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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 6 of 6  /   
Committee Date Time Place Paper Title / Authors Abstract Paper #
ICSS, IPSJ-SPT 2024-03-21
14:30		 Okinawa OIST
(Primary: On-site, Secondary: Online)		 A poisoning-resilient LDP schema using oblivious transfer and Hadamard transform
Masahiro Shimizu, Hiroaki Kikuchi (Meiji Univ.) ICSS2023-80		 In recent years, Local Differential Privacy (LDP) has been actively used to collect and utilize users’ usage history from... [more] ICSS2023-80
pp.79-86
AI 2023-09-12
14:35		 Hokkaido   Proposal of effective location information privacy protection method using measurement equipment error
Riho Isawa, Yuichi Sei, Yasuyuki Tahara, Akihiko Ahsuga (UEC) AI2023-15		 In recent years, systems that publicly share statistical data based on people's location information, such as congestion... [more] AI2023-15
pp.77-82
RCC, ISEC, IT, WBS 2023-03-14
10:30		 Yamaguchi
(Primary: On-site, Secondary: Online)		 [Invited Talk] Information-Theoretic Analyses for Two Problems Taking Security into Consideration -- Parameter Estimation Problem under Local Differential Privacy, and Privacy-Utility Tradeoff Problem --
Shota Saito (Gunma Univ.) IT2022-71 ISEC2022-50 WBS2022-68 RCC2022-68		 This lecture surveys the following two problems: 1) parameter estimation problem under $(epsilon, delta)$-local differen... [more] IT2022-71 ISEC2022-50 WBS2022-68 RCC2022-68
pp.19-24
NS, SR, RCS, SeMI, RCC
(Joint)		 2022-07-14
14:00		 Ishikawa The Kanazawa Theatre + Online
(Primary: On-site, Secondary: Online)		 A Study of Dynamic Privacy Control Method in LDP for Preserving Temporal/Spatial Intrinsic Information Value of Trajectory Data
Taisho Sasada, Yuzo Taenaka, Youki Kadobayashi (NAIST) NS2022-47		 Spatiotemporal data, including people's movement trajectories and transit points, is highly valuable for various applica... [more] NS2022-47
pp.106-111
RCS, SR, NS, SeMI, RCC
(Joint)		 2021-07-16
13:25		 Online Online An Evaluation of Learning Accuracy in Federated Learning with Local Differential Privacy
Yuta Kakizaki, Koya Sato, Keiichi Iwamura (Tokyo Univ. of Science) SR2021-37		 In federated learning, where each device learns cooperatively without disclosing the training data, the privacy level ca... [more] SR2021-37
pp.87-93
LOIS, ISEC, SITE 2020-11-06
14:40		 Online Online Considering the variation of the noise-addition amount in local differential privacy with user trajectory adjacency
Taisho Sasada, Yuzo Taenaka, Youki Kadobayashi, Doudou Fall (NAIST) ISEC2020-39 SITE2020-36 LOIS2020-19		 The detection of clusters in densely populated areas by contact tracing is urgent, because these locations are trajector... [more] ISEC2020-39 SITE2020-36 LOIS2020-19
pp.45-50
 Results 1 - 6 of 6  /   
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