Contributions to Context-Aware Smart Healthcare: A Security and Privacy Perspective

• URL: http://arxiv.org/abs/2206.14567v1
• Date: Tue, 28 Jun 2022 16:54:16 GMT
• Title: Contributions to Context-Aware Smart Healthcare: A Security and Privacy Perspective
• Authors: Edgar Batista
• Abstract summary: dissertation contributes to several security and privacy challenges within the smart health paradigm. We present an extensive analysis on the security aspects of the underlying sensors and networks deployed in context-aware environments. We contribute to process mining, a popular analytical field that helps analyse business processes within organisations.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The management of health data, from their gathering to their analysis, arises a number of challenging issues due to their highly confidential nature. In particular, this dissertation contributes to several security and privacy challenges within the smart health paradigm. More concretely, we firstly develop some contributions to context-aware environments enabling smart health scenarios. We present an extensive analysis on the security aspects of the underlying sensors and networks deployed in such environments, a novel user-centred privacy framework for analysing ubiquitous computing systems, and a complete analysis on the security and privacy challenges that need to be faced to implement cognitive cities properly. Second, we contribute to process mining, a popular analytical field that helps analyse business processes within organisations. Despite its popularity within the healthcare industry, we address two major issues: the high complexity of healthcare processes and the scarce research on privacy aspects. Regarding the first issue, we present a novel process discovery algorithm with a built-in heuristic that simplifies complex processes and, regarding the second, we propose two novel privacy-preserving process mining methods, which achieve a remarkable trade-off between accuracy and privacy. Last but not least, we present some smart health applications, namely a context-aware recommender system for routes, a platform supporting early mobilization programmes in hospital settings, and a health-oriented geographic information system. The results of this dissertation are intended to help the research community to enhance the security of the intelligent environments of the future as well as the privacy of the citizens regarding their personal and health data.

Related papers

• The Gradient of Health Data Privacy [15.417809900388262]
  This paper introduces a novel "privacy gradient" approach to health data governance. Our multidimensional concept considers factors such as data sensitivity, stakeholder relationships, purpose of use, and temporal aspects. We demonstrate how this approach can address critical privacy challenges in diverse healthcare settings worldwide.
  arXiv  Detail & Related papers  (2024-10-01T17:35:18Z)
• Privacy-Preserving Collaborative Genomic Research: A Real-Life Deployment and Vision [2.7968600664591983]
  This paper presents a privacy-preserving framework for genomic research, developed in collaboration with Lynx.MD. The framework addresses critical cybersecurity and privacy challenges, enabling the privacy-preserving sharing and analysis of genomic data. Implementing the framework within Lynx.MD involves encoding genomic data into binary formats and applying noise through controlled perturbation techniques.
  arXiv  Detail & Related papers  (2024-07-12T05:43:13Z)
• Privacy Risks of General-Purpose AI Systems: A Foundation for Investigating Practitioner Perspectives [47.17703009473386]
  Powerful AI models have led to impressive leaps in performance across a wide range of tasks. Privacy concerns have led to a wealth of literature covering various privacy risks and vulnerabilities of AI models. We conduct a systematic review of these survey papers to provide a concise and usable overview of privacy risks in GPAIS.
  arXiv  Detail & Related papers  (2024-07-02T07:49:48Z)
• A Qualitative Analysis Framework for mHealth Privacy Practices [0.0]
  This paper introduces a novel framework for the qualitative evaluation of privacy practices in mHealth apps. Our investigation encompasses an analysis of 152 leading mHealth apps on the Android platform. Our findings indicate persistent issues with negligence and misuse of sensitive user information.
  arXiv  Detail & Related papers  (2024-05-28T08:57:52Z)
• A Survey of Privacy-Preserving Model Explanations: Privacy Risks, Attacks, and Countermeasures [50.987594546912725]
  Despite a growing corpus of research in AI privacy and explainability, there is little attention on privacy-preserving model explanations. This article presents the first thorough survey about privacy attacks on model explanations and their countermeasures.
  arXiv  Detail & Related papers  (2024-03-31T12:44:48Z)
• A Survey on Privacy of Health Data Lifecycle: A Taxonomy, Review, and Future Directions [1.3927943269211591]
  We review existing work and distill 10 distinct privacy concerns occurring in a health data lifecycle. We propose a taxonomy of techniques used for privacy preservation in healthcare. We identify several future research directions to mitigate the security challenges for privacy preservation in health data management.
  arXiv  Detail & Related papers  (2023-11-09T14:37:41Z)
• A Unified View of Differentially Private Deep Generative Modeling [60.72161965018005]
  Data with privacy concerns comes with stringent regulations that frequently prohibited data access and data sharing. Overcoming these obstacles is key for technological progress in many real-world application scenarios that involve privacy sensitive data. Differentially private (DP) data publishing provides a compelling solution, where only a sanitized form of the data is publicly released.
  arXiv  Detail & Related papers  (2023-09-27T14:38:16Z)
• Advancing Differential Privacy: Where We Are Now and Future Directions for Real-World Deployment [100.1798289103163]
  We present a detailed review of current practices and state-of-the-art methodologies in the field of differential privacy (DP) Key points and high-level contents of the article were originated from the discussions from "Differential Privacy (DP): Challenges Towards the Next Frontier" This article aims to provide a reference point for the algorithmic and design decisions within the realm of privacy, highlighting important challenges and potential research directions.
  arXiv  Detail & Related papers  (2023-04-14T05:29:18Z)
• The Medkit-Learn(ing) Environment: Medical Decision Modelling through Simulation [81.72197368690031]
  We present a new benchmarking suite designed specifically for medical sequential decision making. The Medkit-Learn(ing) Environment is a publicly available Python package providing simple and easy access to high-fidelity synthetic medical data.
  arXiv  Detail & Related papers  (2021-06-08T10:38:09Z)
• Differentially Private Multi-Agent Planning for Logistic-like Problems [70.3758644421664]
  This paper proposes a novel strong privacy-preserving planning approach for logistic-like problems. Two challenges are addressed: 1) simultaneously achieving strong privacy, completeness and efficiency, and 2) addressing communication constraints. To the best of our knowledge, this paper is the first to apply differential privacy to the field of multi-agent planning.
  arXiv  Detail & Related papers  (2020-08-16T03:43:09Z)
• Security and Privacy for mHealth and uHealth Systems: a Systematic Mapping Study [0.0]
  This study aims to identify, classify, compare, and evaluate state-of-the-art on security and privacy of m/uHealth systems.
  arXiv  Detail & Related papers  (2020-06-22T08:44:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.