Gaussian Secure Source Coding and Wyner's Common Information

We study secure source-coding with causal disclosure, under the Gaussian distribution. The optimality of Gaussian auxiliary random variables is shown in various scenarios. We explicitly characterize the tradeoff between the rates of communication and secret key. This tradeoff is the result of a mutual information optimization under Markov constraints. As a corollary, we deduce a general formula for Wyner's Common Information in the Gaussian setting.Comment: ISIT 2015, 5 pages, uses IEEEtran.cl

Computing the Rate-Distortion Function of Gray-Wyner System

In this paper, the rate-distortion theory of Gray-Wyner lossy source coding system is investigated. An iterative algorithm is proposed to compute rate-distortion function for general successive source. For the case of jointly Gaussian distributed sources, the Lagrangian analysis of scalable source coding in [1] is generalized to the Gray-Wyner instance. Upon the existing single-letter characterization of the rate-distortion region, we compute and determine an analytical expression of the rate-distortion function under quadratic distortion constraints. According to the rate-distortion function, another approach, different from Viswanatha et al. used, is provided to compute Wyner's Common Information. The convergence of proposed iterative algorithm, RD function with different parameters and the projection plane of RD region are also shown via numerical simulations at last.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

Function Computation over Networks:Efficient Information Processing for Cache and Sensor Applications

This thesis looks at efficient information processing for two network applications: content delivery with caching and collecting summary statistics in wireless sensor networks. Both applications are studied under the same paradigm: function computation over networks, where distributed source nodes cooperatively communicate some functions of individual observations to one or multiple destinations. One approach that always works is to convey all observations and then let the destinations compute the desired functions by themselves. However, if the available communication resources are limited, then revealing less unwanted information becomes critical. Centered on this goal, this thesis develops new coding schemes using information-theoretic tools. The first part of this thesis focuses on content delivery with caching. Caching is a technique that facilitates reallocation of communication resources in order to avoid network congestion during peak-traffic times. An information-theoretic model, termed sequential coding for computing, is proposed to analyze the potential gains offered by the caching technique. For the single-user case, the proposed framework succeeds in verifying the optimality of some simple caching strategies and in providing guidance towards optimal caching strategies. For the two-user case, five representative subproblems are considered, which draw connections with classic source coding problems including the Gray-Wyner system, successive refinement, and the Kaspi/Heegard-Berger problem. Afterwards, the problem of distributed computing with successive refinement is considered. It is shown that if full data recovery is required in the second stage of successive refinement, then any information acquired in the first stage will be useful later in the second stage. The second part of this thesis looks at the collection of summary statistics in wireless sensor networks. Summary statistics include arithmetic mean, median, standard deviation, etc, and they belong to the class of symmetric functions. This thesis develops arithmetic computation coding in order to efficiently perform in-network computation for weighted arithmetic sums and symmetric functions. The developed arithmetic computation coding increases the achievable computation rate from $\Theta((\log L)/L)$ to $\Theta(1/\log L)$, where $L$ is the number of sensors. Finally, this thesis demonstrates that interaction among sensors is beneficial for computation of type-threshold functions, e.g., the maximum and the indicator function, and that a non-vanishing computation rate is achievable

Physical layer security performance study for Two-Hop wireless networks with Buffer-Aided relay selection

指導教員：姜　暁

Algorithms and architecture for multiusers, multi-terminal, multi-layer information theoretic security

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.Includes bibliographical references (p. 161-164).As modern infrastructure systems become increasingly more complex, we are faced with many new challenges in the area of information security. In this thesis we examine some approaches to security based on ideas from information theory. The protocols considered in this thesis, build upon the "wiretap channel," a model for physical layer security proposed by A. Wyner in 1975. At a higher level, the protocols considered here can strengthen existing mechanisms for security by providing a new location based approach at the physical layer.In the first part of this thesis, we extend the wiretap channel model to the case when there are multiple receivers, each experiencing a time varying fading channel. Both the scenario when each legitimate receiver wants a common message as well as the scenario when they all want separate messages are studied and capacity results are established in several special cases. When each receiver wants a separate independent message, an opportunistic scheme that transmits to the strongest user at each time, and uses Gaussian codebooks is shown to achieve the sum secrecy capacity in the limit of many users. When each receiver wants a common message, a lower bound to the capacity is provided, independent of the number of receivers. In the second part of the thesis the role of multiple antennas for secure communication studied. We establish the secrecy capacity of the multi antenna wiretap channel (MIMOME channel), when the channel matrices of the legitimate receiver and eavesdropper are fixed and known to all the terminals. To establish the capacity, a new computable upper bound on the secrecy capacity of the wiretap channel is developed, which may be of independent interest. It is shown that Gaussian codebooks suffice to attain the capacity for this problem. For the case when the legitimate receiver has a single antenna (MISOME channel) a rank one transmission scheme is shown to attain the capacity.(CONT.) In the high signal-to-noise ratio (SNR) regime, it is shown that a capacity achieving scheme involves simultaneous diagonalization of the channel matrices using the generalized singular value decomposition and independently coding accross the resulting parallel channels. Furthermore a semi-blind masked beamforming scheme is studied, which transmits signal of interest in the subspace of the legitimate receiver's channel and synthetic noise in the orthogonal subspace. It is shown that this scheme is nearly optimal in the high SNR regime for the MISOME case and the performance penalty for the MIMOME channel is evaluated in terms of the generalized singular values. The behavior of the secrecy capacity in the limit of many antennas is also studied. When the channel matrices have i.i.d. CN(O, 1) entries, we show that (1) the secrecy capacity for the MISOME channel converges (almost surely) to zero if and only if the eavesdropper increases its antennas at a rate twice as fast as the sender (2) when a total of T >> 1 antennas have to be allocated between the sender and the receiver, the optimal allocation, which maximizes the number of eavesdropping antennas for zero secrecy capacity is 2 : 1. In the final part of the thesis, we consider a variation of the wiretap channel where the sender and legitimate receiver also have access to correlated source sequences. They use both the sources and the structure of the underlying channel to extract secret keys. We provide general upper and lower bounds on the secret key rate and establish the capacity for the reversely degraded case.by Ashish Khisti.Ph.D

