386 citations · 722 across the 12 of their papers we have counts for
12 papers
Federated Pruning: Improving Neural Network Efficiency with Federated Learning
Rongmei Lin, Yonghui Xiao, Tien-Ju Yang +4
Automatic Speech Recognition models require large amount of speech data for training, and the collection of such data often leads to privacy concerns. Federated learning has been w…
Online Model Compression for Federated Learning with Large Models
Tien-Ju Yang, Yonghui Xiao, Giovanni Motta +3
This paper addresses the challenges of training large neural network models under federated learning settings: high on-device memory usage and communication cost. The proposed Onli…
PANDA: Policy-aware Location Privacy for Epidemic Surveillance
Yang Cao, Shun Takagi, Yonghui Xiao +2
In this demonstration, we present a privacy-preserving epidemic surveillance system. Recently, many countries that suffer from coronavirus crises attempt to access citizen's locati…
PGLP: Customizable and Rigorous Location Privacy through Policy Graph
Yang Cao, Yonghui Xiao, Shun Takagi +6
Location privacy has been extensively studied in the literature. However, existing location privacy models are either not rigorous or not customizable, which limits the trade-off b…
Protecting Spatiotemporal Event Privacy in Continuous Location-Based Services
Yang Cao, Yonghui Xiao, Li Xiong +2
Location privacy-preserving mechanisms (LPPMs) have been extensively studied for protecting users' location privacy by releasing a perturbed location to third parties such as locat…
PriSTE: From Location Privacy to Spatiotemporal Event Privacy
Yang Cao, Yonghui Xiao, Li Xiong +1
Location privacy-preserving mechanisms (LPPMs) have been extensively studied for protecting a user's location at each time point or a sequence of locations with different timestamp…
Quantifying Differential Privacy in Continuous Data Release under Temporal Correlations
Yang Cao, Masatoshi Yoshikawa, Yonghui Xiao +1
Differential Privacy (DP) has received increasing attention as a rigorous privacy framework. Many existing studies employ traditional DP mechanisms (e.g., the Laplace mechanism) as…
Quantifying Differential Privacy under Temporal Correlations
Yang Cao, Masatoshi Yoshikawa, Yonghui Xiao +1
Differential Privacy (DP) has received increased attention as a rigorous privacy framework. Existing studies employ traditional DP mechanisms (e.g., the Laplace mechanism) as primi…
DPHMM: Customizable Data Release with Differential Privacy via Hidden Markov Model
Yonghui Xiao, Yilin Shen, Jinfei Liu +3
Hidden Markov model (HMM) has been well studied and extensively used. In this paper, we present DPHMM ({Differentially Private Hidden Markov Model}), an HMM embedded with a private…
Protecting Locations with Differential Privacy under Temporal Correlations
Yonghui Xiao, Li Xiong
Concerns on location privacy frequently arise with the rapid development of GPS enabled devices and location-based applications. While spatial transformation techniques such as loc…
Bayesian inference under differential privacy
Yonghui Xiao, Li Xiong
Bayesian inference is an important technique throughout statistics. The essence of Beyesian inference is to derive the posterior belief updated from prior belief by the learned inf…
DPCube: Differentially Private Histogram Release through Multidimensional Partitioning
Yonghui Xiao, Li Xiong, Liyue Fan +1
Differential privacy is a strong notion for protecting individual privacy in privacy preserving data analysis or publishing. In this paper, we study the problem of differentially p…