Local differential privacy (LDP), which perturbs each user's data locally and only sends the
noisy version of her information to the aggregator, is a popular privacy-preserving data …

In this paper, we tackle the problem of answering multi-dimensional range queries under
local differential privacy. There are three key technical challenges: capturing the correlations …

Truth discovery is an effective way to eliminate data inconsistency by integrating different
worker-provided values. Although directly conducting non-private truth discovery …

As in traditional machine learning models, models trained with federated learning may
exhibit disparate performance across demographic groups. Model holders must identify …

The objective of differential privacy (DP) is to protect privacy by producing an output
distribution that is indistinguishable between any two neighboring databases. However …

Large Language Models (LLMs) are gaining increasing attention due to their exceptional
performance across numerous tasks. As a result, the general public utilize them as an …

Many individuals and companies choose the public cloud as their data and IT infrastructure
platform. But remote accesses over the data inevitably bring the issue of trust. Despite strong …

Although time-series data collected from users can be utilized to provide services for various
applications, they could reveal sensitive information about users. Recently, local differential …

Local Differential Privacy (LDP) is a popular standard for privacy-preserving data collection.
Numerous LDP protocols have been proposed in the literature which differ in how they …

Trajectory data collection is a common task with many applications in our daily lives.
Analyzing trajectory data enables service providers to enhance their services, which …