Coding mechanisms for communication and compression : analysis of wireless channels and DNA sequencing

textThis thesis comprises of two related but distinct components: Coding arguments for communication channels and information-theoretic analysis for haplotype assembly. The common thread for both problems is utilizing information and coding theoretic principles in understanding their underlying mechanisms. For the first class of problems, I study two practical challenges that prevent optimal discrete codes utilizing in real communication and compression systems, namely, coding over analog alphabet and fading. In particular, I use an expansion coding scheme to convert the original analog channel coding and source coding problems into a set of independent discrete subproblems. By adopting optimal discrete codes over the expanded levels, this low-complexity coding scheme can approach Shannon limit perfectly or in ratio. Meanwhile, I design a polar coding scheme to deal with the unstable state of fading channels. This novel coding mechanism of hierarchically utilizing different types of polar codes has been proved to be ergodic capacity achievable for several fading systems, without channel state information known at the transmitter. For the second class of problems, I build an information-theoretic view for haplotype assembly. More precisely, the recovery of the target pair of haplotype sequences using short reads is rephrased as the joint source-channel coding problem. Two binary messages, representing haplotypes and chromosome memberships of reads, are encoded and transmitted over a channel with erasures and errors, where the channel model reflects salient features of highthroughput sequencing. The focus is on determining the required number of reads for reliable haplotype reconstruction.Electrical and Computer Engineerin

Systematic hybrid analog/digital signal coding

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000.Includes bibliographical references (p. 201-206).This thesis develops low-latency, low-complexity signal processing solutions for systematic source coding, or source coding with side information at the decoder. We consider an analog source signal transmitted through a hybrid channel that is the composition of two channels: a noisy analog channel through which the source is sent unprocessed and a secondary rate-constrained digital channel; the source is processed prior to transmission through the digital channel. The challenge is to design a digital encoder and decoder that provide a minimum-distortion reconstruction of the source at the decoder, which has observations of analog and digital channel outputs. The methods described in this thesis have importance to a wide array of applications. For example, in the case of in-band on-channel (IBOC) digital audio broadcast (DAB), an existing noisy analog communications infrastructure may be augmented by a low-bandwidth digital side channel for improved fidelity, while compatibility with existing analog receivers is preserved. Another application is a source coding scheme which devotes a fraction of available bandwidth to the analog source and the rest of the bandwidth to a digital representation. This scheme is applicable in a wireless communications environment (or any environment with unknown SNR), where analog transmission has the advantage of a gentle roll-off of fidelity with SNR. A very general paradigm for low-latency, low-complexity source coding is composed of three basic cascaded elements: 1) a space rotation, or transformation, 2) quantization, and 3) lossless bitstream coding. The paradigm has been applied with great success to conventional source coding, and it applies equally well to systematic source coding. Focusing on the case involving a Gaussian source, Gaussian channel and mean-squared distortion, we determine optimal or near-optimal components for each of the three elements, each of which has analogous components in conventional source coding. The space rotation can take many forms such as linear block transforms, lapped transforms, or subband decomposition, all for which we derive conditions of optimality. For a very general case we develop algorithms for the design of locally optimal quantizers. For the Gaussian case, we describe a low-complexity scalar quantizer, the nested lattice scalar quantizer, that has performance very near that of the optimal systematic scalar quantizer. Analogous to entropy coding for conventional source coding, Slepian-Wolf coding is shown to be an effective lossless bitstream coding stage for systematic source coding.by Richard J. Barron.Ph.D

