methods in statistical physics and lattice
quantum field theory assume a flat metric background. We outline here a
generalization of such methods to models on discretized spaces without metric
background. Cellular decompositions play the role of discretizations. The group
of scale transformations is replaced by the groupoid of changes of cellular
decompositions. We introduce cellular moves which generate this groupoid and
allow to define a renormalization groupoid flow.
We proceed to test our approach on several models. Quantum BF theory is the
simplest example as it is almost topological and the renormalization almost
trivial. More interesting is generalized lattice gauge theory for which a
qualitative picture of the renormalization groupoid flow can be given. This is
confirmed by the exact renormalization in dimension two.
A main motivation for our approach are discrete models of quantum gravity. We
investigate both the Reisenberger and the Barrett-Crane spin foam model in view
of their amenability to a renormalization treatment. In the second case a lack
of tunable local parameters prompts us to introduce a new model. For the
Reisenberger and the new model we discuss qualitative aspects of the
renormalization groupoid flow. In both cases quantum BF theory is the UV fixed
point.Comment: 40 pages, 17 figures, LaTeX + AMS + XY-pic + eps; added subsection
4.3 on relation to spin network diagrams, reference added, minor adjustment
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