Anonymization is a practical solution for preserving user's privacy in data publishing. Data
owners such as hospitals, banks, social network (SN) service providers, and insurance …

Recommender systems have become an integral part of online services due to their ability to
help users locate specific information in a sea of data. However, existing studies show that …

In recent years, along with the blooming of Machine Learning (ML)-based applications and
services, ensuring data privacy and security have become a critical obligation. ML-based …

Federated learning facilitates the collaborative training of models without the sharing of raw
data. However, recent attacks demonstrate that simply maintaining data locality during …

Differential privacy is a strong notion for privacy that can be used to prove formal
guarantees, in terms of a privacy budget, ε, about how much information is leaked by a …

Federated learning (FL) is an emerging paradigm that enables multiple organizations to
jointly train a model without revealing their private data to each other. This paper studies {\it …

The Internet of Things (IoT) is transforming major industries, including but not limited to
healthcare, agriculture, finance, energy, and transportation. IoT platforms are continually …

Machine learning models hosted in a cloud service are increasingly popular but risk privacy:
clients sending prediction requests to the service need to disclose potentially sensitive …

In the research of location privacy protection, the existing methods are mostly based on the
traditional anonymization, fuzzy and cryptography technology, and little success in the big …

Privacy-preserving data publishing is an important problem that has been the focus of
extensive study. The state-of-the-art solution for this problem is differential privacy, which …