10,825 research outputs found
On the Optimality of Secret Key Agreement via Omniscience
For the multiterminal secret key agreement problem under a private source
model, it is known that the maximum key rate, i.e., the secrecy capacity, can
be achieved through communication for omniscience, but the omniscience strategy
can be strictly suboptimal in terms of minimizing the public discussion rate.
While a single-letter characterization is not known for the minimum discussion
rate needed for achieving the secrecy capacity, we derive single-letter lower
and upper bounds that yield some simple conditions for omniscience to be
discussion-rate optimal. These conditions turn out to be enough to deduce the
optimality of omniscience for a large class of sources including the
hypergraphical sources. Through conjectures and examples, we explore other
source models to which our methods do not easily extend
A Practical Approach for Successive Omniscience
The system that we study in this paper contains a set of users that observe a
discrete memoryless multiple source and communicate via noise-free channels
with the aim of attaining omniscience, the state that all users recover the
entire multiple source. We adopt the concept of successive omniscience (SO),
i.e., letting the local omniscience in some user subset be attained before the
global omniscience in the entire system, and consider the problem of how to
efficiently attain omniscience in a successive manner. Based on the existing
results on SO, we propose a CompSetSO algorithm for determining a complimentary
set, a user subset in which the local omniscience can be attained first without
increasing the sum-rate, the total number of communications, for the global
omniscience. We also derive a sufficient condition for a user subset to be
complimentary so that running the CompSetSO algorithm only requires a lower
bound, instead of the exact value, of the minimum sum-rate for attaining global
omniscience. The CompSetSO algorithm returns a complimentary user subset in
polynomial time. We show by example how to recursively apply the CompSetSO
algorithm so that the global omniscience can be attained by multi-stages of SO
Coded Cooperative Data Exchange for a Secret Key
We consider a coded cooperative data exchange problem with the goal of
generating a secret key. Specifically, we investigate the number of public
transmissions required for a set of clients to agree on a secret key with
probability one, subject to the constraint that it remains private from an
eavesdropper.
Although the problems are closely related, we prove that secret key
generation with fewest number of linear transmissions is NP-hard, while it is
known that the analogous problem in traditional cooperative data exchange can
be solved in polynomial time. In doing this, we completely characterize the
best possible performance of linear coding schemes, and also prove that linear
codes can be strictly suboptimal. Finally, we extend the single-key results to
characterize the minimum number of public transmissions required to generate a
desired integer number of statistically independent secret keys.Comment: Full version of a paper that appeared at ISIT 2014. 19 pages, 2
figure
Perfect Omniscience, Perfect Secrecy and Steiner Tree Packing
We consider perfect secret key generation for a ``pairwise independent
network'' model in which every pair of terminals share a random binary string,
with the strings shared by distinct terminal pairs being mutually independent.
The terminals are then allowed to communicate interactively over a public
noiseless channel of unlimited capacity. All the terminals as well as an
eavesdropper observe this communication. The objective is to generate a perfect
secret key shared by a given set of terminals at the largest rate possible, and
concealed from the eavesdropper.
First, we show how the notion of perfect omniscience plays a central role in
characterizing perfect secret key capacity. Second, a multigraph representation
of the underlying secrecy model leads us to an efficient algorithm for perfect
secret key generation based on maximal Steiner tree packing. This algorithm
attains capacity when all the terminals seek to share a key, and, in general,
attains at least half the capacity. Third, when a single ``helper'' terminal
assists the remaining ``user'' terminals in generating a perfect secret key, we
give necessary and sufficient conditions for the optimality of the algorithm;
also, a ``weak'' helper is shown to be sufficient for optimality.Comment: accepted to the IEEE Transactions on Information Theor
On the Communication Complexity of Secret Key Generation in the Multiterminal Source Model
Communication complexity refers to the minimum rate of public communication
required for generating a maximal-rate secret key (SK) in the multiterminal
source model of Csiszar and Narayan. Tyagi recently characterized this
communication complexity for a two-terminal system. We extend the ideas in
Tyagi's work to derive a lower bound on communication complexity in the general
multiterminal setting. In the important special case of the complete graph
pairwise independent network (PIN) model, our bound allows us to determine the
exact linear communication complexity, i.e., the communication complexity when
the communication and SK are restricted to be linear functions of the
randomness available at the terminals.Comment: A 5-page version of this manuscript will be submitted to the 2014
IEEE International Symposium on Information Theory (ISIT 2014
Cooperative Data Exchange based on MDS Codes
The cooperative data exchange problem is studied for the fully connected
network. In this problem, each node initially only possesses a subset of the
packets making up the file. Nodes make broadcast transmissions that are
received by all other nodes. The goal is for each node to recover the full
file. In this paper, we present a polynomial-time deterministic algorithm to
compute the optimal (i.e., minimal) number of required broadcast transmissions
and to determine the precise transmissions to be made by the nodes. A
particular feature of our approach is that {\it each} of the
transmissions is a linear combination of {\it exactly} packets, and we
show how to optimally choose the value of We also show how the
coefficients of these linear combinations can be chosen by leveraging a
connection to Maximum Distance Separable (MDS) codes. Moreover, we show that
our method can be used to solve cooperative data exchange problems with
weighted cost as well as the so-called successive local omniscience problem.Comment: 21 pages, 1 figur
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