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

Reliable Inference from Unreliable Agents

Distributed inference using multiple sensors has been an active area of research since the emergence of wireless sensor networks (WSNs). Several researchers have addressed the design issues to ensure optimal inference performance in such networks. The central goal of this thesis is to analyze distributed inference systems with potentially unreliable components and design strategies to ensure reliable inference in such systems. The inference process can be that of detection or estimation or classification, and the components/agents in the system can be sensors and/or humans. The system components can be unreliable due to a variety of reasons: faulty sensors, security attacks causing sensors to send falsified information, or unskilled human workers sending imperfect information. This thesis first quantifies the effect of such unreliable agents on the inference performance of the network and then designs schemes that ensure a reliable overall inference. In the first part of this thesis, we study the case when only sensors are present in the system, referred to as sensor networks. For sensor networks, the presence of malicious sensors, referred to as Byzantines, are considered. Byzantines are sensors that inject false information into the system. In such systems, the effect of Byzantines on the overall inference performance is characterized in terms of the optimal attack strategies. Game-theoretic formulations are explored to analyze two-player interactions. Next, Byzantine mitigation schemes are designed that address the problem from the system\u27s perspective. These mitigation schemes are of two kinds: Byzantine identification schemes and Byzantine tolerant schemes. Using learning based techniques, Byzantine identification schemes are designed that learn the identity of Byzantines in the network and use this information to improve system performance. When such schemes are not possible, Byzantine tolerant schemes using error-correcting codes are developed that tolerate the effect of Byzantines and maintain good performance in the network. Error-correcting codes help in correcting the erroneous information from these Byzantines and thereby counter their attack. The second line of research in this thesis considers humans-only networks, referred to as human networks. A similar research strategy is adopted for human networks where, the effect of unskilled humans sharing beliefs with a central observer called \emph{CEO} is analyzed, and the loss in performance due to the presence of such unskilled humans is characterized. This problem falls under the family of problems in information theory literature referred to as the \emph{CEO Problem}, but for belief sharing. The asymptotic behavior of the minimum achievable mean squared error distortion at the CEO is studied in the limit when the number of agents $L$ and the sum rate $R$ tend to infinity. An intermediate regime of performance between the exponential behavior in discrete CEO problems and the $1/R$ behavior in Gaussian CEO problems is established. This result can be summarized as the fact that sharing beliefs (uniform) is fundamentally easier in terms of convergence rate than sharing measurements (Gaussian), but sharing decisions is even easier (discrete). Besides theoretical analysis, experimental results are reported for experiments designed in collaboration with cognitive psychologists to understand the behavior of humans in the network. The act of fusing decisions from multiple agents is observed for humans and the behavior is statistically modeled using hierarchical Bayesian models. The implications of such modeling on the design of large human-machine systems is discussed. Furthermore, an error-correcting codes based scheme is proposed to improve system performance in the presence of unreliable humans in the inference process. For a crowdsourcing system consisting of unskilled human workers providing unreliable responses, the scheme helps in designing easy-to-perform tasks and also mitigates the effect of erroneous data. The benefits of using the proposed approach in comparison to the majority voting based approach are highlighted using simulated and real datasets. In the final part of the thesis, a human-machine inference framework is developed where humans and machines interact to perform complex tasks in a faster and more efficient manner. A mathematical framework is built to understand the benefits of human-machine collaboration. Such a study is extremely important for current scenarios where humans and machines are constantly interacting with each other to perform even the simplest of tasks. While machines perform best in some tasks, humans still give better results in tasks such as identifying new patterns. By using humans and machines together, one can extract complete information about a phenomenon of interest. Such an architecture, referred to as Human-Machine Inference Networks (HuMaINs), provides promising results for the two cases of human-machine collaboration: \emph{machine as a coach} and \emph{machine as a colleague}. For simple systems, we demonstrate tangible performance gains by such a collaboration which provides design modules for larger, and more complex human-machine systems. However, the details of such larger systems needs to be further explored

