Heather Molyneaux | Searcharxiv

arXiv2025

Exploring User Perspectives on Data Collection, Data Sharing Preferences, and Privacy Concerns with Remote Healthcare Technology

Remote healthcare technology can help tackle societal issues by improving access to quality healthcare services and enhancing diagnoses through in-place monitoring. These services can be implemented through a combination of mobile devices, applications, wearable sensors, and other smart technology. It is paramount to handle sensitive data that is collected in ways that meet users' privacy expectations. We surveyed 384 people in Canada aged 20 to 93 years old to explore participants' comfort with data collection, sharing preferences, and potential privacy concerns related to remote healthcare technology. We explore these topics within the context of various healthcare scenarios including health emergencies and managing chronic health conditions.

cs.CY↗

arXiv2022

Towards a Robust and Trustworthy Machine Learning System Development: An Engineering Perspective

While Machine Learning (ML) technologies are widely adopted in many mission critical fields to support intelligent decision-making, concerns remain about system resilience against ML-specific security attacks and privacy breaches as well as the trust that users have in these systems. In this article, we present our recent systematic and comprehensive survey on the state-of-the-art ML robustness and trustworthiness from a security engineering perspective, focusing on the problems in system threat analysis, design and evaluation faced in developing practical machine learning applications, in terms of robustness and user trust. Accordingly, we organize the presentation of this survey intended to facilitate the convey of the body of knowledge from this angle. We then describe a metamodel we created that represents the body of knowledge in a standard and visualized way. We further illustrate how to leverage the metamodel to guide a systematic threat analysis and security design process which extends and scales up the classic process. Finally, we propose the future research directions motivated by our findings. Our work differs itself from the existing surveys by (i) exploring the fundamental principles and best practices to support robust and trustworthy ML system development, and (ii) studying the interplay of robustness and user trust in the context of ML systems. We expect this survey provides a big picture for machine learning security practitioners.

cs.LG↗