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Stark C. Draper

Publications and source records attributed to Stark C. Draper.

46 records · Page 3Linked to original sources

Secret Key Generation from Sparse Wireless Channels: Ergodic Capacity and Secrecy Outage

This paper investigates generation of a secret key from a reciprocal wireless channel. In particular we consider wireless channels that exhibit sparse structure in the wideband regime and the impact of the sparsity on the secret key capacity. We explore this problem in two steps. First, we study key generation from a state-dependent discrete memoryless multiple source. The state of source captures the effect of channel sparsity. Secondly, we consider a wireless channel model that captures channel sparsity and correlation between the legitimate users' channel and the eavesdropper's channel. Such dependency can significantly reduce the secret key capacity. According to system delay requirements, two performance measures are considered: (i) ergodic secret key capacity and (ii) outage probability. We show that in the wideband regime when a white sounding sequence is adopted, a sparser channel can achieve a higher ergodic secret key rate than a richer channel can. For outage performance, we show that if the users generate secret keys at a fraction of the ergodic capacity, the outage probability will decay exponentially in signal bandwidth. Moreover, a larger exponent is achieved by a richer channel.

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The AWGN Red Alert Problem

Consider the following unequal error protection scenario. One special message, dubbed the "red alert" message, is required to have an extremely small probability of missed detection. The remainder of the messages must keep their average probability of error and probability of false alarm below a certain threshold. The goal then is to design a codebook that maximizes the error exponent of the red alert message while ensuring that the average probability of error and probability of false alarm go to zero as the blocklength goes to infinity. This red alert exponent has previously been characterized for discrete memoryless channels. This paper completely characterizes the optimal red alert exponent for additive white Gaussian noise channels with block power constraints.

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Optimal Backpressure Scheduling in Wireless Networks using Mutual Information Accumulation

In this paper we develop scheduling policies that maximize the stability region of a wireless network under the assumption that mutual information accumulation is implemented at the physical layer. When the link quality between nodes is not sufficiently high that a packet can be decoded within a single slot, the system can accumulate information across multiple slots, eventually decoding the packet. The result is an expanded stability region. The accumulation process over weak links is temporally coupled and therefore does not satisfy the independent and identically distributed (i.i.d) assumption that underlies many previous analysis in this area. Therefore the problem setting also poses new analytic challenges. We propose two dynamic scheduling algorithms to cope with the non-i.i.d nature of the decoding. The first performs scheduling every $T$ slots, and approaches the boundary of the stability region as $T$ gets large, but at the cost of increased average delay. The second introduces virtual queues for each link and constructs a virtual system wherein two virtual nodes are introduced for each link. The constructed virtual system is shown to have the same stability region as the original system. Through controlling the virtual queues in the constructed system, we avoid the non-i.i.d analysis difficulty and attain the full stability region. We derive performance bounds for both algorithms and compare them through simulation results.

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Exploiting Channel Diversity in Secret Key Generation from Multipath Fading Randomness

We design and analyze a method to extract secret keys from the randomness inherent to wireless channels. We study a channel model for multipath wireless channel and exploit the channel diversity in generating secret key bits. We compare the key extraction methods based both on entire channel state information (CSI) and on single channel parameter such as the received signal strength indicators (RSSI). Due to the reduction in the degree-of-freedom when going from CSI to RSSI, the rate of key extraction based on CSI is far higher than that based on RSSI. This suggests that exploiting channel diversity and making CSI information available to higher layers would greatly benefit the secret key generation. We propose a key generation system based on low-density parity-check (LDPC) codes and describe the design and performance of two systems: one based on binary LDPC codes and the other (useful at higher signal-to-noise ratios) based on four-ary LDPC codes.

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A Theoretical Analysis of Authentication, Privacy and Reusability Across Secure Biometric Systems

We present a theoretical framework for the analysis of privacy and security tradeoffs in secure biometric authentication systems. We use this framework to conduct a comparative information-theoretic analysis of two biometric systems that are based on linear error correction codes, namely fuzzy commitment and secure sketches. We derive upper bounds for the probability of false rejection ($P_{FR}$) and false acceptance ($P_{FA}$) for these systems. We use mutual information to quantify the information leaked about a user's biometric identity, in the scenario where one or multiple biometric enrollments of the user are fully or partially compromised. We also quantify the probability of successful attack ($P_{SA}$) based on the compromised information. Our analysis reveals that fuzzy commitment and secure sketch systems have identical $P_{FR}, P_{FA}, P_{SA}$ and information leakage, but secure sketch systems have lower storage requirements. We analyze both single-factor (keyless) and two-factor (key-based) variants of secure biometrics, and consider the most general scenarios in which a single user may provide noisy biometric enrollments at several access control devices, some of which may be subsequently compromised by an attacker. Our analysis highlights the revocability and reusability properties of key-based systems and exposes a subtle design tradeoff between reducing information leakage from compromised systems and preventing successful attacks on systems whose data have not been compromised.

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Rank Minimization over Finite Fields: Fundamental Limits and Coding-Theoretic Interpretations

This paper establishes information-theoretic limits in estimating a finite field low-rank matrix given random linear measurements of it. These linear measurements are obtained by taking inner products of the low-rank matrix with random sensing matrices. Necessary and sufficient conditions on the number of measurements required are provided. It is shown that these conditions are sharp and the minimum-rank decoder is asymptotically optimal. The reliability function of this decoder is also derived by appealing to de Caen's lower bound on the probability of a union. The sufficient condition also holds when the sensing matrices are sparse - a scenario that may be amenable to efficient decoding. More precisely, it is shown that if the n\times n-sensing matrices contain, on average, Ω(nlog n) entries, the number of measurements required is the same as that when the sensing matrices are dense and contain entries drawn uniformly at random from the field. Analogies are drawn between the above results and rank-metric codes in the coding theory literature. In fact, we are also strongly motivated by understanding when minimum rank distance decoding of random rank-metric codes succeeds. To this end, we derive distance properties of equiprobable and sparse rank-metric codes. These distance properties provide a precise geometric interpretation of the fact that the sparse ensemble requires as few measurements as the dense one. Finally, we provide a non-exhaustive procedure to search for the unknown low-rank matrix.

