Error Free Perfect Secrecy Systems

Shannon's fundamental bound for perfect secrecy says that the entropy of the secret message cannot be larger than the entropy of the secret key initially shared by the sender and the legitimate receiver. Massey gave an information theoretic proof of this result, however this proof does not require independence of the key and ciphertext. By further assuming independence, we obtain a tighter lower bound, namely that the key entropy is not less than the logarithm of the message sample size in any cipher achieving perfect secrecy, even if the source distribution is fixed. The same bound also applies to the entropy of the ciphertext. The bounds still hold if the secret message has been compressed before encryption. This paper also illustrates that the lower bound only gives the minimum size of the pre-shared secret key. When a cipher system is used multiple times, this is no longer a reasonable measure for the portion of key consumed in each round. Instead, this paper proposes and justifies a new measure for key consumption rate. The existence of a fundamental tradeoff between the expected key consumption and the number of channel uses for conveying a ciphertext is shown. Optimal and nearly optimal secure codes are designed.Comment: Submitted to the IEEE Trans. Info. Theor

Root Cause Analysis Using Graph Representation of Constraints

When several quantitative variables are related through constraints and objectives, it can be difficult to understand why a certain quantity changes in magnitude after certain changes, or why a certain number seems larger or smaller than expected (as compared to a reference value). Large organizations which seek to optimize very large numbers of parameters to achieve constraints such as supply-demand matching face such problems, where the relationships between variables are controlled by a mixture of human processes and software algorithms. This disclosure describes scalable, flexible frameworks and searching techniques that improve transparency, e.g., enable root-cause analysis, for large families of variables. The techniques enable the understanding of the difference between two data-generating flows (versions) with comparable inputs, intermediate variables, and output variables

Immune-Mediated Drug Induced Liver Injury: A Multidisciplinary Approach

This thesis presents an approach to expose relationships between immune mediated drug induced liver injury (IMDILI) and the three-dimensional structural features of toxic drug molecules and their metabolites. The series of analyses test the hypothesis that drugs which produce similar patterns of toxicity interact with targets within common toxicological pathways and that activation of the underlying mechanisms depends on structural similarity among toxic molecules. Spontaneous adverse drug reaction (ADR) reports were used to identify cases of IMDILI. Network map tools were used to compare the known and predicted protein interactions with each of the probe drugs to explore the interactions that are common between the drugs. The IMDILI probe set was then used to develop a pharmacophore model which became the starting point for identifying potential toxicity targets for IMDILI. Pharmacophore screening results demonstrated similarities between the probe IMDILI set of drugs and Toll-Like Receptor 7 (TLR7) agonists, suggesting TLR7 as a potential toxicity target. This thesis highlights the potential for multidisciplinary approaches in the study of complex diseases. Such approaches are particularly helpful for rare diseases where little knowledge is available, and may provide key insights into mechanisms of toxicity that cannot be gleaned from a single disciplinary study

Immune-Mediated Drug Induced Liver Injury: A Multidisciplinary Approach

This thesis presents an approach to expose relationships between immune mediated drug induced liver injury (IMDILI) and the three-dimensional structural features of toxic drug molecules and their metabolites. The series of analyses test the hypothesis that drugs which produce similar patterns of toxicity interact with targets within common toxicological pathways and that activation of the underlying mechanisms depends on structural similarity among toxic molecules. Spontaneous adverse drug reaction (ADR) reports were used to identify cases of IMDILI. Network map tools were used to compare the known and predicted protein interactions with each of the probe drugs to explore the interactions that are common between the drugs. The IMDILI probe set was then used to develop a pharmacophore model which became the starting point for identifying potential toxicity targets for IMDILI. Pharmacophore screening results demonstrated similarities between the probe IMDILI set of drugs and Toll-Like Receptor 7 (TLR7) agonists, suggesting TLR7 as a potential toxicity target. This thesis highlights the potential for multidisciplinary approaches in the study of complex diseases. Such approaches are particularly helpful for rare diseases where little knowledge is available, and may provide key insights into mechanisms of toxicity that cannot be gleaned from a single disciplinary study

