Existing person re-identification benchmarks and methods mainly focus on matching
cropped pedestrian images between queries and candidates. However, it is different from …

Fast-growing smart city applications, such as smart delivery, smart community, and smart
health, are generating big data that are widely distributed on the internet. IoT (Internet of …

The extensive collection and processing of personal information in big data analytics has
given rise to serious privacy concerns, related to wide scale electronic surveillance, profiling …

In local differential privacy (LDP), each user perturbs her data locally before sending the
noisy data to a data collector. The latter then analyzes the data to obtain useful statistics …

Differential privacy is an essential and prevalent privacy model that has been widely
explored in recent decades. This survey provides a comprehensive and structured overview …

We propose a general approach for differentially private synthetic data generation, that
consists of three steps:(1) select a collection of low-dimensional marginals,(2) measure …

\epsilon-differential privacy is the state-of-the-art model for releasing sensitive information
while protecting privacy. Numerous methods have been proposed to enforce epsilon …

A request from a client is received to generate a differentially private random forest classifier
trained using a set of restricted data. The differentially private random forest classifier is …

Large Language Models (LLMs) have become integral to numerous domains, significantly
advancing applications in data management, mining, and analysis. Their profound …

Camouflaging user's actual location with fakes is a prevalent obfuscation technique for
protecting location privacy. We show that the protection mechanisms based on the existing …