With the rapid development of low-cost consumer electronics and pervasive adoption of next
generation wireless communication technologies, a tremendous amount of data has been …

With the advent of the era of big data, privacy issues have been becoming a hot topic in
public. Local differential privacy (LDP) is a state‐of‐the‐art privacy preservation technique …

The advancement of large language models (LLMs) has significantly enhanced the ability to
effectively tackle various downstream NLP tasks and unify these tasks into generative …

Accurately learning from user data while providing quantifiable privacy guarantees provides
an opportunity to build better ML models while maintaining user trust. This paper presents a …

Differentially private (DP) learning, notably DP stochastic gradient descent (DP-SGD), has
limited applicability in fine-tuning gigantic pre-trained language models (LMs) for natural …

Vertical Federated Learning (FL) is a new paradigm that enables users with non-
overlapping attributes of the same data samples to jointly train a model without directly …

Local differential privacy (LDP) is a strong notion of privacy that often leads to a significant
drop in utility. The original definition of LDP assumes that all the elements in the data …

Pre-trained language models (PLMs) have demonstrated significant proficiency in solving a
wide range of general natural language processing (NLP) tasks. Researchers have …

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 …

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 …