402 citations · 843 across the 16 of their papers we have counts for
7 papers · 1 filter
Practical Data Poisoning Attack against Next-Item Recommendation
Hengtong Zhang, Yaliang Li, Bolin Ding +1
Online recommendation systems make use of a variety of information sources to provide users the items that users are potentially interested in. However, due to the openness of the…
Linear and Range Counting under Metric-based Local Differential Privacy
Zhuolun Xiang, Bolin Ding, Xi He +1
Local differential privacy (LDP) enables private data sharing and analytics without the need for a trusted data collector. Error-optimal primitives (for, e.g., estimating means and…
Improving Utility and Security of the Shuffler-based Differential Privacy
Tianhao Wang, Bolin Ding, Min Xu +5
When collecting information, local differential privacy (LDP) alleviates privacy concerns of users because their private information is randomized before being sent it to the centr…
Towards Differentially Private Truth Discovery for Crowd Sensing Systems
Yaliang Li, Houping Xiao, Zhan Qin +5
Nowadays, crowd sensing becomes increasingly more popular due to the ubiquitous usage of mobile devices. However, the quality of such human-generated sensory data varies significan…
An Algorithmic Framework For Differentially Private Data Analysis on Trusted Processors
Joshua Allen, Bolin Ding, Janardhan Kulkarni +3
Differential privacy has emerged as the main definition for private data analysis and machine learning. The {\em global} model of differential privacy, which assumes that users tru…
Comparing Population Means under Local Differential Privacy: with Significance and Power
Bolin Ding, Harsha Nori, Paul Li +1
A statistical hypothesis test determines whether a hypothesis should be rejected based on samples from populations. In particular, randomized controlled experiments (or A/B testing…