Zobrazeno 1 - 10
of 369
pro vyhledávání: '"YOSHIKAWA, MASATOSHI"'
Autor:
Zheng, Lele, Cao, Yang, Jiang, Renhe, Taura, Kenjiro, Shen, Yulong, Li, Sheng, Yoshikawa, Masatoshi
Recent works show that sensitive user data can be reconstructed from gradient updates, breaking the key privacy promise of federated learning. While success was demonstrated primarily on image data, these methods do not directly transfer to other dom
Externí odkaz:
http://arxiv.org/abs/2410.16121
Differentially private graph analysis is a powerful tool for deriving insights from diverse graph data while protecting individual information. Designing private analytic algorithms for different graph queries often requires starting from scratch. In
Externí odkaz:
http://arxiv.org/abs/2408.02928
Autor:
Zheng, Lele, Cao, Yang, Jiang, Renhe, Taura, Kenjiro, Shen, Yulong, Li, Sheng, Yoshikawa, Masatoshi
Spatiotemporal federated learning has recently raised intensive studies due to its ability to train valuable models with only shared gradients in various location-based services. On the other hand, recent studies have shown that shared gradients may
Externí odkaz:
http://arxiv.org/abs/2407.08529
Autor:
Liu, Shang, Cao, Yang, Murakami, Takao, Liu, Weiran, Liew, Seng Pei, Takahashi, Tsubasa, Liu, Jinfei, Yoshikawa, Masatoshi
Collaborative graph analysis across multiple institutions is becoming increasingly popular. Realistic examples include social network analysis across various social platforms, financial transaction analysis across multiple banks, and analyzing the tr
Externí odkaz:
http://arxiv.org/abs/2405.20576
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 triangle counting in graphs is essential for analyzing connection patterns and calculating clustering coefficients while protecting sensitive individual information. Previous works have relied on either central or local models
Externí odkaz:
http://arxiv.org/abs/2312.12938
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
The release of differentially private streaming data has been extensively studied, yet striking a good balance between privacy and utility on temporally correlated data in the stream remains an open problem. Existing works focus on enhancing privacy
Externí odkaz:
http://arxiv.org/abs/2306.13293
Autor:
Shibata, Hisaichi, Hanaoka, Shouhei, Cao, Yang, Yoshikawa, Masatoshi, Takenaga, Tomomi, Nomura, Yukihiro, Hayashi, Naoto, Abe, Osamu
Diagnostic radiologists need artificial intelligence (AI) for medical imaging, but access to medical images required for training in AI has become increasingly restrictive. To release and use medical images, we need an algorithm that can simultaneous
Externí odkaz:
http://arxiv.org/abs/2212.10688
Publikováno v:
Proceedings of the VLDB Endowment, 16(7): 1657-1670, 2023
The Shapley value (SV) is a fair and principled metric for contribution evaluation in cross-silo federated learning (cross-silo FL), wherein organizations, i.e., clients, collaboratively train prediction models with the coordination of a parameter se
Externí odkaz:
http://arxiv.org/abs/2209.04856