3 papers
cs.CR2026
Fully Oblivious Differential Privacy for Frequency Estimation in the Augmented Shuffle Model with Trusted Processors
Takao Murakami, Yuichi Sei, Reo Eriguchi
In the shuffle model of DP (Differential Privacy), a shuffler randomly permutes users' data to achieve high accuracy and privacy. Recent studies show that most existing shuffle pro…
cs.CR2025
Augmented Shuffle Differential Privacy Protocols for Large-Domain Categorical and Key-Value Data
Takao Murakami, Yuichi Sei, Reo Eriguchi
Shuffle DP (Differential Privacy) protocols provide high accuracy and privacy by introducing a shuffler who randomly shuffles data in a distributed system. However, most shuffle DP…
cs.CR2025
Augmented Shuffle Protocols for Accurate and Robust Frequency Estimation under Differential Privacy
Takao Murakami, Yuichi Sei, Reo Eriguchi
The shuffle model of DP (Differential Privacy) provides high utility by introducing a shuffler that randomly shuffles noisy data sent from users. However, recent studies show that…