765 research outputs found
Exponential Strong Converse for Successive Refinement with Causal Decoder Side Information
We consider the -user successive refinement problem with causal decoder
side information and derive an exponential strong converse theorem. The
rate-distortion region for the problem can be derived as a straightforward
extension of the two-user case by Maor and Merhav (2008). We show that for any
rate-distortion tuple outside the rate-distortion region of the -user
successive refinement problem with causal decoder side information, the joint
excess-distortion probability approaches one exponentially fast. Our proof
follows by judiciously adapting the recently proposed strong converse technique
by Oohama using the information spectrum method, the variational form of the
rate-distortion region and H\"older's inequality. The lossy source coding
problem with causal decoder side information considered by El Gamal and
Weissman is a special case () of the current problem. Therefore, the
exponential strong converse theorem for the El Gamal and Weissman problem
follows as a corollary of our result
Information Theoretic Security for Broadcasting of Two Encrypted Sources under Side-Channel Attacks
We consider the secure communication problem for broadcasting of two
encrypted sources. The sender wishes to broadcast two secret messages via two
common key cryptosystems. We assume that the adversary can use the
side-channel, where the side information on common keys can be obtained via the
rate constraint noiseless channel. To solve this problem we formulate the post
encryption coding system. On the information leakage on two secrete messages to
the adversary, we provide an explicit sufficient condition to attain the
exponential decay of this quantity for large block lengths of encrypted
sources.Comment: 13 pages, 4 figures. In the current version we we have corrected
errors in Fig. 2 and Fig. 4. arXiv admin note: substantial text overlap with
arXiv:1801.02563, arXiv:1801.0492
Modulation and Estimation with a Helper
The problem of transmitting a parameter value over an additive white Gaussian
noise (AWGN) channel is considered, where, in addition to the transmitter and
the receiver, there is a helper that observes the noise non-causally and
provides a description of limited rate to the transmitter and/or
the receiver. We derive upper and lower bounds on the optimal achievable
-th moment of the estimation error and show that they coincide for
small values of and for low SNR values. The upper bound relies on a
recently proposed channel-coding scheme that effectively conveys
bits essentially error-free and the rest of the rate - over the same AWGN
channel without help, with the error-free bits allocated to the most
significant bits of the quantized parameter. We then concentrate on the setting
with a total transmit energy constraint, for which we derive achievability
results for both channel coding and parameter modulation for several scenarios:
when the helper assists only the transmitter or only the receiver and knows the
noise, and when the helper assists the transmitter and/or the receiver and
knows both the noise and the message. In particular, for the message-informed
helper that assists both the receiver and the transmitter, it is shown that the
error probability in the channel-coding task decays doubly exponentially.
Finally, we translate these results to those for continuous-time power-limited
AWGN channels with unconstrained bandwidth. As a byproduct, we show that the
capacity with a message-informed helper that is available only at the
transmitter can exceed the capacity of the same scenario when the helper knows
only the noise but not the message.Comment: This work has been submitted to the IEEE for possible publication.
Copyright may be transferred without notice, after which this version may no
longer be accessibl
The Sender-Excited Secret Key Agreement Model: Capacity, Reliability and Secrecy Exponents
We consider the secret key generation problem when sources are randomly
excited by the sender and there is a noiseless public discussion channel. Our
setting is thus similar to recent works on channels with action-dependent
states where the channel state may be influenced by some of the parties
involved. We derive single-letter expressions for the secret key capacity
through a type of source emulation analysis. We also derive lower bounds on the
achievable reliability and secrecy exponents, i.e., the exponential rates of
decay of the probability of decoding error and of the information leakage.
These exponents allow us to determine a set of strongly-achievable secret key
rates. For degraded eavesdroppers the maximum strongly-achievable rate equals
the secret key capacity; our exponents can also be specialized to previously
known results.
In deriving our strong achievability results we introduce a coding scheme
that combines wiretap coding (to excite the channel) and key extraction (to
distill keys from residual randomness). The secret key capacity is naturally
seen to be a combination of both source- and channel-type randomness. Through
examples we illustrate a fundamental interplay between the portion of the
secret key rate due to each type of randomness. We also illustrate inherent
tradeoffs between the achievable reliability and secrecy exponents. Our new
scheme also naturally accommodates rate limits on the public discussion. We
show that under rate constraints we are able to achieve larger rates than those
that can be attained through a pure source emulation strategy.Comment: 18 pages, 8 figures; Submitted to the IEEE Transactions on
Information Theory; Revised in Oct 201
Brascamp-Lieb Inequality and Its Reverse: An Information Theoretic View
We generalize a result by Carlen and Cordero-Erausquin on the equivalence
between the Brascamp-Lieb inequality and the subadditivity of relative entropy
by allowing for random transformations (a broadcast channel). This leads to a
unified perspective on several functional inequalities that have been gaining
popularity in the context of proving impossibility results. We demonstrate that
the information theoretic dual of the Brascamp-Lieb inequality is a convenient
setting for proving properties such as data processing, tensorization,
convexity and Gaussian optimality. Consequences of the latter include an
extension of the Brascamp-Lieb inequality allowing for Gaussian random
transformations, the determination of the multivariate Wyner common information
for Gaussian sources, and a multivariate version of Nelson's hypercontractivity
theorem. Finally we present an information theoretic characterization of a
reverse Brascamp-Lieb inequality involving a random transformation (a multiple
access channel).Comment: 5 pages; to be presented at ISIT 201
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