224 research outputs found
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
INFORMATION THEORETIC SECRET KEY GENERATION: STRUCTURED CODES AND TREE PACKING
This dissertation deals with a multiterminal source model for
secret key generation by multiple network terminals with prior and
privileged access to a set of correlated signals complemented by
public discussion among themselves. Emphasis is placed on a
characterization of secret key capacity, i.e., the largest rate of
an achievable secret key, and on algorithms for key construction.
Various information theoretic security requirements of increasing
stringency: weak, strong and perfect secrecy, as well as different
types of sources: finite-valued and continuous, are studied.
Specifically, three different models are investigated.
First, we consider strong secrecy generation for a
discrete multiterminal source model. We discover a
connection between secret key capacity and a new
source coding concept of ``minimum information rate for signal dissemination,''
that is of independent interest in multiterminal data compression.
Our main contribution is to show for this discrete model
that structured linear codes suffice to generate a
strong secret key of the best rate.
Second, strong secrecy generation is considered for models with
continuous observations, in particular jointly Gaussian signals.
In the absence of suitable analogs of source coding notions for
the previous discrete model, new techniques are required for a
characterization of secret key capacity as well as for the design
of algorithms for secret key generation. Our proof of the secret
key capacity result, in particular the converse proof, as well as
our capacity-achieving algorithms for secret key construction
based on structured codes and quantization for a model with two
terminals, constitute the two main contributions for this second
model.
Last, we turn our attention to perfect secrecy generation for
fixed signal observation lengths as well as for their asymptotic
limits. In contrast with the analysis of the previous two models
that relies on probabilistic techniques, perfect secret key
generation bears the essence of ``zero-error information theory,''
and accordingly, we rely on mathematical techniques of a
combinatorial nature. The model under consideration is the
``Pairwise Independent Network'' (PIN) model in which every pair
of terminals share a random binary string, with the strings shared
by distinct pairs of terminals being mutually independent. This
model, which is motivated by practical aspects of a wireless
communication network in which terminals communicate on the same
frequency, results in three main contributions. First, the
concept of perfect omniscience in data compression leads to a
single-letter formula for the perfect secret key capacity of the
PIN model; moreover, this capacity is shown to be achieved by
linear noninteractive public communication, and coincides with
strong secret key capacity. Second, taking advantage of a
multigraph representation of the PIN model, we put forth an
efficient algorithm for perfect secret key generation based on a
combinatorial concept of maximal packing of Steiner trees of the
multigraph. When all the terminals seek to share perfect secrecy,
the algorithm is shown to achieve capacity. When only a subset of
terminals wish to share perfect secrecy, the algorithm is shown to
achieve at least half of it. Additionally, we obtain nonasymptotic
and asymptotic bounds on the size and rate of the best perfect
secret key generated by the algorithm. These bounds are of
independent interest from a purely graph theoretic viewpoint as
they constitute new estimates for the maximum size and rate of
Steiner tree packing of a given multigraph. Third, a particular
configuration of the PIN model arises when a lone ``helper''
terminal aids all the other ``user'' terminals generate perfect
secrecy. This model has special features that enable us to obtain
necessary and sufficient conditions for Steiner tree packing to
achieve perfect secret key capacity
Secret Key Agreement under Discussion Rate Constraints
For the multiterminal secret key agreement problem, new single-letter lower
bounds are obtained on the public discussion rate required to achieve any given
secret key rate below the secrecy capacity. The results apply to general source
model without helpers or wiretapper's side information but can be strengthened
for hypergraphical sources. In particular, for the pairwise independent
network, the results give rise to a complete characterization of the maximum
secret key rate achievable under a constraint on the total discussion rate
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
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Dagstuhl Reports : Volume 1, Issue 2, February 2011
Online Privacy: Towards Informational Self-Determination on the Internet (Dagstuhl Perspectives Workshop 11061) : Simone Fischer-Hübner, Chris Hoofnagle, Kai Rannenberg, Michael Waidner, Ioannis Krontiris and Michael Marhöfer Self-Repairing Programs (Dagstuhl Seminar 11062) : Mauro Pezzé, Martin C. Rinard, Westley Weimer and Andreas Zeller Theory and Applications of Graph Searching Problems (Dagstuhl Seminar 11071) : Fedor V. Fomin, Pierre Fraigniaud, Stephan Kreutzer and Dimitrios M. Thilikos Combinatorial and Algorithmic Aspects of Sequence Processing (Dagstuhl Seminar 11081) : Maxime Crochemore, Lila Kari, Mehryar Mohri and Dirk Nowotka Packing and Scheduling Algorithms for Information and Communication Services (Dagstuhl Seminar 11091) Klaus Jansen, Claire Mathieu, Hadas Shachnai and Neal E. Youn
Differentially Private Release and Learning of Threshold Functions
We prove new upper and lower bounds on the sample complexity of differentially private algorithms for releasing approximate answers to
threshold functions. A threshold function over a totally ordered domain
evaluates to if , and evaluates to otherwise. We
give the first nontrivial lower bound for releasing thresholds with
differential privacy, showing that the task is impossible
over an infinite domain , and moreover requires sample complexity , which grows with the size of the domain. Inspired by the
techniques used to prove this lower bound, we give an algorithm for releasing
thresholds with samples. This improves the
previous best upper bound of (Beimel et al., RANDOM
'13).
Our sample complexity upper and lower bounds also apply to the tasks of
learning distributions with respect to Kolmogorov distance and of properly PAC
learning thresholds with differential privacy. The lower bound gives the first
separation between the sample complexity of properly learning a concept class
with differential privacy and learning without privacy. For
properly learning thresholds in dimensions, this lower bound extends to
.
To obtain our results, we give reductions in both directions from releasing
and properly learning thresholds and the simpler interior point problem. Given
a database of elements from , the interior point problem asks for an
element between the smallest and largest elements in . We introduce new
recursive constructions for bounding the sample complexity of the interior
point problem, as well as further reductions and techniques for proving
impossibility results for other basic problems in differential privacy.Comment: 43 page
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