Capacity Analysis of Linear Operator Channels over Finite Fields

Motivated by communication through a network employing linear network coding, capacities of linear operator channels (LOCs) with arbitrarily distributed transfer matrices over finite fields are studied. Both the Shannon capacity $C$ and the subspace coding capacity $C_{\text{SS}}$ are analyzed. By establishing and comparing lower bounds on $C$ and upper bounds on $C_{\text{SS}}$, various necessary conditions and sufficient conditions such that $C=C_{\text{SS}}$ are obtained. A new class of LOCs such that $C=C_{\text{SS}}$ is identified, which includes LOCs with uniform-given-rank transfer matrices as special cases. It is also demonstrated that $C_{\text{SS}}$ is strictly less than $C$ for a broad class of LOCs. In general, an optimal subspace coding scheme is difficult to find because it requires to solve the maximization of a non-concave function. However, for a LOC with a unique subspace degradation, $C_{\text{SS}}$ can be obtained by solving a convex optimization problem over rank distribution. Classes of LOCs with a unique subspace degradation are characterized. Since LOCs with uniform-given-rank transfer matrices have unique subspace degradations, some existing results on LOCs with uniform-given-rank transfer matrices are explained from a more general way.Comment: To appear in IEEE Transactions on Information Theor

Teletext data change detection and noiseless data compression

Full Channel teletext system is a high speed data broadcasting system. Pages of information are broadcast in a cyclic manner. The detection of data change in the information pages is necessary for data analysis, database update and retransmission. Lossless data compression is also necessary to enhance the data throughput in rebroadcasting and to reduce the storage requirement. Performing data change detection and data compression in real time using a software approach in a small machine is impossible for such high speed data. In this paper, we describe the algorithms that are suitable for hardware implementation for both data change detection and noiseless data compression.published_or_final_versio

Real time teletext broadcast system performance enhancement using ghost rows

Teletext is a one way broadcast system which provides a variety of services to its users. A one way broadcast system is attractive because a single transmission of a piece of information satisfies all potential users of that information. The main drawback of these types of systems is that a user must wait until the required information arrives. Therefore, the access time is an important consideration in the design of broadcast teletext systems. In addition, another important factor, information update delay, is very significant in real time broadcast teletext. The shortening of this delay is important.for real time applications especially for systems broadcasting financial information. In this paper, we propose to use the ghost rows together with the storage capabilities of modern terminals to shorten both the access time and the update delay. A queueing model is developed to analyse the performance of this new system. ©1997 IEEE.published_or_final_versio

On Linear Operator Channels over Finite Fields

Motivated by linear network coding, communication channels perform linear operation over finite fields, namely linear operator channels (LOCs), are studied in this paper. For such a channel, its output vector is a linear transform of its input vector, and the transformation matrix is randomly and independently generated. The transformation matrix is assumed to remain constant for every T input vectors and to be unknown to both the transmitter and the receiver. There are NO constraints on the distribution of the transformation matrix and the field size. Specifically, the optimality of subspace coding over LOCs is investigated. A lower bound on the maximum achievable rate of subspace coding is obtained and it is shown to be tight for some cases. The maximum achievable rate of constant-dimensional subspace coding is characterized and the loss of rate incurred by using constant-dimensional subspace coding is insignificant. The maximum achievable rate of channel training is close to the lower bound on the maximum achievable rate of subspace coding. Two coding approaches based on channel training are proposed and their performances are evaluated. Our first approach makes use of rank-metric codes and its optimality depends on the existence of maximum rank distance codes. Our second approach applies linear coding and it can achieve the maximum achievable rate of channel training. Our code designs require only the knowledge of the expectation of the rank of the transformation matrix. The second scheme can also be realized ratelessly without a priori knowledge of the channel statistics.Comment: 53 pages, 3 figures, submitted to IEEE Transaction on Information Theor