Source-channel coding for robust image transmission and for dirty-paper coding

In this dissertation, we studied two seemingly uncorrelated, but conceptually related problems in terms of source-channel coding: 1) wireless image transmission and 2) Costa ("dirty-paper") code design. In the first part of the dissertation, we consider progressive image transmission over a wireless system employing space-time coded OFDM. The space-time coded OFDM system based on a newly built broadband MIMO fading model is theoretically evaluated by assuming perfect channel state information (CSI) at the receiver for coherent detection. Then an adaptive modulation scheme is proposed to pick the constellation size that offers the best reconstructed image quality for each average signal-to-noise ratio (SNR). A more practical scenario is also considered without the assumption of perfect CSI. We employ low-complexity decision-feedback decoding for differentially space- time coded OFDM systems to exploit transmitter diversity. For JSCC, we adopt a product channel code structure that is proven to provide powerful error protection and bursty error correction. To further improve the system performance, we also apply the powerful iterative (turbo) coding techniques and propose the iterative decoding of differentially space-time coded multiple descriptions of images. The second part of the dissertation deals with practical dirty-paper code designs. We first invoke an information-theoretical interpretation of algebraic binning and motivate the code design guidelines in terms of source-channel coding. Then two dirty-paper code designs are proposed. The first is a nested turbo construction based on soft-output trellis-coded quantization (SOTCQ) for source coding and turbo trellis- coded modulation (TTCM) for channel coding. A novel procedure is devised to balance the dimensionalities of the equivalent lattice codes corresponding to SOTCQ and TTCM. The second dirty-paper code design employs TCQ and IRA codes for near-capacity performance. This is done by synergistically combining TCQ with IRA codes so that they work together as well as they do individually. Our TCQ/IRA design approaches the dirty-paper capacity limit at the low rate regime (e.g., < 1:0 bit/sample), while our nested SOTCQ/TTCM scheme provides the best performs so far at medium-to-high rates (e.g., >= 1:0 bit/sample). Thus the two proposed practical code designs are complementary to each other

Quantum information theory

Finally, here is a modern, self-contained text on quantum information theory suitable for graduate-level courses. Developing the subject \u27from the ground up\u27 it covers classical results as well as major advances of the past decade. Beginning with an extensive overview of classical information theory suitable for the non-expert, the author then turns his attention to quantum mechanics for quantum information theory, and the important protocols of teleportation, super-dense coding and entanglement distribution. He develops all of the tools necessary for understanding important results in quantum information theory, including capacity theorems for classical, entanglement-assisted, private and quantum communication. The book also covers important recent developments such as superadditivity of private, coherent and Holevo information, and the superactivation of quantum capacity. This book will be warmly welcomed by the upcoming generation of quantum information theorists and the already established community of classical information theorists

Compress-spread-forward with multiterminal source coding and complete complementary sequences

We propose a new technique, compress-spread forward (CSF), for high-performance wireless streaming from two base stations in parallel. CSF uses multiterminal source coding for efficient source compression and complete complementary sequences for error-free multiple access and synchronization. Our practical design shows significant performance gains due to spatial diversity and distributed source coding

From Classical to Quantum Shannon Theory

The aim of this book is to develop "from the ground up" many of the major, exciting, pre- and post-millenium developments in the general area of study known as quantum Shannon theory. As such, we spend a significant amount of time on quantum mechanics for quantum information theory (Part II), we give a careful study of the important unit protocols of teleportation, super-dense coding, and entanglement distribution (Part III), and we develop many of the tools necessary for understanding information transmission or compression (Part IV). Parts V and VI are the culmination of this book, where all of the tools developed come into play for understanding many of the important results in quantum Shannon theory.Comment: v8: 774 pages, 301 exercises, 81 figures, several corrections; this draft, pre-publication copy is available under a Creative Commons Attribution-NonCommercial-ShareAlike license (see http://creativecommons.org/licenses/by-nc-sa/3.0/), "Quantum Information Theory, Second Edition" is available for purchase from Cambridge University Pres