Physical-Layer Communications Using Direct Antenna Modulation

Conventional wireless communications could be threatened by an eavesdropper with a sufficiently sensitive receiver and unlimited computational resources, or may reach the channel capacity in the near future. Recent research into a new digital modulation technique termed Direct Antenna Modulation (DAM) shows that DAM is a potential solution to the aforementioned problems. Direction-dependency, which describes the manner of signal transmission, is the most important attribute of a DAM system. Direction-dependent transmission can provide extra protection from a physical-layer source against security attack. Various transmission schemes are discussed in this work, and it is shown that accurate demodulation can be prevented from eavesdropping in the following two scenarios: first, when the angular separation between eavesdropper and intended recipient is very small; second, when one or two eavesdropping directions are pre-known. In addition, DAM system can be configured to have extra channel resources by introducing space as an additional domain for multiplexing. With the technique of space multiplexing, the transmitter can send independent data streams towards multiple receivers located at various transmission directions simultaneously. An algorithmic method is also presented to provide space multiplexing with a relatively low system cost

Generating secret in a network

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 247-253) and index.This monograph studies the theory of information through the multiuser secret key agreement problem. A general notion of mutual dependence is established for the secrecy capacity, as a natural generalization of Shannon's mutual information to the multivariate case. Under linear-type source models, this capacity can be achieved practically by linear network codes. In addition to being an unusual application of the network coding solution to a secrecy problem, it gives secrecy capacity an interpretation of network information flow and partition connectivity, further confirming the intuitive meaning of secrecy capacity as mutual dependence. New identities in submodular function optimization and matroid theory are discovered in proving these results. A framework is also developed to view matroids as graphs, allowing certain theory on graphs to generalize to matroids. In order to study cooperation schemes in a network, a general channel model with multiple inputs is formulated. Single-letter secrecy capacity upper bounds are derived using the Shearer-type lemma. Lower bounds are obtained with a new cooperation scheme called the mixed source emulation. In the same way that mixed strategies may surpass pure strategies in zero-sum games, mixed source emulation outperforms the conventional pure source emulation approach in terms of the achievable key rate. Necessary and sufficient conditions are derived for tightness of these secrecy bounds, which shows that secrecy capacity can be characterized for a larger class of channels than the broadcast-type channels considered in previous work. The mixed source emulation scheme is also shown to be unnecessary for some channels while insufficient for others. The possibility of a better cooperative scheme becomes apparent, but a general scheme remains to be found.by Chung Chan.Ph.D

Sparse graph codes for compression, sensing, and secrecy

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.Cataloged from student PDF version of thesis.Includes bibliographical references (p. 201-212).Sparse graph codes were first introduced by Gallager over 40 years ago. Over the last two decades, such codes have been the subject of intense research, and capacity approaching sparse graph codes with low complexity encoding and decoding algorithms have been designed for many channels. Motivated by the success of sparse graph codes for channel coding, we explore the use of sparse graph codes for four other problems related to compression, sensing, and security. First, we construct locally encodable and decodable source codes for a simple class of sources. Local encodability refers to the property that when the original source data changes slightly, the compression produced by the source code can be updated easily. Local decodability refers to the property that a single source symbol can be recovered without having to decode the entire source block. Second, we analyze a simple message-passing algorithm for compressed sensing recovery, and show that our algorithm provides a nontrivial f1/f1 guarantee. We also show that very sparse matrices and matrices whose entries must be either 0 or 1 have poor performance with respect to the restricted isometry property for the f2 norm. Third, we analyze the performance of a special class of sparse graph codes, LDPC codes, for the problem of quantizing a uniformly random bit string under Hamming distortion. We show that LDPC codes can come arbitrarily close to the rate-distortion bound using an optimal quantizer. This is a special case of a general result showing a duality between lossy source coding and channel coding-if we ignore computational complexity, then good channel codes are automatically good lossy source codes. We also prove a lower bound on the average degree of vertices in an LDPC code as a function of the gap to the rate-distortion bound. Finally, we construct efficient, capacity-achieving codes for the wiretap channel, a model of communication that allows one to provide information-theoretic, rather than computational, security guarantees. Our main results include the introduction of a new security critertion which is an information-theoretic analog of semantic security, the construction of capacity-achieving codes possessing strong security with nearly linear time encoding and decoding algorithms for any degraded wiretap channel, and the construction of capacity-achieving codes possessing semantic security with linear time encoding and decoding algorithms for erasure wiretap channels. Our analysis relies on a relatively small set of tools. One tool is density evolution, a powerful method for analyzing the behavior of message-passing algorithms on long, random sparse graph codes. Another concept we use extensively is the notion of an expander graph. Expander graphs have powerful properties that allow us to prove adversarial, rather than probabilistic, guarantees for message-passing algorithms. Expander graphs are also useful in the context of the wiretap channel because they provide a method for constructing randomness extractors. Finally, we use several well-known isoperimetric inequalities (Harper's inequality, Azuma's inequality, and the Gaussian Isoperimetric inequality) in our analysis of the duality between lossy source coding and channel coding.by Venkat Bala Chandar.Ph.D