99 citations · 187 across the 6 of their papers we have counts for
6 papers
A Comprehensive Survey on Local Differential Privacy Toward Data Statistics and Analysis
Teng Wang, Xuefeng Zhang, Jingyu Feng +1
Collecting and analyzing massive data generated from smart devices have become increasingly pervasive in crowdsensing, which are the building blocks for data-driven decision-making…
Local Differential Privacy based Federated Learning for Internet of Things
Yang Zhao, Jun Zhao, Mengmeng Yang +5
Internet of Vehicles (IoV) is a promising branch of the Internet of Things. IoV simulates a large variety of crowdsourcing applications such as Waze, Uber, and Amazon Mechanical Tu…
Reviewing and Improving the Gaussian Mechanism for Differential Privacy
Jun Zhao, Teng Wang, Tao Bai +7
Differential privacy provides a rigorous framework to quantify data privacy, and has received considerable interest recently. A randomized mechanism satisfying -differentia…
Conditional Analysis for Key-Value Data with Local Differential Privacy
Lin Sun, Jun Zhao, Xiaojun Ye +3
Local differential privacy (LDP) has been deemed as the de facto measure for privacy-preserving distributed data collection and analysis. Recently, researchers have extended LDP to…
Locally Differentially Private Data Collection and Analysis
Teng Wang, Jun Zhao, Xinyu Yang +1
Local differential privacy (LDP) can provide each user with strong privacy guarantees under untrusted data curators while ensuring accurate statistics derived from privatized data.…
Privacy-preserving Crowd-guided AI Decision-making in Ethical Dilemmas
Teng Wang, Jun Zhao, Han Yu +4
With the rapid development of artificial intelligence (AI), ethical issues surrounding AI have attracted increasing attention. In particular, autonomous vehicles may face moral dil…