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Hierarchical and High-Girth QC LDPC Codes

We present a general approach to designing capacity-approaching high-girth low-density parity-check (LDPC) codes that are friendly to hardware implementation. Our methodology starts by defining a new class of "hierarchical" quasi-cyclic (HQC) LDPC codes that generalizes the structure of quasi-cyclic (QC) LDPC codes. Whereas the parity check matrices of QC LDPC codes are composed of circulant sub-matrices, those of HQC LDPC codes are composed of a hierarchy of circulant sub-matrices that are in turn constructed from circulant sub-matrices, and so on, through some number of levels. We show how to map any class of codes defined using a protograph into a family of HQC LDPC codes. Next, we present a girth-maximizing algorithm that optimizes the degrees of freedom within the family of codes to yield a high-girth HQC LDPC code. Finally, we discuss how certain characteristics of a code protograph will lead to inevitable short cycles, and show that these short cycles can be eliminated using a "squashing" procedure that results in a high-girth QC LDPC code, although not a hierarchical one. We illustrate our approach with designed examples of girth-10 QC LDPC codes obtained from protographs of one-sided spatially-coupled codes.

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Key Generation Using External Source Excitation: Capacity, Reliability, and Secrecy Exponent

We study the fundamental limits to secret key generation from an excited distributed source (EDS). In an EDS a pair of terminals observe dependent sources of randomness excited by a pre-arranged signal. We first determine the secret key capacity for such systems with one-way public messaging. We then characterize a tradeoff between the secret key rate and exponential bounds on the probability of key agreement failure and on the secrecy of the key generated. We find that there is a fundamental tradeoff between reliability and secrecy. We then explore this framework within the context of reciprocal wireless channels. In this setting, the users transmit pre-arranged excitation signals to each other. When the fading is Rayleigh, the observations of the users are jointly Gaussian sources. We show that an on-off excitation signal with an SNR-dependent duty cycle achieves the secret key capacity of this system. Furthermore, we characterize a fundamental metric -- minimum energy per key bit for reliable key generation -- and show that in contrast to conventional AWGN channels, there is a non-zero threshold SNR that achieves the minimum energy per key bit. The capacity achieving on-off excitation signal achieves the minimum energy per key bit at any SNR below the threshold. Finally, we build off our error exponent results to investigate the energy required to generate a key using a finite block length. Again we find that on-off excitation signals yield an improvement when compared to constant excitation signals. In addition to Rayleigh fading, we analyze the performance of a system based on binary channel phase quantization.

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Divide & Concur and Difference-Map BP Decoders for LDPC Codes

The "Divide and Concur'' (DC) algorithm, recently introduced by Gravel and Elser, can be considered a competitor to the belief propagation (BP) algorithm, in that both algorithms can be applied to a wide variety of constraint satisfaction, optimization, and probabilistic inference problems. We show that DC can be interpreted as a message-passing algorithm on a constraint graph, which helps make the comparison with BP more clear. The "difference-map'' dynamics of the DC algorithm enables it to avoid "traps'' which may be related to the "trapping sets'' or "pseudo-codewords'' that plague BP decoders of low-density parity check (LDPC) codes in the error-floor regime. We investigate two decoders for low-density parity-check (LDPC) codes based on these ideas. The first decoder is based directly on DC, while the second decoder borrows the important "difference-map'' concept from the DC algorithm and translates it into a BP-like decoder. We show that this "difference-map belief propagation'' (DMBP) decoder has dramatically improved error-floor performance compared to standard BP decoders, while maintaining a similar computational complexity. We present simulation results for LDPC codes on the additive white Gaussian noise and binary symmetric channels, comparing DC and DMBP decoders with other decoders based on BP, linear programming, and mixed-integer linear programming.

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Cooperative Routing for Wireless Networks using Mutual-Information Accumulation

Cooperation between the nodes of wireless multihop networks can increase communication reliability, reduce energy consumption, and decrease latency. The possible improvements are even greater when nodes perform mutual information accumulation using rateless codes. In this paper, we investigate routing problems in such networks. Given a network, a source, and a destination, our objective is to minimize end-to-end transmission delay under energy and bandwidth constraints. We provide an algorithm that determines which nodes should participate in forwarding the message and what resources (time, energy, bandwidth) should be allocated to each. Our approach factors into two sub-problems, each of which can be solved efficiently. For any transmission order we show that solving for the optimum resource allocation can be formulated as a linear programming problem. We then show that the transmission order can be improved systematically by swapping nodes based on the solution of the linear program. Solving a sequence of linear programs leads to a locally optimal solution in a very efficient manner. In comparison to the proposed cooperative routing solution, it is observed that conventional shortest path multihop routing typically incurs additional delays and energy expenditures on the order of 70%. Our first algorithm is centralized, assuming that routing computations can be done at a central processor with full access to channel state information for the entire system. We also design two distributed routing algorithms that require only local channel state information. We provide simulations showing that for the same networks the distributed algorithms find routes that are only about two to five percent less efficient than the centralized algorithm.

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