Foundations of quantitative information flow: Channels, cascades, and the information order

Secrecy is fundamental to computer security, but real systems often cannot avoid leaking some secret information. For this reason, the past decade has seen growing interest in quantitative theories of information flow that allow us to quantify the information being leaked. Within these theories, the system is modeled as an information-theoretic channel that specifies the probability of each output, given each input. Given a prior distribution on those inputs, entropy-like measures quantify the amount of information leakage caused by the channel. ^ This thesis presents new results in the theory of min-entropy leakage. First, we study the perspective of secrecy as a resource that is gradually consumed by a system. We explore this intuition through various models of min-entropy consumption. Next, we consider several composition operators that allow smaller systems to be combined into larger systems, and explore the extent to which the leakage of a combined system is constrained by the leakage of its constituents. Most significantly, we prove upper bounds on the leakage of a cascade of two channels, where the output of the first channel is used as input to the second. In addition, we show how to decompose a channel into a cascade of channels. ^ We also establish fundamental new results about the recently-proposed g-leakage family of measures. These results further highlight the significance of channel cascading. We prove that whenever channel A is composition refined by channel B, that is, whenever A is the cascade of B and R for some channel R, the leakage of A never exceeds that of B, regardless of the prior distribution or leakage measure (Shannon leakage, guessing entropy leakage, min-entropy leakage, or g-leakage). Moreover, we show that composition refinement is a partial order if we quotient away channel structure that is redundant with respect to leakage alone. These results are strengthened by the proof that composition refinement is the only way for one channel to never leak more than another with respect to g-leakage. Therefore, composition refinement robustly answers the question of when a channel is always at least as secure as another from a leakage point of view.^

Two-bit pattern analysis for quantitative information flow

Protecting confidential information from improper disclosure is a fundamental security goal. While encryption and access control are important tools for ensuring confidentiality, they cannot prevent an authorized system from leaking confidential information to its publicly observable outputs, whether inadvertently or maliciously. Hence, secure information flow aims to provide end-to-end control of information flow. Unfortunately, the traditionally-adopted policy of noninterference, which forbids all improper leakage, is often too restrictive. Theories of quantitative information flow address this issue by quantifying the amount of confidential information leaked by a system, with the goal of showing that it is intuitively "small" enough to be tolerated. Given such a theory, it is crucial to develop automated techniques for calculating the leakage in a system. ^ This dissertation is concerned with program analysis for calculating the maximum leakage, or capacity, of confidential information in the context of deterministic systems and under three proposed entropy measures of information leakage: Shannon entropy leakage, min-entropy leakage, and g-leakage. In this context, it turns out that calculating the maximum leakage of a program reduces to counting the number of possible outputs that it can produce. ^ The new approach introduced in this dissertation is to determine two-bit patterns, the relationships among pairs of bits in the output; for instance we might determine that two bits must be unequal. By counting the number of solutions to the two-bit patterns, we obtain an upper bound on the number of possible outputs. Hence, the maximum leakage can be bounded. We first describe a straightforward computation of the two-bit patterns using an automated prover. We then show a more efficient implementation that uses an implication graph to represent the two- bit patterns. It efficiently constructs the graph through the use of an automated prover, random executions, STP counterexamples, and deductive closure. The effectiveness of our techniques, both in terms of efficiency and accuracy, is shown through a number of case studies found in recent literature. ^

THERMODYNAMICS AND APPLICATION OF A PAIR OF SYNTHETIC NUCLEOBASES FROM ARTIFICIALLY EXPANDED GENETIC INFORMATION SYSTEM

UV-melting experiments were performed on 9-mer duplexes containing a pair of synthetic nucleobases P·Z, two members of Expanded Genetic Information System (AEGIS), or P, Z containing mismatches. Enthalpy, entropy and free energy change were derived from simulation using two-state transition model. Nearest neighbor thermodynamic parameters of trimers or tetramers containing P·Z pair or P, Z containing mismatches were derived based on known nearest neighbor parameters. Proposed structures based on thermodynamic parameters are discussed. An application using P·Z pair as reverse selection tool of desired nucleic acid secondary structure is described.

An automatic classifier of emotions built from entropy of noise

The electrocardiogram (ECG) signal has been widely used to study the physiological substrates of emotion. However, searching for better filtering techniques in order to obtain a signal with better quality and with the maximum relevant information remains an important issue for researchers in this field. Signal processing is largely performed for ECG analysis and interpretation, but this process can be susceptible to error in the delineation phase. In addition, it can lead to the loss of important information that is usually considered as noise and, consequently, discarded from the analysis. The goal of this study was to evaluate if the ECG noise allows for the classification of emotions, while using its entropy as an input in a decision tree classifier. We collected the ECG signal from 25 healthy participants while they were presented with videos eliciting negative (fear and disgust) and neutral emotions. The results indicated that the neutral condition showed a perfect identification (100%), whereas the classification of negative emotions indicated good identification performances (60% of sensitivity and 80% of specificity). These results suggest that the entropy of noise contains relevant information that can be useful to improve the analysis of the physiological correlates of emotion.

Health information management : What business are we in?

Developments in information technology will drive the change in records management; however, it should be the health information managers who drive the information management change. The role of health information management will be challenged to use information technology to broker a range of requests for information from a variety of users, including he alth consumers. The purposes of this paper are to conceptualise the role of health information management in the context of a technologically driven and managed health care environment, and to demonstrat e how this framework has been used to review and develop the undergraduate program in health information management at the Queensland University of Technology.