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arXiv:1409.5844 · IEEE Transactions on Information Theory

Key Capacity for Product Sources With Application to Stationary Gaussian Processes

Jingbo Liu, Paul Cuff, Sergio Verdú

Published Dec 10, 2015• 21 citations
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Abstract

We show that for product sources, rate splitting is optimal for secret key agreement using limited one-way communication between two terminals. This yields an alternative proof of the tensorization property of a strong data processing inequality originally studied by Erkip and Cover and amended recently by Anantharam et al. We derive a water-filling solution of the communication-rate-key-rate tradeoff for a wide class of discrete memoryless vector Gaussian sources which subsumes the case without an eavesdropper. Moreover, we derive an explicit formula for the maximum secret key per bit of communication for all discrete memoryless vector Gaussian sources using a tensorization property and a variation on the enhanced channel technique of Weingarten et al. Finally, a one-shot information spectrum achievability bound for key generation is proved from which we characterize the communication-rate-key-rate tradeoff for stationary Gaussian processes.

DOI
10.1109/tit.2015.2507602

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We show that for product sources, rate splitting is optimal for secret key agreement using limited one-way communication between two terminals. This yields an alternative proof of the tensorization property of a strong data processing inequality originally studied by Erkip and Cover and amended recently by Anantharam et al. We derive a water-filling solution of the communication-rate-key-rate tradeoff for a wide class of discrete memoryless vector Gaussian sources which subsumes the case without an eavesdropper. Moreover, we derive an explicit formula for the maximum secret key per bit of communication for all discrete memoryless vector Gaussian sources using a tensorization property and a variation on the enhanced channel technique of Weingarten et al. Finally, a one-shot information spectrum achievability bound for key generation is proved from which we characterize the communication-rate-key-rate tradeoff for stationary Gaussian processes.
We show that for product sources, rate splitting is optimal for secret key agreement using limited one-way communication at two terminals. This yields an alternative proof of the tensorization property of a strong data processing inequality originally studied by Erkip and Cover and amended recently by Anantharam et al. We derive a `water-filling' solution of the communication rate-key rate tradeoff for two arbitrarily correlated vector Gaussian sources, for the case with an eavesdropper, and for stationary Gaussian processes.
We investigate the secret key agreement from correlated vector Gaussian sources in which the legitimate parties can use the public communication with limited rate. For the class of protocols with the one-way public communication, we show that the optimal trade-off between the rate of key generation and the rate of the public communication is characterized as an optimization problem of a Gaussian random variable. The characterization is derived by using the enhancement technique introduced by Weingarten et. al. for MIMO Gaussian broadcast channel.
We investigate the secret key agreement from correlated vector Gaussian sources in which legitimate parties can use public communication with limited rate. For the class of protocols with one-way public communication, we show that the optimal trade-off between the rate of key generation and the rate of the public communication is characterized as an optimization problem of a Gaussian random variable. The characterization is derived by using the enhancement technique introduced by Weingarten for multiple-input-multiple-output (MIMO) Gaussian broadcast channel.
In this paper, we consider the problem of secret key generation with one-way communication through both a rate-limited public channel and a rate-limited secure channels where the public channel is from Alice to Bob and Eve and the secure channel is from Alice to Bob. In this model, we do not pose any constraints on the sources, i.e. Bob is not degraded to or less noisy than Eve. We obtain the optimal secret key rate in this problem, both for the discrete memoryless sources and vector Gaussian sources. The vector Gaussian characterization is derived by suitably applying the enhancement argument, and Proving a new extremal inequality. The extremal inequality can be seen as coupling of two extremal inequalities, which are related to the degraded compound MIMO Gaussian broadcast channel, and the vector generalization of Costa's entropy power inequality, accordingly.
In this paper, we consider the problem of secret key generation with one-way communication through both a rate-limited public channel and a rate-limited secure channels where the public channel is from Alice to Bob and Eve and the secure channel is from Alice to Bob. In this model, we do not pose any constraints on the sources, i.e. Bob is not degraded to or less noisy than Eve. We obtain the optimal secret key rate in this problem, both for the discrete memoryless sources and vector Gaussian sources. The vector Gaussian characterization is derived by suitably applying the enhancement argument, and proving a new extremal inequality. The extremal inequality can be seen as coupling of two extremal inequalities, which are related to the degraded compound MIMO Gaussian broadcast channel, and the vector generalization of Costa's entropy power inequality, accordingly.
We consider secret key (SK) generation by two terminals, each of which observes an i.i.d. source which is correlated with the other; the two sources are jointly Gaussian with known distribution. The terminals are then allowed to communicate with each other, possibly interactively in many rounds and without rate restrictions, over a noiseless two-way public channel. Randomization is permitted at each terminal. The goal is for the terminals to generate a SK comprising discrete common randomness which is concealed from an eavesdropper that has access to the public interterminal communication. We establish that this maximum rate of SK generation, i.e., SK capacity, is, as expected, the per-symbol mutual information I of the correlated sources. Our main technical contribution is a new scheme for achieving SK capacity using structured codes. In our scheme, vector quantization at rate R nats/source symbol of the source at terminal 1 is performed by employing nested lattice codes with dithering. Then, a SK is generated by the terminals from the quantized random sequence at terminal 1 and the (unquantized) Gaussian source at terminal 2, using both lossy and lossless data compression techniques. Our algorithm, using a rate-R lattice quantizer, achieves a maximum SK rate of 1/2 log 1/(e-2I+(1-e-2I)e-2R), which tends to the SK capacity I with increasing R. Thus, our result also specifies the associated tradeoff between quantization rate R and the maximum achievable SK rate. This tradeoff is shown to be optimum among a certain restricted class of schemes for SK generation that involve quantization at rate R of a Gaussian source at one terminal.