31 citations · 91 across the 18 of their papers we have counts for
19 papers
Data Overvaluation Attack and Truthful Data Valuation in Federated Learning
Shuyuan Zheng, Sudong Cai, Chuan Xiao +4
In collaborative machine learning (CML), data valuation, i.e., evaluating the contribution of each client's data to the machine learning model, has become a critical task for incen…
Extracting Spatiotemporal Data from Gradients with Large Language Models
Lele Zheng, Yang Cao, Renhe Jiang +4
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 primar…
PGB: Benchmarking Differentially Private Synthetic Graph Generation Algorithms
Shang Liu, Hao Du, Yang Cao +3
Differentially private graph analysis is a powerful tool for deriving insights from diverse graph data while protecting individual information. Designing private analytic algorithm…
Enhancing Privacy of Spatiotemporal Federated Learning against Gradient Inversion Attacks
Lele Zheng, Yang Cao, Renhe Jiang +4
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. O…
Federated Graph Analytics with Differential Privacy
Shang Liu, Yang Cao, Takao Murakami +5
Collaborative graph analysis across multiple institutions is becoming increasingly popular. Realistic examples include social network analysis across various social platforms, fina…
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…
CARGO: Crypto-Assisted Differentially Private Triangle Counting without Trusted Servers
Shang Liu, Yang Cao, Takao Murakami +2
Differentially private triangle counting in graphs is essential for analyzing connection patterns and calculating clustering coefficients while protecting sensitive individual info…
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…
Differentially Private Streaming Data Release under Temporal Correlations via Post-processing
Xuyang Cao, Yang Cao, Primal Pappachan +2
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 stre…
Local Differential Privacy Image Generation Using Flow-based Deep Generative Models
Hisaichi Shibata, Shouhei Hanaoka, Yang Cao +5
Diagnostic radiologists need artificial intelligence (AI) for medical imaging, but access to medical images required for training in AI has become increasingly restrictive. To rele…
Secure Shapley Value for Cross-Silo Federated Learning (Technical Report)
Shuyuan Zheng, Yang Cao, Masatoshi Yoshikawa
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, collabora…
A Crypto-Assisted Approach for Publishing Graph Statistics with Node Local Differential Privacy
Shang Liu, Yang Cao, Takao Murakami +1
Publishing graph statistics under node differential privacy has attracted much attention since it provides a stronger privacy guarantee than edge differential privacy. Existing wor…
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…
Understanding the Interplay between Privacy and Robustness in Federated Learning
Yaowei Han, Yang Cao, Masatoshi Yoshikawa
Federated Learning (FL) is emerging as a promising paradigm of privacy-preserving machine learning, which trains an algorithm across multiple clients without exchanging their data…
FL-Market: Trading Private Models in Federated Learning
Shuyuan Zheng, Yang Cao, Masatoshi Yoshikawa +2
The difficulty in acquiring a sufficient amount of training data is a major bottleneck for machine learning (ML) based data analytics. Recently, commoditizing ML models has been pr…
Pricing Private Data with Personalized Differential Privacy and Partial Arbitrage Freeness
Shuyuan Zheng, Yang Cao, Masatoshi Yoshikawa
There is a growing trend regarding perceiving personal data as a commodity. Existing studies have built frameworks and theories about how to determine an arbitrage-free price of a…
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…