Privacy Preserving Risk Mitigation Approach for Healthcare Domain

In the healthcare domain, protecting the electronic health record (EHR) is crucial for preserving the privacy of the patient. To help protect the sensitive data, access control mechanisms can be utilized to restrict access to only legitimate users. However, an issue arises when the authorized users abuse their access privileges and violate privacy preferences of the patients. While traditional access control schemes fall short of defending against the misbehavior of authorized users, risk-aware access control models can provide adaptable access to the system resources based on assessing the risk of an access request. When an access request is deemed risky, but within acceptable thresholds, risk mitigation strategies can be exploited to minimize the risk calculated. This paper proposes a risk-aware, privacy-preserving risk mitigation approach that can be utilized in the healthcare domain. The risk mitigation approach controls the patient’s medical data that can be exposed to healthcare professionals, according to their trust level as well as the risk incurred of such data exposure, by developing a novel Risk Measure formula. The developed Risk Measure is proven to manage the risk effectively. Furthermore, Risk Mitigation Data Disclosure algorithms, RIMIDI0 and RIMIDI1, which utilize the developed risk measures, are proposed. Experimental results show the feasibility and effectiveness of the proposed method in preserving the privacy preferences of the patient. Since the proposed approach exposes the patient’s data that are relevant to the undergoing medical procedure while preserving the privacy preferences, positive outcomes can be realized, which will ultimately bring forth quality healthcare services.

Toward Safe and Usable Technologies for Autistic: Risks Identification and Mitigation Guidelines

Recent advances in technology provide countless innovative solutions and applications for supporting children with autism in educational learning and personal development. This result is an increasingly recognized need to deal with new and unexpected risks and issues such as social exclusion. Diverse advanced technologies were aimed to support learning activity from different perspectives with multiple strategies. However, despite the significant amount of work and explored technology, several common risks pop up due to user’s vulnerability to diverse risks such as negative screen effects. Accordingly, there is still plenty of room for improvement in this regard. To address these vulnerabilities and gaps, this paper aims at identifying issues and challenges improving the technology applied for Autism Spectrum Disorders and autistics dedicated applications. It put forward requirements and design decisions supporting safe autistic dedicated interaction with regards to ISO31000 risk management process.

Deep reinforcement learning with planning guardrails for building energydemand response

Building energy demand response is projected to be important in decarbonizing energy use. A demand responseprogram that communicates ‘‘artificial’’ hourly price signals to workers as part of a social game has the potentialto elicit energy consumption changes that simultaneously reduce energy costs and emissions. The efficacy ofsuch a program depends on the pricing agent’s ability to learn how workers respond to prices and mitigatethe risk of high energy costs during this learning process. We assess the value of deep reinforcement learning(RL) for mitigating this risk. Specifically, we explore the value of combining: (i) a model-free RL method thatcan learn by posting price signals to workers, (ii) a supervisory ‘‘planning model’’ that provides a syntheticlearning environment, and (iii) a guardrail method that determines whether a price should be posted to realworkers or the planning environment for feedback. In a simulated medium-sized office building, we compareour pricing agent against existing model-free and model-based deep RL agents, and the simpler strategy ofpassing on the time-of-use price signal to workers. We find that our controller eliminates 175,000 US Dollarsin initial investment, decreases by 30% the energy cost, and curbs emissions by 32% compared to energyconsumption under the time-of-use rate. In contrast, the model-free and model-based deep RL benchmarksare unable to overcome initial learning costs. Our results bode well for risk-aware deep RL facilitating thedeployment of building demand response.