TRAAC: Trust and Risk Aware Access Control

Postprin

Efficient and Risk-Aware Control of Electricity Distribution Grids

This article presents an economic model predictive control (EMPC) algorithm for reducing losses and increasing the resilience of medium-voltage electricity distribution grids characterized by high penetration of renewable energy sources and possibly subject to natural or malicious adverse events. The proposed control system optimizes grid operations through network reconfiguration, control of distributed energy storage systems (ESSs), and on-load tap changers. The core of the EMPC algorithm is a nonconvex optimization problem integrating the ESSs dynamics, the topological and power technical constraints of the grid, and the modeling of the cascading effects of potential adverse events. An equivalent (i.e., having the same optimal solution) proxy of the nonconvex problem is proposed to make the solution more tractable. Simulations performed on a 16-bus test distribution network validate the proposed control strategy

Trust-based model for privacy control in context aware systems

In context-aware systems, there is a high demand on providing privacy solutions to users when they are interacting and exchanging personal information. Privacy in this context encompasses reasoning about trust and risk involved in interactions between users. Trust, therefore, controls the amount of information that can be revealed, and risk analysis allows us to evaluate the expected benefit that would motivate users to participate in these interactions. In this paper, we propose a trust-based model for privacy control in context-aware systems based on incorporating trust and risk. Through this approach, it is clear how to reason about trust and risk in designing and implementing context-aware systems that provide mechanisms to protect users' privacy. Our approach also includes experiential learning mechanisms from past observations in reaching better decisions in future interactions. The outlined model in this paper serves as an attempt to solve the concerns of privacy control in context-aware systems. To validate this model, we are currently applying it on a context-aware system that tracks users' location. We hope to report on the performance evaluation and the experience of implementation in the near future

Belief Control Barrier Functions for Risk-aware Control

Ensuring safety in real-world robotic systems is often challenging due to unmodeled disturbances and noisy sensor measurements. To account for such stochastic uncertainties, many robotic systems leverage probabilistic state estimators such as Kalman filters to obtain a robot's belief, i.e. a probability distribution over possible states. We propose belief control barrier functions (BCBFs) to enable risk-aware control synthesis, leveraging all information provided by state estimators. This allows robots to stay in predefined safety regions with desired confidence under these stochastic uncertainties. BCBFs are general and can be applied to a variety of robotic systems that use extended Kalman filters as state estimator. We demonstrate BCBFs on a quadrotor that is exposed to external disturbances and varying sensing conditions. Our results show improved safety compared to traditional state-based approaches while allowing control frequencies of up to 1kHz

Risk-Aware Access Control And XACML

In this thesis, we propose an extension of an existing RAAC abstract model that supports risk assessment, risk-aware authorisation decision making and the use of system and user obligations as risk mitigation methods. We also propose an implementation of the extended abstract model based on XACML, a standard that defines an XML-based language for the specification of access control policies, requests and responses. We develop a novel Risk-Aware Group Based Access Control (RA-GBAC

Risk-aware stochastic control of a sailboat

Sailboat path-planning is a natural hybrid control problem (due to continuous steering and occasional "tack-switching" maneuvers), with the actual path-to-target greatly affected by stochastically evolving wind conditions. Previous studies have focused on finding risk-neutral policies that minimize the expected time of arrival. In contrast, we present a robust control approach, which maximizes the probability of arriving before a specified deadline/threshold. Our numerical method recovers the optimal risk-aware (and threshold-specific) policies for all initial sailboat positions and a broad range of thresholds simultaneously. This is accomplished by solving two quasi-variational inequalities based on second-order Hamilton-Jacobi-Bellman (HJB) PDEs with degenerate parabolicity. Monte-Carlo simulations show that risk-awareness in sailing is particularly useful when a carefully calculated bet on the evolving wind direction might yield a reduction in the number of tack-switches.Comment: 6 pages; 4 figure

Tone from the Top in Risk Management: A Complementarity Perspective on How Control Systems Influence Risk Awareness

Prompted by the weaknesses of standardized risk management approaches in the aftermath of the 2008 financial crisis, scholars, regulators, and practitioners alike emphasize the importance of creating a risk-aware culture in organizations. Recent insights highlight the special role of tone from the top as crucial driver of risk awareness. In this study, we take a systems-perspective on control system design to investigate the role of tone from the top in creating risk awareness. In particular, we argue that both interactive and diagnostic use of budgets and performance measures interact with tone from the top in managing risk awareness. Our results show that interactive control strengthens the effect of tone from the top on risk awareness, while tone from the top and diagnostic control are, on average, not interrelated with regard to creating risk awareness. To shed light on the boundary conditions of the proposed interdependencies, we further investigate whether the predicted interdependencies are sensitive to the level of perceived environmental uncertainty. We find that the effect of tone from the top and interactive control becomes significantly stronger in a situation of high perceived environmental uncertainty. Most interestingly, tone from the top and diagnostic control are complements with regard to risk awareness in settings of low perceived environmental uncertainty and substitutes at high levels of perceived environmental uncertainty.Series: Department of Strategy and Innovation Working Paper Serie

Learning Disturbances Online for Risk-Aware Control: Risk-Aware Flight with Less Than One Minute of Data

Recent advances in safety-critical risk-aware control are predicated on apriori knowledge of the disturbances a system might face. This paper proposes a method to efficiently learn these disturbances online, in a risk-aware context. First, we introduce the concept of a Surface-at-Risk, a risk measure for stochastic processes that extends Value-at-Risk -- a commonly utilized risk measure in the risk-aware controls community. Second, we model the norm of the state discrepancy between the model and the true system evolution as a scalar-valued stochastic process and determine an upper bound to its Surface-at-Risk via Gaussian Process Regression. Third, we provide theoretical results on the accuracy of our fitted surface subject to mild assumptions that are verifiable with respect to the data sets collected during system operation. Finally, we experimentally verify our procedure by augmenting a drone's controller and highlight performance increases achieved via our risk-aware approach after collecting less than a minute of operating data