Updated on 2026/04/15

写真a

 
CAO YANG
 
Organization
School of Computing Associate Professor
Title
Associate Professor
Profile

Yang Cao is an Associate Professor at the Department of Computer Science, Tokyo Institute of Technology (Tokyo Tech), and directing the Trustworthy Data Science Lab. 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, MSRA, KDDI, LINE, WeBank, etc.

https://scholar.google.com/citations?hl=en&user=S-p4DFMAAAAJJ 

https://cao-lab.org

 

External link

Degree

  • Ph.D. ( 2017   Kyoto University )

Research Interests

  • Federated Learning

  • Differential Privacy

  • Privacy-Preserving Machine Learning

  • Data Market

  • Blockchain

  • Trustworthy Data Science

Research Areas

  • Informatics / Intelligent informatics

  • Informatics / Database

  • Informatics / Information security

Research History

  • Institute of Science Tokyo   School of Computing

    2024.10

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  • Tokyo Institute of Technology   School of Computing

    2024.4

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  • Hokkaido University   Associate Professor

    2022 - 2024.3

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    Country:Japan

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  • Kyoto University   Graduate School of Informatics   Program-Specific Associate Professor

    2022

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    Country:Japan

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  • Meta Inc   Visiting Researcher

    2021 - 2022

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  • Kyoto University   Graduate School of Informatics   Program-Specific Assistant Professor

    2018 - 2022

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  • Emory University   Department of Computer Science   Postdoctoral Research Fellow

    2017 - 2018

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  • Emory University   Department of Computer Science   Research Assistant

    2016 - 2017

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Committee Memberships

  • International Conference Advanced Data Mining and Applications (ADMA) 2025   Program Chair  

    2025.10   

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  • The Thirteenth International Conference on Advanced Cloud and Big Data (CBD) 2025   General Chair  

    2025.10   

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  • Springer World Wide Web Journal (WWWJ)   Associate Editor  

    2025.8   

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  • ACM Conference on Computer and Communications Security (CCS)   Program Committee Member  

    2025.1 - 2025.12   

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  • Australasian Database Conference (ADC) 2024   Program Chair  

    2024.12   

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  • Springer Data Science and Engineering (DSEJ)   Associate Editor  

    2024.10   

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  • IEEE Transactions on Dependable and Secure Computing (TDSC)   Associate Editor  

    2024.8 - 2026.8   

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  • IPSJ Transactions on Data Processing (IPSJ-TOD)   Associate Editor  

    2024.5   

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  • ACM SIGMOD International Conference on Management of Data (SIGMOD)   Program Committee Member  

    2024.1   

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  • ACM SIGKDDD Conference on Knowledge Discovery and Data Mining (KDD)   Program Committee Member  

    2024.1   

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  • IEEE Open Journal of the Computer Society (OJCS)   Associate Editor  

    2024.1 - 2025.12   

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  • International Conference on Very Large Data Bases (VLDB) 2024   Proceeding co-Chair  

    2024.1 - 2024.12   

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  • IEICE Transactions on Information and Systems, Special Issue on Data Engineering and Information Management   Associate Editor  

    2024.1 - 2024.12   

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  • International Conference on Very Large Data Bases (VLDB)   Program Committee Member  

    2023.1   

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Papers

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MISC

Awards

  • Best Paper Award

    2025.12   Australasian Database Conference (ADC)   "Pursuit of Truth: Incentive Mechanism Involving Privacy Demands in Mobile Crowdsourcing"

    Ping Wang, Pengpeng Qiao, Yang Cao, Tao Feng

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    Award type:Award from international society, conference, symposium, etc. 

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  • Outstanding Paper Award

    2025.10   ACM International Conference on Multimedia   "Differentially Private Visual Learning with Public Subspace Augmented by Synthetic Data"

    Haichao Sha, Yuncheng Wu, Ruixuan Liu, YANG CAO, Hong Chen

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    Award type:Award from international society, conference, symposium, etc. 

