17 citations · 39 across the 6 of their papers we have counts for
15 papers
Preventing Manipulation Attack in Local Differential Privacy using Verifiable Randomization Mechanism
Fumiyuki Kato, Yang Cao, Masatoshi Yoshikawa
Several randomization mechanisms for local differential privacy (LDP) (e.g., randomized response) are well-studied to improve the utility. However, recent studies show that LDP is…
Transparent Contribution Evaluation for Secure Federated Learning on Blockchain
Shuaicheng Ma, Yang Cao, Li Xiong
Federated Learning is a promising machine learning paradigm when multiple parties collaborate to build a high-quality machine learning model. Nonetheless, these parties are only wi…
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…
Voice-Indistinguishability: Protecting Voiceprint in Privacy-Preserving Speech Data Release
Yaowei Han, Sheng Li, Yang Cao +2
With the development of smart devices, such as the Amazon Echo and Apple's HomePod, speech data have become a new dimension of big data. However, privacy and security concerns may…
FedSel: Federated SGD under Local Differential Privacy with Top-k Dimension Selection
Ruixuan Liu, Yang Cao, Masatoshi Yoshikawa +1
As massive data are produced from small gadgets, federated learning on mobile devices has become an emerging trend. In the federated setting, Stochastic Gradient Descent (SGD) has…