102 citations · 218 across the 11 of their papers we have counts for
5 papers · 1 filter
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…
Collecting and Analyzing Multidimensional Data with Local Differential Privacy
Ning Wang, Xiaokui Xiao, Yin Yang +5
Local differential privacy (LDP) is a recently proposed privacy standard for collecting and analyzing data, which has been used, e.g., in the Chrome browser, iOS and macOS. In LDP,…
Distributed Clustering in the Anonymized Space with Local Differential Privacy
Lin Sun, Jun Zhao, Xiaojun Ye
Clustering and analyzing on collected data can improve user experiences and quality of services in big data, IoT applications. However, directly releasing original data brings pote…
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…