111 citations · 430 across the 24 of their papers we have counts for
24 papers
TabularMark: Watermarking Tabular Datasets for Machine Learning
Yihao Zheng, Haocheng Xia, Junyuan Pang +5
Watermarking is broadly utilized to protect ownership of shared data while preserving data utility. However, existing watermarking methods for tabular datasets fall short on the de…
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…
PreCurious: How Innocent Pre-Trained Language Models Turn into Privacy Traps
Ruixuan Liu, Tianhao Wang, Yang Cao +1
The pre-training and fine-tuning paradigm has demonstrated its effectiveness and has become the standard approach for tailoring language models to various tasks. Currently, communi…
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…
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…
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…
Transparent Contribution Evaluation for Secure Federated Learning on Blockchain
Shuaicheng Ma, Yang Cao, Li Xiong
Federated Learning is a promising machine learning paradigm when multiple parties collaborate to build a high-quality machine learning model. Nonetheless, these parties are only wi…
PANDA: Policy-aware Location Privacy for Epidemic Surveillance
Yang Cao, Shun Takagi, Yonghui Xiao +2
In this demonstration, we present a privacy-preserving epidemic surveillance system. Recently, many countries that suffer from coronavirus crises attempt to access citizen's locati…
P3GM: Private High-Dimensional Data Release via Privacy Preserving Phased Generative Model
Shun Takagi, Tsubasa Takahashi, Yang Cao +1
How can we release a massive volume of sensitive data while mitigating privacy risks? Privacy-preserving data synthesis enables the data holder to outsource analytical tasks to an…
PGLP: Customizable and Rigorous Location Privacy through Policy Graph
Yang Cao, Yonghui Xiao, Shun Takagi +6
Location privacy has been extensively studied in the literature. However, existing location privacy models are either not rigorous or not customizable, which limits the trade-off b…
Voice-Indistinguishability: Protecting Voiceprint in Privacy-Preserving Speech Data Release
Yaowei Han, Sheng Li, Yang Cao +2
With the development of smart devices, such as the Amazon Echo and Apple's HomePod, speech data have become a new dimension of big data. However, privacy and security concerns may…
FedSel: Federated SGD under Local Differential Privacy with Top-k Dimension Selection
Ruixuan Liu, Yang Cao, Masatoshi Yoshikawa +1
As massive data are produced from small gadgets, federated learning on mobile devices has become an emerging trend. In the federated setting, Stochastic Gradient Descent (SGD) has…
PCKV: Locally Differentially Private Correlated Key-Value Data Collection with Optimized Utility
Xiaolan Gu, Ming Li, Yueqiang Cheng +2
Data collection under local differential privacy (LDP) has been mostly studied for homogeneous data. Real-world applications often involve a mixture of different data types such as…
Providing Input-Discriminative Protection for Local Differential Privacy
Xiaolan Gu, Ming Li, Li Xiong +1
Local Differential Privacy (LDP) provides provable privacy protection for data collection without the assumption of the trusted data server. In the real-world scenario, different d…
Effcient logging and querying for Blockchain-based cross-site genomic dataset access audit
Shuaicheng Ma, Yang Cao, Li Xiong
Background: Genomic data have been collected by different institutions and companies and need to be shared for broader use. In a cross-site genomic data sharing system, a secure an…
Protecting Spatiotemporal Event Privacy in Continuous Location-Based Services
Yang Cao, Yonghui Xiao, Li Xiong +2
Location privacy-preserving mechanisms (LPPMs) have been extensively studied for protecting users' location privacy by releasing a perturbed location to third parties such as locat…
Trading Location Data with Bounded Personalized Privacy Loss
Shuyuan Zheng, Yang Cao, Masatoshi Yoshikawa
As personal data have been the new oil of the digital era, there is a growing trend perceiving personal data as a commodity. Although some people are willing to trade their persona…
Blockchain-based Bidirectional Updates on Fine-grained Medical Data
Chunmiao Li, Yang Cao, Zhenjiang Hu +1
Electronic medical data sharing between stakeholders, such as patients, doctors, and researchers, can promote more effective medical treatment collaboratively. These sensitive and…
When and where do you want to hide? Recommendation of location privacy preferences with local differential privacy
Maho Asada, Masatoshi Yoshikawa, Yang Cao
In recent years, it has become easy to obtain location information quite precisely. However, the acquisition of such information has risks such as individual identification and lea…
PriSTE: From Location Privacy to Spatiotemporal Event Privacy
Yang Cao, Yonghui Xiao, Li Xiong +1
Location privacy-preserving mechanisms (LPPMs) have been extensively studied for protecting a user's location at each time point or a sequence of locations with different timestamp…
Quantifying Differential Privacy in Continuous Data Release under Temporal Correlations
Yang Cao, Masatoshi Yoshikawa, Yonghui Xiao +1
Differential Privacy (DP) has received increasing attention as a rigorous privacy framework. Many existing studies employ traditional DP mechanisms (e.g., the Laplace mechanism) as…
How to Balance Privacy and Money through Pricing Mechanism in Personal Data Market
Rachana Nget, Yang Cao, Masatoshi Yoshikawa
A personal data market is a platform including three participants: data owners (individuals), data buyers and market maker. Data owners who provide personal data are compensated ac…
Quantifying Differential Privacy under Temporal Correlations
Yang Cao, Masatoshi Yoshikawa, Yonghui Xiao +1
Differential Privacy (DP) has received increased attention as a rigorous privacy framework. Existing studies employ traditional DP mechanisms (e.g., the Laplace mechanism) as primi…