Noninteractive fuzzy rule-based systems

In this paper, we have introduced a noninteractive model for fuzzy rule-based systems. A critical aspect of this noninteractive model is the introduction of a new set of rules with fewer parameters and without considering the interaction between the functionality of inputs. The new noninteractive model of the fuzzy rule-based system represents the output as a linear combination of the nonlinear function of individual inputs

A behavioral foundation for fuzzy measures

In Savage [41] a ‘behavioral foundation’ was given for subjective probabilities, to be used in the maximization of expected utility. This paper analogously gives a behavioral foundation for fuzzy measures, to be used in the maximization of ‘Choquet-expected utility’. This opens the way to empirical verification or falsification of fuzzy measures, and frees them of their ‘ad hoc’ character

Critical infrastructure systems of systems assessment methodology.

Assessing the risk of malevolent attacks against large-scale critical infrastructures requires modifications to existing methodologies that separately consider physical security and cyber security. This research has developed a risk assessment methodology that explicitly accounts for both physical and cyber security, while preserving the traditional security paradigm of detect, delay, and respond. This methodology also accounts for the condition that a facility may be able to recover from or mitigate the impact of a successful attack before serious consequences occur. The methodology uses evidence-based techniques (which are a generalization of probability theory) to evaluate the security posture of the cyber protection systems. Cyber threats are compared against cyber security posture using a category-based approach nested within a path-based analysis to determine the most vulnerable cyber attack path. The methodology summarizes the impact of a blended cyber/physical adversary attack in a conditional risk estimate where the consequence term is scaled by a ''willingness to pay'' avoidance approach

OptimShare: A Unified Framework for Privacy Preserving Data Sharing -- Towards the Practical Utility of Data with Privacy

Tabular data sharing serves as a common method for data exchange. However, sharing sensitive information without adequate privacy protection can compromise individual privacy. Thus, ensuring privacy-preserving data sharing is crucial. Differential privacy (DP) is regarded as the gold standard in data privacy. Despite this, current DP methods tend to generate privacy-preserving tabular datasets that often suffer from limited practical utility due to heavy perturbation and disregard for the tables' utility dynamics. Besides, there has not been much research on selective attribute release, particularly in the context of controlled partially perturbed data sharing. This has significant implications for scenarios such as cross-agency data sharing in real-world situations. We introduce OptimShare: a utility-focused, multi-criteria solution designed to perturb input datasets selectively optimized for specific real-world applications. OptimShare combines the principles of differential privacy, fuzzy logic, and probability theory to establish an integrated tool for privacy-preserving data sharing. Empirical assessments confirm that OptimShare successfully strikes a balance between better data utility and robust privacy, effectively serving various real-world problem scenarios

Unfolding-based Improvements on Fuzzy Logic Programs

AbstractUnfolding is a semantics-preserving program transformation technique that consists in the expansion of subexpressions of a program using their own definitions. In this paper we define two unfolding-based transformation rules that extend the classical definition of the unfolding rule (for pure logic programs) to a fuzzy logic setting. We use a fuzzy variant of Prolog where each program clause can be interpreted under a different (fuzzy) logic. We adapt the concept of a computation rule, a mapping that selects the subexpression of a goal involved in a computation step, and we prove the independence of the computation rule. We also define a basic transformation system and we demonstrate its strong correctness, that is, original and transformed programs compute the same fuzzy computed answers. Finally, we prove that our transformation rules always produce an improvement in the efficiency of the residual program, by reducing the length of successful Fuzzy SLD-derivations