activity
20162023
most citedLoPub: High-Dimensional Crowdsourced Data Publication with Local Differential Privacy

294 citations · 535 across the 22 of their papers we have counts for

collaborators
Showing 2020Show all

6 papers · 1 filter

cs.GT2020

Defending against Contagious Attacks on a Network with Resource Reallocation

Rufan Bai, Haoxing Lin, Xinyu Yang +3

In classic network security games, the defender distributes defending resources to the nodes of the network, and the attacker attacks a node, with the objective to maximize the dam…

cs.LG2020★ 6 cited

Latent Dirichlet Allocation Model Training with Differential Privacy

Fangyuan Zhao, Xuebin Ren, Shusen Yang +3

Latent Dirichlet Allocation (LDA) is a popular topic modeling technique for hidden semantic discovery of text data and serves as a fundamental tool for text analysis in various app…

cs.CR2020★ 99 cited

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…

cs.DC2020★ 10 cited

DPCrowd: Privacy-preserving and Communication-efficient Decentralized Statistical Estimation for Real-time Crowd-sourced Data

Xuebin Ren, Chia-Mu Yu, Wei Yu +3

In Internet of Things (IoT) driven smart-world systems, real-time crowd-sourced databases from multiple distributed servers can be aggregated to extract dynamic statistics from a l…

cs.IR2020★ 59 cited

Preserving Dynamic Attention for Long-Term Spatial-Temporal Prediction

Haoxing Lin, Rufan Bai, Weijia Jia +2

Effective long-term predictions have been increasingly demanded in urban-wise data mining systems. Many practical applications, such as accident prevention and resource pre-allocat…

cs.DC2020

OL4EL: Online Learning for Edge-cloud Collaborative Learning on Heterogeneous Edges with Resource Constraints

Qing Han, Shusen Yang, Xuebin Ren +3

Distributed machine learning (ML) at network edge is a promising paradigm that can preserve both network bandwidth and privacy of data providers. However, heterogeneous and limited…