11 citations · 31 across the 14 of their papers we have counts for
14 papers
Shuffling-Aware Optimization for Private Vector Mean Estimation
Shun Takagi, Seng Pei Liew
We study -dimensional unbiased mean estimation in the single-message shuffle model, where each user sends a single privatized message and the analyzer only observes the shuffled…
Analysis of Shuffling Beyond Pure Local Differential Privacy
Shun Takagi, Seng Pei Liew
Shuffling is a powerful way to amplify privacy of a local randomizer in private distributed data analysis. Most existing analyses of how shuffling amplifies privacy are based on th…
DPSQL+: A Differentially Private SQL Library with a Minimum Frequency Rule
Tomoya Matsumoto, Shokichi Takakura, Shun Takagi +1
SQL is the de facto interface for exploratory data analysis; however, releasing exact query results can expose sensitive information through membership or attribute inference attac…
Securing Private Federated Learning in a Malicious Setting: A Scalable TEE-Based Approach with Client Auditing
Shun Takagi, Satoshi Hasegawa
In cross-device private federated learning, differentially private follow-the-regularized-leader (DP-FTRL) has emerged as a promising privacy-preserving method. However, existing a…
HRNet: Differentially Private Hierarchical and Multi-Resolution Network for Human Mobility Data Synthesization
Shun Takagi, Li Xiong, Fumiyuki Kato +2
Human mobility data offers valuable insights for many applications such as urban planning and pandemic response, but its use also raises privacy concerns. In this paper, we introdu…
ULDP-FL: Federated Learning with Across Silo User-Level Differential Privacy
Fumiyuki Kato, Li Xiong, Shun Takagi +2
Differentially Private Federated Learning (DP-FL) has garnered attention as a collaborative machine learning approach that ensures formal privacy. Most DP-FL approaches ensure DP a…
Network Shuffling: Privacy Amplification via Random Walks
Seng Pei Liew, Tsubasa Takahashi, Shun Takagi +3
Recently, it is shown that shuffling can amplify the central differential privacy guarantees of data randomized with local differential privacy. Within this setup, a centralized, t…
HDPView: Differentially Private Materialized View for Exploring High Dimensional Relational Data
Fumiyuki Kato, Tsubasa Takahashi, Shun Takagi +3
How can we explore the unknown properties of high-dimensional sensitive relational data while preserving privacy? We study how to construct an explorable privacy-preserving materia…
Asymmetric Differential Privacy
Shun Takagi, Yang Cao, Masatoshi Yoshikawa
Differential privacy (DP) is getting attention as a privacy definition when publishing statistics of a dataset. This paper focuses on the limitation that DP inevitably causes two-s…
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…
Differentially Private Variational Autoencoders with Term-wise Gradient Aggregation
Tsubasa Takahashi, Shun Takagi, Hajime Ono +1
This paper studies how to learn variational autoencoders with a variety of divergences under differential privacy constraints. We often build a VAE with an appropriate prior distri…
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…
P3GM: Private High-Dimensional Data Release via Privacy Preserving Phased Generative Model
Shun Takagi, Tsubasa Takahashi, Yang Cao +1
How can we release a massive volume of sensitive data while mitigating privacy risks? Privacy-preserving data synthesis enables the data holder to outsource analytical tasks to an…
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…