Differentially Private Federated Learning (DP-FL) has garnered attention...
Federated Learning (FL) allows multiple participating clients to train
m...
Mobility data captures the locations of moving objects such as humans,
a...
Federated Learning, as a popular paradigm for collaborative training, is...
Adversarial examples are crafted by adding indistinguishable perturbatio...
Deep learning models trained on large-scale data have achieved encouragi...
Graph Neural Networks (GNNs) have achieved great success in mining
graph...
Location-based services (LBS) have been significantly developed and wide...
Automatic Speech Recognition models require large amount of speech data ...
Tensor factorization has received increasing interest due to its intrins...
Mining user-generated content–e.g., for the early detection of outbreaks...
Federated Learning (FL) allows multiple participating clients to train
m...
Tensor factorization has been proved as an efficient unsupervised learni...
Representation learning on static graph-structured data has shown a
sign...
Adversarial data examples have drawn significant attention from the mach...
Federated learning enables multiple clients, such as mobile phones and
o...
Poisoning attacks are a category of adversarial machine learning threats...
Federated learning is a prominent framework that enables clients (e.g.,
...
Federated learning has emerged as an important paradigm for training mac...
Understanding product attributes plays an important role in improving on...
We present an algorithm based on multi-layer transformers for identifyin...
Samples with ground truth labels may not always be available in numerous...
Due to the over-parameterization nature, neural networks are a powerful ...
The increasing complexity of algorithms for analyzing medical data, incl...
Federated Learning is a promising machine learning paradigm when multipl...
Mining massive spatio-temporal data can help a variety of real-world
app...
Location privacy has been extensively studied in the literature. However...
In this demonstration, we present a privacy-preserving epidemic surveill...
The inductive bias of a neural network is largely determined by the
arch...
Federated learning (FL) is a machine learning setting where many clients...
Data collection under local differential privacy (LDP) has been mostly
s...
Local Differential Privacy (LDP) provides provable privacy protection fo...
Tensor factorization has been demonstrated as an efficient approach for
...
Location privacy-preserving mechanisms (LPPMs) have been extensively stu...
Background: Genomic data have been collected by different institutions a...
k nearest neighbor (kNN) queries and skyline queries are important
opera...
Recent work on minimum hyperspherical energy (MHE) has demonstrated its
...
Visual information plays a critical role in human decision-making proces...
Skyline, aiming at finding a Pareto optimal subset of points in a
multi-...
Skyline queries are important in many application domains. In this paper...
Location privacy-preserving mechanisms (LPPMs) have been extensively stu...
Recently, product images have gained increasing attention in clothing
re...
Outsourcing data and computation to cloud server provides a cost-effecti...
Differential Privacy (DP) has received increasing attention as a rigorou...
In this paper, we propose a novel Eclipse query which is more practical ...