AIMC Topic: Privacy
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Rebirth of Distributed AI-A Review of eHealth Research.
Sensors (Basel, Switzerland)
Jul 23, 2021
The envisioned smart city domains are expected to rely heavily on artificial intelligence and machine learning (ML) approaches for their operations, where the basic ingredient is data. Privacy of the data and training time have been major roadblocks ...
F-Classify: Fuzzy Rule Based Classification Method for Privacy Preservation of Multiple Sensitive Attributes.
Sensors (Basel, Switzerland)
Jul 20, 2021
With the advent of smart health, smart cities, and smart grids, the amount of data has grown swiftly. When the collected data is published for valuable information mining, privacy turns out to be a key matter due to the presence of sensitive informat...
Gait-Based Implicit Authentication Using Edge Computing and Deep Learning for Mobile Devices.
Sensors (Basel, Switzerland)
Jul 5, 2021
Implicit authentication mechanisms are expected to prevent security and privacy threats for mobile devices using behavior modeling. However, recently, researchers have demonstrated that the performance of behavioral biometrics is insufficiently accur...
Leveraging data and AI to deliver on the promise of digital health.
International journal of medical informatics
Apr 10, 2021
Rising rates of NCDs threaten fragile healthcare systems in low- and middle-income countries. Fortunately, new digital technology provides tools to more effectively address the growing dual burden of disease. Two-thirds of the world's population subs...
Voice-Controlled Intelligent Personal Assistants in Health Care: International Delphi Study.
Journal of medical Internet research
Apr 9, 2021
BACKGROUND: Voice-controlled intelligent personal assistants (VIPAs), such as Amazon Echo and Google Home, involve artificial intelligence-powered algorithms designed to simulate humans. Their hands-free interface and growing capabilities have a wide...
FeARH: Federated machine learning with anonymous random hybridization on electronic medical records.
Journal of biomedical informatics
Mar 9, 2021
Electrical medical records are restricted and difficult to centralize for machine learning model training due to privacy and regulatory issues. One solution is to train models in a distributed manner that involves many parties in the process. However...
VANTAGE6: an open source priVAcy preserviNg federaTed leArninG infrastructurE for Secure Insight eXchange.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Jan 25, 2021
Answering many of the research questions in the field of cancer informatics requires incorporating and centralizing data that are hosted by different parties. Federated Learning (FL) has emerged as a new approach in which a global model can be genera...
Insights into mobile health application market via a content analysis of marketplace data with machine learning.
PloS one
Jan 6, 2021
BACKGROUND: Despite the benefits offered by an abundance of health applications promoted on app marketplaces (e.g., Google Play Store), the wide adoption of mobile health and e-health apps is yet to come.
Adoption of Digital Technologies in Health Care During the COVID-19 Pandemic: Systematic Review of Early Scientific Literature.
Journal of medical Internet research
Nov 6, 2020
BACKGROUND: The COVID-19 pandemic is favoring digital transitions in many industries and in society as a whole. Health care organizations have responded to the first phase of the pandemic by rapidly adopting digital solutions and advanced technology ...
How Might Artificial Intelligence Applications Impact Risk Management?
AMA journal of ethics
Nov 1, 2020
Artificial intelligence (AI) applications have attracted considerable ethical attention for good reasons. Although AI models might advance human welfare in unprecedented ways, progress will not occur without substantial risks. This article considers ...
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