Selected Publications

(Full publication list can be found at Google scholar, DBLP, Research Map)

• TabularMark: Watermarking Tabular Datasets for Machine Learning
  Yihao Zheng, Haocheng Xia, Junyuan Pang, Jinfei Liu, Kui Ren, Lingyang Chu, Yang Cao, Li Xiong.
  ACM CCS 2024

• PreCurious: How Innocent Pre-Trained Language Models Turn into Privacy Traps.
  Ruixuan Liu, Tianhao Wang, Yang Cao, Li Xiong.
  ACM CCS 2024 [arXiv]

• ULDP-FL: Federated Learning with Across Silo User-Level Differential Privacy.
  Fumiyuki Kato, Li Xiong, Shun Takagi, Yang Cao, Masatoshi Yoshikawa.
  VLDB 2024 [arXiv]

• HRNet: Differentially Private Hierarchical and Multi-Resolution Network for Human Mobility Data Synthesization.
  Shun Takagi, Li Xiong, Fumiyuki Kato, Yang Cao, Masatoshi Yoshikawa.
  VLDB 2024 [arXiv]

• Noise-Aware Algorithm for Heterogeneous Differentially Private Federated Learning.
  Saber Malekmohammadi, Yaoliang Yu, Yang Cao.
  ICML 2024 [arXiv]

• Optimal Graph Learning and Nuclear Norm Maximization for Deep Cross-Domain Robust Label Propagation.
  Wei Wang, Hanyang Li, Ke Shi, Chao Huang, Yang Cao, Cong Wang, Xiaochun Cao.
  IJCAI 2024

• Federated Heterogeneous Graph Neural Network for Privacy-preserving Recommendation.
  Bo Yan, Yang Cao, Haoyu Wang, Wenchuan Yang, Junping Du, Chuan Shi
  WWW 2024

• A Generalized Shuffle Framework for Privacy Amplification: Strengthening Privacy Guarantees and Enhancing Utility.
  E Chen, Yang Cao, Yifei Ge.
  AAAI 2024

• CARGO: Crypto-Assisted Differentially Private Triangle Counting without Trusted Servers.
  Shang Liu, Yang Cao, Takao Murakami, Jinfei Liu, Masatoshi Yoshikawa.
  ICDE 2024 [arXiv] [Code]

• OLIVE: Oblivious Federated Learning on Trusted Execution Environment Against the Risk of Sparsification.
  Fumiyuki Kato, Yang Cao, Masatoshi Yoshikawa.
  VLDB 2023 [arXiv] [Code] [Slides]

• Secure Shapley Value for Cross-Silo Federated Learning.
  Shuyuan Zheng, Yang Cao, Masatoshi Yoshikawa.
  VLDB 2023 [arXiv] [Code] [Slides]

• Equitable Data Valuation Meets the Right to Be Forgotten in Model Markets.
  Haocheng Xia, Jinfei Liu, Jian Lou, Zhan Qin, Kui Ren, Yang Cao, Li Xiong.
  VLDB 2023

• PrivateRec: Differentially Private Model Training and Online Serving for Federated News Recommendation.
  Ruixuan Liu, Yang Cao, Yanlin Wang, Lingjuan Lyu, Yun Chen, Hong Chen.
  KDD 2023 [arXiv]

• HDPView: Differentially Private Materialized View for Exploring High Dimensional Relational Data.
  Fumiyuki Kato, Tsubasa Takahashi, Shun Takagi, Yang Cao, Seng Pei Liew, Masatoshi Yoshikawa.
  VLDB 2022 [arXiv] [Code]

• Network Shuffling: Privacy Amplification via Random Walks.
  Seng Pei Liew, Tsubasa Takahashi, Shun Takagi, Fumiyuki Kato, Yang Cao, Masatoshi Yoshikawa.
  SIGMOD 2022 [arXiv]

• FL-Market: Trading Private Models in Federated Learning.
  Shuyuan Zheng, Yang Cao, Masatoshi Yoshikawa, Huizhong Li, Qiang Yan
  IEEE BigData 2022 Selected as a Top-10 Best Paper [arXiv] [Slides] [Code]

• FLAME: Differentially Private Federated Learning in the Shuffle Model.
  Ruixuan Liu, Yang Cao, Hong Chen, Ruoyang Guo, Masatoshi Yoshikawa.
  AAAI 2021 [arXiv] [Slides] [Code]

• P3GM: Private High-Dimensional Data Release via Privacy Preserving Phased Generative Model.
  Shun Takagi, Tsubasa Takahashi, Yang Cao, Masatoshi Yoshikawa.
  IEEE ICDE 2020 [arXiv]

• PGLP: Customizable and Rigorous Location Privacy through Policy Graph.
  Yang Cao, Yonghui Xiao, Shun Takagi, Li Xiong, Masatoshi Yoshikawa, Yilin Shen, Jinfei Liu, Hongxia Jin, Xiaofeng Xu.
  ESORICS 2020 [arXiv] [Code] [Slides]

• PCKV: Locally Differentially Private Correlated Key-Value Data Collection with Optimized Utility.
  Xiaolan Gu, Ming Li, Yueqiang Cheng, Li Xiong and Yang Cao.
  USENIX Security 2020 [arXiv] [Slides] [Youtube]

• FedSel: Federated SGD under Local Differential Privacy with Top-k Dimension Selection.
  Ruixuan Liu, Yang Cao, Masatoshi Yoshikawa, Hong Chen.
  DASFAA 2020 [arXiv] [Slides] [PDF]

• Voice-Indistinguishability: Protecting Voiceprint in Privacy Preserving Speech Data Release.
  Yaowei Han, Sheng Li, Yang Cao, Qiang Ma, Masatoshi Yoshikawa.
  IEEE ICME 2020 Selected as a Top-10 Best Paper [arXiv] [Slides] [Code]

• Providing Input-Discriminative Protection for Local Differential Privacy.
  Xiaolan Gu, Ming Li, Li Xiong and Yang Cao.
  IEEE ICDE 2020 [arXiv] [Slides]

• Protecting Spatiotemporal Event Privacy in Continuous Location-Based Services.
  Yang Cao, Yonghui Xiao, Li Xiong, Liquan Bai and Masatoshi Yoshikawa.
  IEEE TKDE 2019 [arXiv] [IEEE]

• Quantifying Differential Privacy in Continuous Data Release under Temporal Correlations.
  Yang Cao, Masatoshi Yoshikawa, Yonghui Xiao, Li Xiong.
  IEEE TKDE 2018, the special issue on Best of ICDE 2017. [Paper] [Code] [Slides] [Poster]