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Is Homomorphic Encryption-Based Deep Learning Secure Enough?

Sensors (Basel, Switzerland)
As the amount of data collected and analyzed by machine learning technology increases, data that can identify individuals is also being collected in large quantities. In particular, as deep learning technology-which requires a large amount of analysi...

Federated personalized random forest for human activity recognition.

Mathematical biosciences and engineering : MBE
User data usually exists in the organization or own local equipment in the form of data island. It is difficult to collect these data to train better machine learning models because of the General Data Protection Regulation (GDPR) and other laws. The...

Genetic CFL: Hyperparameter Optimization in Clustered Federated Learning.

Computational intelligence and neuroscience
Federated learning (FL) is a distributed model for deep learning that integrates client-server architecture, edge computing, and real-time intelligence. FL has the capability of revolutionizing machine learning (ML) but lacks in the practicality of i...

Deep Learning Approaches for Continuous Authentication Based on Activity Patterns Using Mobile Sensing.

Sensors (Basel, Switzerland)
Smartphones as ubiquitous gadgets are rapidly becoming more intelligent and context-aware as sensing, networking, and processing capabilities advance. These devices provide users with a comprehensive platform to undertake activities such as socializi...

DeepFake electrocardiograms using generative adversarial networks are the beginning of the end for privacy issues in medicine.

Scientific reports
Recent global developments underscore the prominent role big data have in modern medical science. But privacy issues constitute a prevalent problem for collecting and sharing data between researchers. However, synthetic data generated to represent re...

Analysis of Application Examples of Differential Privacy in Deep Learning.

Computational intelligence and neuroscience
Artificial Intelligence has been widely applied today, and the subsequent privacy leakage problems have also been paid attention to. Attacks such as model inference attacks on deep neural networks can easily extract user information from neural netwo...

Enabling Security Services in Socially Assistive Robot Scenarios for Healthcare Applications.

Sensors (Basel, Switzerland)
Today's IoT deployments are highly complex, heterogeneous and constantly changing. This poses severe security challenges such as limited end-to-end security support, lack of cross-platform cross-vertical security interoperability as well as the lack ...

An Accurate Deep Learning Model for Clinical Entity Recognition From Clinical Notes.

IEEE journal of biomedical and health informatics
The growing use of electronic health records in the medical domain results in generating a large amount of medical data that is stored in the form of clinical notes. These clinical notes are enriched with clinical entities like disease, treatment, te...

Data Sharing of Imaging in an Evolving Health Care World: Report of the ACR Data Sharing Workgroup, Part 1: Data Ethics of Privacy, Consent, and Anonymization.

Journal of the American College of Radiology : JACR
Radiology is at the forefront of the artificial intelligence transformation of health care across multiple areas, from patient selection to study acquisition to image interpretation. Needing large data sets to develop and train these algorithms, deve...

Privacy and artificial intelligence: challenges for protecting health information in a new era.

BMC medical ethics
BACKGROUND: Advances in healthcare artificial intelligence (AI) are occurring rapidly and there is a growing discussion about managing its development. Many AI technologies end up owned and controlled by private entities. The nature of the implementa...