Federated learning (FL) is a collaborative artificial intelligence (AI) approach that enables
distributed training of AI models without data sharing, thereby promoting privacy by design …

Differential privacy (DP) aims to confer data processing systems with inherent privacy
guarantees, offering strong protections for personal data. But DP's approach to privacy …

The use of synthetic data for training neural networks has recently received increased
attention, especially in the area of face recognition. This was mainly motivated by the …

Recommendation models are deployed in a variety of commercial applications in order to
provide personalized services for users. However, most of them rely on the users' original …

To ensure the privacy preservation and transparent use of regulated medical big data at
decentralized and distributed medical institutions, this paper proposes a blockchain-based …

The growing interest in data-driven medicine, in conjunction with the formation of initiatives
such as the European Health Data Space (EHDS) has demonstrated the need for …

Federated learning (FL) has emerged as a significant method for developing machine
learning models across multiple devices without centralized data collection. Candidemia, a …

Privacy-preserving record linkage (PPRL) aims to link records of the same real-world entity
from different databases without exposing any private information about the entity. Bloom …

The performance of machine learning algorithms can be considerably improved when
trained over larger datasets. In many domains, such as medicine and finance, larger …

Vehicle-based mobile crowdsensing has gained widespread attention due to its low cost
and efficient data collection mode. One common method to improve the accuracy of sensing …