40 citations · 145 across the 11 of their papers we have counts for
11 papers
Advancing Differential Privacy: Where We Are Now and Future Directions for Real-World Deployment
Rachel Cummings, Damien Desfontaines, David Evans +21
In this article, we present a detailed review of current practices and state-of-the-art methodologies in the field of differential privacy (DP), with a focus of advancing DP's depl…
CrowdTeacher: Robust Co-teaching with Noisy Answers & Sample-specific Perturbations for Tabular Data
Mani Sotoodeh, Li Xiong, Joyce C. Ho
Samples with ground truth labels may not always be available in numerous domains. While learning from crowdsourcing labels has been explored, existing models can still fail in the…
An Analysis Of Protected Health Information Leakage In Deep-Learning Based De-Identification Algorithms
Salman Seyedi, Li Xiong, Shamim Nemati +1
The increasing complexity of algorithms for analyzing medical data, including de-identification tasks, raises the possibility that complex algorithms are learning not just the gene…
Spatio-Temporal Tensor Sketching via Adaptive Sampling
Jing Ma, Qiuchen Zhang, Joyce C. Ho +1
Mining massive spatio-temporal data can help a variety of real-world applications such as city capacity planning, event management, and social network analysis. The tensor represen…
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…
PCKV: Locally Differentially Private Correlated Key-Value Data Collection with Optimized Utility
Xiaolan Gu, Ming Li, Yueqiang Cheng +2
Data collection under local differential privacy (LDP) has been mostly studied for homogeneous data. Real-world applications often involve a mixture of different data types such as…
Providing Input-Discriminative Protection for Local Differential Privacy
Xiaolan Gu, Ming Li, Li Xiong +1
Local Differential Privacy (LDP) provides provable privacy protection for data collection without the assumption of the trusted data server. In the real-world scenario, different d…
Privacy-Preserving Tensor Factorization for Collaborative Health Data Analysis
Jing Ma, Qiuchen Zhang, Jian Lou +3
Tensor factorization has been demonstrated as an efficient approach for computational phenotyping, where massive electronic health records (EHRs) are converted to concise and meani…
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…
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…