11 citations · 19 across the 9 of their papers we have counts for
9 papers
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…
OLIVE: Oblivious Federated Learning on Trusted Execution Environment against the risk of sparsification
Fumiyuki Kato, Yang Cao, Masatoshi Yoshikawa
Combining Federated Learning (FL) with a Trusted Execution Environment (TEE) is a promising approach for realizing privacy-preserving FL, which has garnered significant academic at…
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…
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…
PCT-TEE: Trajectory-based Private Contact Tracing System with Trusted Execution Environment
Fumiyuki Kato, Yang Cao, Masatoshi Yoshikawa
Existing Bluetooth-based Private Contact Tracing (PCT) systems can privately detect whether people have come into direct contact with COVID-19 patients. However, we find that the e…
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…