9 citations · 22 across the 8 of their papers we have counts for
19 papers
Quantifying the Privacy-Utility Trade-offs in COVID-19 Contact Tracing Apps
Patrick Ocheja, Yang Cao, Shiyao Ding +1
How to contain the spread of the COVID-19 virus is a major concern for most countries. As the situation continues to change, various countries are making efforts to reopen their ec…
Secure and Efficient Trajectory-Based Contact Tracing using Trusted Hardware
Fumiyuki Kato, Yang Cao, Masatoshi Yoshikawa
The COVID-19 pandemic has prompted technological measures to control the spread of the disease. Private contact tracing (PCT) is one of the promising techniques for the purpose. Ho…
Geo-Graph-Indistinguishability: Location Privacy on Road Networks Based on Differential Privacy
Shun Takagi, Yang Cao, Yasuhito Asano +1
In recent years, concerns about location privacy are increasing with the spread of location-based services (LBSs). Many methods to protect location privacy have been proposed in th…
FLAME: Differentially Private Federated Learning in the Shuffle Model
Ruixuan Liu, Yang Cao, Hong Chen +2
Federated Learning (FL) is a promising machine learning paradigm that enables the analyzer to train a model without collecting users' raw data. To ensure users' privacy, differenti…
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…