Dr. Yang CAO (曹洋) Associate Professor Laboratory for Algorithmics Division of Computer Science and Information Technology Graduate School of Information Science and Technology Hokkaido University I am looking for Postdoctoral Fellows, PhD students, and Research Assistants. Please see Recruitment for more details. Research Interests Differential Privacy, Federated Learning, Data Economy, Trustworthy AI. Email: yang@ist.hokudai.ac.jp Short Bio Yang Cao is an Associate Professor at the Graduate School of Information Science and Technology, Hokkaido University. After earning a Ph.D. in Informatics from Kyoto University in 2017, he spent one year as a postdoctoral fellow at Emory hosted by Prof. Li Xiong. He then returned to Kyoto University in 2018 as a program-specific faculty member working with Prof. Masatoshi Yoshikawa until September 2022. Currently, he is an Associate Professor (tenured) at Hokkaido University and co-directing Laboratory for Algorithmics with Prof. Atsuyoshi Nakamura. He is passionate about studying and teaching on algorithmic trustworthiness in data science and AI. Two of his papers on data privacy were selected as best paper finalists in top-tier conferences IEEE ICDE 2017 and ICME 2020. He was a recipient of the IEEE Computer Society Japan Chapter Young Author Award 2019, Database Society of Japan Kambayashi Young Researcher Award 2021. His research projects were/are supported by JSPS, JST, NSF, MSRA, KDDI, LINE, WeBank, etc. Selected Publications (Google scholar, DBLP, Research Map) OLIVE: Oblivious Federated Learning on Trusted Execution Environment Against the Risk of Sparsification. Fumiyuki Kato, Yang Cao, Masatoshi Yoshikawa. VLDB 2023 [arXiv] [Code] Secure Shapley Value for Cross-Silo Federated Learning. Shuyuan Zheng, Yang Cao, Masatoshi Yoshikawa. VLDB 2023 [arXiv] [Code] 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] Research Grants PI: JST PRESTO, New Trust Enhancing Technologies for Large Language Models, 2023-2027. Co-PI: JST CREST (PI: Prof. Kenjiro Taura at UTokyo), Privacy-Preserving Data Analysis and Secure Data Infrastructure for Real Applications, 2021-2027. Co-PI: JST/NSF SICORP (Japan PI: Prof. Masatoshi Yoshikawa at KyotoU, US PI: Prof. Li Xiong at Emory), Hyperlocal Risk Monitoring and Pandemic Preparedness through Privacy-Enhanced Mobility and Social Interactions Analysis, 2021-2024. PI: JSPS Grant-in-Aid for Scientific Research (B), A Principled Framework for Explaining, Choosing and Negotiating Privacy Parameters of Differential Privacy, 2022-2025. Co-PI: JSPS Grant-in-Aid for Scientific Research (A) (PI: Dr. Takao Murakami at AIST), 分散型ソーシャルグラフに向けた差分プライバシー技術, 2022-2027. Co-PI: JSPS Grant-in-Aid for Exploratory Research (PI: Prof. Masatoshi Yoshikawa at KyotoU), 個人の選好と報酬配分を考慮したパーソナルデータの健全で頑健な流通系構築に向けて, 2021-2024. PI: Kayamori Foundation of Informational Science Advancement, Privacy-Preserving Face Image Sharing, 2022-2023. PI: KDDI Foundation, Privacy-Preserving Spatiotemporal Data Collection and Analysis for Epidemic Surveillance, 2021-2023. PI: CCF-Tencent Rhino-Bird Young Faculty Open Research Fund, Privacy Protection and Incentivization in Federated Learning, 2020-2021. PI (Collaborator: Prof. Junichi Yamagishi at NII): NII Open Collaborative Research Funds, Speaker De-identification with Provable Privacy in Speech Data Release, 2020-2021. PI: Microsoft Research Asia Collaborative Research Grant 2020 (CORE16), Towards a Unified, Customizable and Rigorous Spatiotemporal Event Privacy Protection Framework, 2020. PI: JSPS Grant-in-Aid for Early-Career Scientists, Achieving Differential Privacy under Spatiotemporal Correlations, 2019-2021. Professional Service I am/was a program committee member for the following conferences/workshops: International Conference on Very Large Data Bases (VLDB) 2023, 2024. ACM SIGMOD International Conference on Management of Data (SIGMOD) 2022, 2024. ACM SIGKDDD Conference on Knowledge Discovery and Data Mining (KDD) 2022, 2023. IEEE International Conference on Data Engineering (ICDE) 2020, 2021, 2022, 2023. The AAAI Conference on Artificial Intelligence (AAAI) 2021, 2022 (Senior PC) 2023 ACM International Conference on Web Search and Data Mining (WSDM), 2022. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), 2023. ACM Symposium on Cloud Computing (ACM SoCC), 2023. IEEE International Conference on Big Data (IEEE BigData) 2020, 2022, 2023. ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL) 2019, 2020, 2021. 2022. International Conference on Extending Database Technology (EDBT) 2022, 2023, 2024. International Conference on Database Systems for Advanced Applications (DASFAA) 2019, 2020, 2021, 2022, 2023, 2024. The Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2023, 2024. SIAM International Conference on Data Mining (SDM), 2024. IEEE International Conference on Multimedia and Expo (ICME) 2020, 2021, 2022. International Conference on Acoustics, Speech, & Signal Processing (ICASSP) 2023. Workshop on Privacy in the Electronic Society (WPES) 2022, 2023. Annual IFIP WG 11.3 Conference on Data and Applications Security and Privacy (DBSec) 2022. International Workshop on Blockchain and Distributed Ledger (BCDL) in conjunction with VLDB 2019. IEEE International Workshop on Blockchain and Data Management (BlockDM) 2019, 2020, 2021. International Workshop on Software Foundations for Data Interoperability (SFDI) 2019, 2020, 2021. International Workshop on Data Management and Analytics for Medicine and Healthcare (DMAH) in conjunction with VLDB 2020, 2021. International Workshop on Data Science for Data Marketplaces (DSDM) In conjunction with the International Conference on Very Large Data Bases 2022. I am/was invited reviewers for the following journals: Very Large Data Base (VLDB) Journal IEEE Transactions on Knowledge and Data Engineering (TKDE) IEEE Transactions on Information Forensics and Security (TIFS) IEEE Transactions on Dependable and Secure Computing (TDSC) Distributed and Parallel Databases (DAPD), Springer Information Sciences, Elsevier Computer Networks, Elsevier Computers & Security, Elsevier Future Generation Computer Systems (FGCS), Elsevier IET Information Security International Journal of Information Security (IJIS) IEEE Transactions on Big Data IEEE Transactions on Signal Processing IEEE Access Teaching プログラム理論と言語, 2023 冬. 情報学II, 2023 秋. Theory and Practice of Algorithms, 2023 Spring. 計算機プログラミングⅠ・演習, 2023 春. 科学技術英語演習, 2023 春夏. Practice of Information Systems, 2021/2022 Spring. Distributed Information Systems, 2020/2021 Fall. PBL/FBL Privacy-enhancing Techniques for Combating COVID-19, 2020 Spring PBL/FBL Personal Data Market, 2018 Fall.