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  • DBSJ Kambayashi Young Researcher Award

    2021.3  

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  • Young Author Award

    2019.12   IEEE Computer Society Japan Chapter  

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  • The 1st Place on Blockchain Track

    2019.10   iDash Genome Privacy & Security Competition 2019  

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  • Best Paper Award

    2019.6   DEIM (Data Engineering and Information Management) 2019  

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Research Projects

  • 大規模言語モデルのための新しい信頼性向上技術

    2023 - 2026

    科学技術振興機構  戦略的な研究開発の推進 戦略的創造研究推進事業 さきがけ 

    曹 洋

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    Authorship:Principal investigator 

    大規模言語モデル(LLMs)は私たちの社会を変革していますが、プライバシーや堅牢性、悪用のリスクももたらしています。本研究は、大規模言語モデルの信頼性を向上させる新しい技術を開発することを目指しており、敵対的な環境での大規模言語モデルの堅牢性を向上させ、大規模言語モデルによって生成されたコンテンツの誤用を防ぐことを目的としています。

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    J-GLOBAL

  • A Principled Framework for Explaining, Choosing and Negotiating Privacy Parameters of Differential Privacy

    Grant number:23K24851  2022.4 - 2025.3

    JSPS Grants-in-Aid for Scientific Research (B)  Grants-in-Aid for Scientific Research  Grant-in-Aid for Scientific Research (B)

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    Authorship:Principal investigator 

    Grant amount:\17160000 ( Direct Cost: \13200000 、 Indirect Cost:\3960000 )

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  • 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 (分担)

    Grant number:22H00521  2022.3 - 2027.4

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research  Grant-in-Aid for Scientific Research (A)

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    Authorship:Coinvestigator(s) 

    Grant amount:\41860000 ( Direct Cost: \32200000 、 Indirect Cost:\9660000 )

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  • A Study on Privacy-Preserving Face Image Sharing

    2022.1 - 2023.3

    Kayamori Foundation of Informational Science Advancement  Research Grant 

    Yang CAO

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    Authorship:Principal investigator 

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  • Toward a Healthy and Robust Personal Data Market with Personalized Incentives

    Grant number:21K19767  2021 - 2024

    Grant-in-Aid for Exploratory Research 

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    Authorship:Coinvestigator(s) 

    Grant amount:\6240000 ( Direct Cost: \4800000 、 Indirect Cost:\1440000 )

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  • Location Privacy for Epidemic Surveillance

    2021 - 2023

    KDDI Foundation 

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  • Speaker De-identification with Provable Privacy in Speech Data Release

    2020.4 - 2021.3

    National Institute of Informatics  NII Open Collaborative Research 

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    Authorship:Principal investigator 

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  • Microsoft Collaborative Research Grant (CORE 16) 2020

    2020.1 - 2020.12

    Microsoft 

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    Authorship:Principal investigator  Grant type:Competitive

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  • Achieving Differential Privacy under Spatiotemporal Correlations

    Grant number:19K20269  2019.4 - 2022.3

    Japan Society for the Promotion of Science (JSPS)  Grant-in-Aid for Early-Career Scientists  Grant-in-Aid for Early-Career Scientists

    Yang Cao

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    Authorship:Principal investigator  Grant type:Competitive

    Differential Privacy has been extensively studied and deployed as the de facto privacy standard for preserving data privacy during collection and analysis. In this project, we demonstrate the potential risks and utility insufficiency of Differential Privacy when applied to spatiotemporal data. We propose new, flexible privacy notions for spatiotemporal data, such as Geo-graph-indistinguishability (DBSec 2019, IEICE 2023), Spatiotemporal Event Privacy (IEEE ICDE 2019, IEEE TKDE 2019), and Policy-based Location Privacy (ESORICS 2020) to achieve a better privacy-utility tradeoff.

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  • A study on privacy protection of time-series data through negotiation

    Grant number:16K12437  2016.4 - 2019.3

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research  Grant-in-Aid for Challenging Exploratory Research

    Yoshikawa Masatoshi, CAO Yang

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    Grant amount:\3380000 ( Direct Cost: \2600000 、 Indirect Cost:\780000 )

    It is important to collect, analyze, and utilize personal data for pubic welfare as well as protecting privacy. In this research, we have developed a market mechanism for personal data exchange which allows each individual to specify an upper limit of the degree of disclosure of her personal data.
    Recently, differential privacy, which can express privacy information leakage risk mathematically, is widely studied. However, since differential privacy was developed for static data, We have extended the notion of differential privacy to be applicable to time series data.

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