One of the biggest concerns of big data is privacy. However, the study on big data privacy is
still at a very early stage. We believe the forthcoming solutions and theories of big data …

AI systems are becoming increasingly pervasive within children's devices, apps, and
services. However, it is not yet well-understood how risks and ethical considerations of AI …

Location information of workers are usually required for optimal task allocation in mobile
crowdsensing, which however raises severe concerns of location privacy leakage. Although …

While the application of differential privacy (DP) has been well-studied in cross-device
federated learning (FL), there is a lack of work considering DP and its implications for cross …

Differential privacy is a precise mathematical constraint meant to ensure privacy of individual
pieces of information in a database even while queries are being answered about the …

Shortly after it was first introduced in 2006, differential privacy became the flagship data
privacy definition. Since then, numerous variants and extensions were proposed to adapt it …

With the deep penetration of the Internet and mobile devices, privacy preservation in the
local setting has become increasingly relevant. The local setting refers to the scenario where …

Federated Learning (FL) is a promising framework for multiple clients to learn a joint model
without directly sharing the data. In addition to high utility of the joint model, rigorous privacy …

Ubiquitous deployment of low-cost smart devices and widespread use of high-speed
wireless networks have led to the rapid development of the Internet of Things (IoT). IoT …

With the increasing applications of language models, it has become crucial to protect these
models from leaking private information. Previous work has attempted to tackle this …