1,256,380 research outputs found
The GSFC scientific data storage problem
Scientific data storage problems of telemetry tape
Coding Solutions for the Secure Biometric Storage Problem
The paper studies the problem of securely storing biometric passwords, such
as fingerprints and irises. With the help of coding theory Juels and Wattenberg
derived in 1999 a scheme where similar input strings will be accepted as the
same biometric. In the same time nothing could be learned from the stored data.
They called their scheme a "fuzzy commitment scheme". In this paper we will
revisit the solution of Juels and Wattenberg and we will provide answers to two
important questions: What type of error-correcting codes should be used and
what happens if biometric templates are not uniformly distributed, i.e. the
biometric data come with redundancy. Answering the first question will lead us
to the search for low-rate large-minimum distance error-correcting codes which
come with efficient decoding algorithms up to the designed distance. In order
to answer the second question we relate the rate required with a quantity
connected to the "entropy" of the string, trying to estimate a sort of
"capacity", if we want to see a flavor of the converse of Shannon's noisy
coding theorem. Finally we deal with side-problems arising in a practical
implementation and we propose a possible solution to the main one that seems to
have so far prevented real life applications of the fuzzy scheme, as far as we
know.Comment: the final version appeared in Proceedings Information Theory Workshop
(ITW) 2010, IEEE copyrigh
GDSP: A Graphical Perspective on the Distributed Storage Systems
The classical distributed storage problem can be modeled by a k-uniform {\it
complete} hyper-graph where vertices represent servers and hyper-edges
represent users. Hence each hyper-edge should be able to recover the full file
using only the memories of the vertices associated with it. This paper
considers the generalization of this problem to {\it arbitrary} hyper-graphs
and to the case of multiple files, where each user is only interested in one, a
problem we will refer to as the graphical distributed storage problem (GDSP).
Specifically, we make progress in the analysis of minimum-storage codes for two
main subproblems of the GDSP which extend the classical model in two
independent directions: the case of an arbitrary graph with multiple files, and
the case of an arbitrary hyper-graph with a single file
Energy Storage Sharing Strategy in Distribution Networks Using Bi-level Optimization Approach
In this paper, we address the energy storage management problem in
distribution networks from the perspective of an independent energy storage
manager (IESM) who aims to realize optimal energy storage sharing with
multi-objective optimization, i.e., optimizing the system peak loads and the
electricity purchase costs of the distribution company (DisCo) and its
customers. To achieve the goal of the IESM, an energy storage sharing strategy
is therefore proposed, which allows DisCo and customers to control the assigned
energy storage. The strategy is updated day by day according to the system
information change. The problem is formulated as a bi-level mathematical model
where the upper level model (ULM) seeks for optimal division of energy storage
among Disco and customers, and the lower level models (LLMs) represent the
minimizations of the electricity purchase costs of DisCo and customers.
Further, in order to enhance the computation efficiency, we transform the
bi-level model into a single-level mathematical program with equilibrium
constraints (MPEC) model and linearize it. Finally, we validate the
effectiveness of the strategy and complement our analysis through case studies
On Distributed Storage Allocations for Memory-Limited Systems
In this paper we consider distributed allocation problems with memory
constraint limits. Firstly, we propose a tractable relaxation to the problem of
optimal symmetric allocations from [1]. The approximated problem is based on
the Q-error function, and its solution approaches the solution of the initial
problem, as the number of storage nodes in the network grows. Secondly,
exploiting this relaxation, we are able to formulate and to solve the problem
for storage allocations for memory-limited DSS storing and arbitrary memory
profiles. Finally, we discuss the extension to the case of multiple data
objects, stored in the DSS.Comment: Submitted to IEEE GLOBECOM'1
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