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pro vyhledávání: '"Kato, Fumiyuki"'
Autor:
Kato, Fumiyuki
甲第25427号
情博第865号
新制||情||145(附属図書館)
学位規則第4条第1項該当
Doctor of Informatics
Kyoto University
DFAM
情博第865号
新制||情||145(附属図書館)
学位規則第4条第1項該当
Doctor of Informatics
Kyoto University
DFAM
Externí odkaz:
http://hdl.handle.net/2433/288861
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 introduce the Hierarchical and Multi-Resolution Network (HRNet), a novel deep
Externí odkaz:
http://arxiv.org/abs/2405.08043
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 at the record-level within each silo for cross-silo FL. However, a sing
Externí odkaz:
http://arxiv.org/abs/2308.12210
Autor:
Liew, Seng Pei, Takahashi, Tsubasa, Takagi, Shun, Kato, Fumiyuki, Cao, Yang, Yoshikawa, Masatoshi
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, trusted shuffler is responsible for shuffling by keeping the identities
Externí odkaz:
http://arxiv.org/abs/2204.03919
Autor:
Kato, Fumiyuki, Takahashi, Tsubasa, Takagi, Shun, Cao, Yang, Liew, Seng Pei, Yoshikawa, Masatoshi
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 materialized view under differential privacy. No existing state-of-the-art me
Externí odkaz:
http://arxiv.org/abs/2203.06791
Combining Federated Learning (FL) with a Trusted Execution Environment (TEE) is a promising approach for realizing privacy-preserving FL, which has garnered significant academic attention in recent years. Implementing the TEE on the server side enabl
Externí odkaz:
http://arxiv.org/abs/2202.07165
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 generally vulnerable to malicious data providers in nature. Because a
Externí odkaz:
http://arxiv.org/abs/2104.06569
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 existing systems lack functionality and flexibility, which may hurt the
Externí odkaz:
http://arxiv.org/abs/2012.03782
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. However, the recently proposed Bluetooth-based PCT has several limitatio
Externí odkaz:
http://arxiv.org/abs/2010.13381
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