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A Study on Locally Private Algorithms for Large-Scale Personal Data

Research Project

Project/Area Number 19H04113
Research Category

Grant-in-Aid for Scientific Research (B)

Allocation TypeSingle-year Grants
Section一般
Review Section Basic Section 60070:Information security-related
Research InstitutionNational Institute of Advanced Industrial Science and Technology

Principal Investigator

Murakami Takao  国立研究開発法人産業技術総合研究所, 情報・人間工学領域, 主任研究員 (80587981)

Co-Investigator(Kenkyū-buntansha) 日野 英逸  統計数理研究所, モデリング研究系, 教授 (10580079)
清 雄一  電気通信大学, 大学院情報理工学研究科, 准教授 (20700157)
松田 隆宏  国立研究開発法人産業技術総合研究所, 情報・人間工学領域, 主任研究員 (60709492)
川本 裕輔  国立研究開発法人産業技術総合研究所, 情報・人間工学領域, 主任研究員 (60760006)
Project Period (FY) 2019-04-01 – 2022-03-31
Project Status Completed (Fiscal Year 2021)
Budget Amount *help
¥17,290,000 (Direct Cost: ¥13,300,000、Indirect Cost: ¥3,990,000)
Fiscal Year 2021: ¥5,460,000 (Direct Cost: ¥4,200,000、Indirect Cost: ¥1,260,000)
Fiscal Year 2020: ¥5,460,000 (Direct Cost: ¥4,200,000、Indirect Cost: ¥1,260,000)
Fiscal Year 2019: ¥6,370,000 (Direct Cost: ¥4,900,000、Indirect Cost: ¥1,470,000)
Keywords局所型プライバシー / 差分プライバシー / 時系列データ / グラフデータ / 安全性指標 / プライバシー / グラフ / 位置情報 / パーソナルデータ / 位置情報プライバシー / 局所型差分プライバシー / メンバーシップ推定 / トレース / 人工データ生成法
Outline of Research at the Start

本研究では,長期間にわたる時系列データ(位置情報,電力使用量など)や数多くの属性データ(年齢,結婚状況,収入など)といった大規模なパーソナルデータに対して,ユーザ自身が加工を施してサービス事業者に送信する局所型プライバシー保護技術を確立する.本技術は,長期間にわたる時系列データや数多くの属性データに対し,サービス事業者からの情報漏洩の可能性まで考慮して安全性を保証するものであり,これによりパーソナルデータの利活用促進を加速させる.

Outline of Final Research Achievements

In this work, we studied locally private algorithms for large-scale personal data, such as time-series data (e.g., location traces) and social graph data. Specifically, we proposed a locally private algorithm based on LSH (Locality Sensitive Hashing), a location trace synthesizer, and graph LDP (Local Differential Privacy) algorithms with utility guarantees. We also proposed a privacy notion called ULDP (Utility-Optimized LDP), which provides privacy guarantees equivalent to LDP for only sensitive data.

Academic Significance and Societal Importance of the Research Achievements

従来の局所型プライバシー保護技術のほとんどは,各データが独立であると仮定しており,長期間にわたる時系列データ(位置情報など)やソーシャルグラフデータのような相関を持ったパーソナルデータには適用できない.本研究での成果は,このようなデータに対しても安全性や有用性の理論的保証を与え,ユーザにプライバシーの観点での安心感を与えつつ,パーソナルデータの利活用促進を加速させることが可能となる.

Report

(4 results)
  • 2021 Annual Research Report   Final Research Report ( PDF )
  • 2020 Annual Research Report
  • 2019 Annual Research Report
  • Research Products

    (11 results)

All 2021 2019 Other

All Int'l Joint Research (1 results) Journal Article (1 results) (of which Peer Reviewed: 1 results,  Open Access: 1 results) Presentation (8 results) (of which Int'l Joint Research: 5 results,  Invited: 2 results) Remarks (1 results)

  • [Int'l Joint Research] UC San Diego(米国)

    • Related Report
      2021 Annual Research Report
  • [Journal Article] Privacy-Preserving Multiple Tensor Factorization for Synthesizing Large-Scale Location Traces with Cluster-Specific Features2021

    • Author(s)
      Takao Murakami, Koki Hamada, Yusuke Kawamoto, Takuma Hatano
    • Journal Title

      Proceedings on Privacy Enhancing Technologies (PoPETs)

      Volume: 2 Pages: 5-26

    • Related Report
      2020 Annual Research Report
    • Peer Reviewed / Open Access
  • [Presentation] Communication-Efficient Triangle Counting under Local Differential Privacy2021

    • Author(s)
      Jacob Imola*, Takao Murakami*, Kamalika Chaudhuri (*: equal contributions)
    • Organizer
      Proceedings of the 31th USENIX Security Symposium (USENIX Security 2022)
    • Related Report
      2021 Annual Research Report
    • Int'l Joint Research
  • [Presentation] Locality Sensitive Hashing with Extended Differential Privacy2021

    • Author(s)
      Natasha Fernandes*, Yusuke Kawamoto*, Takao Murakami* (*: equal contribution)
    • Organizer
      Proceedings of the 26th European Symposium on Research in Computer Security (ESORICS 2021)
    • Related Report
      2021 Annual Research Report
    • Int'l Joint Research
  • [Presentation] Toward Accurate Data Analysis under Local Privacy2021

    • Author(s)
      Takao Murakami
    • Organizer
      The 16th International Workshop on Security (IWSEC 2021), Keynote Talk, 2021.
    • Related Report
      2021 Annual Research Report
    • Int'l Joint Research / Invited
  • [Presentation] Locally Differentially Private Analysis of Graph Statistics2021

    • Author(s)
      Jacob Imola*, Takao Murakami*, Kamalika Chaudhuri (*: equal contributions)
    • Organizer
      Proceedings of the 30th USENIX Security Symposium (USENIX Security 2021)
    • Related Report
      2020 Annual Research Report
    • Int'l Joint Research
  • [Presentation] Locally Differentially Private Analysis of Graph Statistics2021

    • Author(s)
      Takao Murakami
    • Organizer
      IT-Security & Privacy Colloquium, the University of Luebeck
    • Related Report
      2020 Annual Research Report
    • Invited
  • [Presentation] TransMIA: Membership Inference Attacks Using Transfer Shadow Training2021

    • Author(s)
      Seira Hidano, Takao Murakami, Yusuke Kawamoto
    • Organizer
      Proceedings of the 2021 International Joint Conference on Neural Networks (IJCNN 2021)
    • Related Report
      2020 Annual Research Report
    • Int'l Joint Research
  • [Presentation] PWS Cup 2019: ID識別・トレース推定に強い位置情報の匿名加工技術を競う2019

    • Author(s)
      村上隆夫,荒井ひろみ,井口誠 ,小栗秀暢,菊池浩明,黒政敦史,中川裕志,中村優一,西山賢志郎,野島良,波多野卓磨,濱田浩気,山岡裕司,山口高康,山田明,渡辺知恵美
    • Organizer
      コンピュータセキュリティシンポジウム2019(CSS 2019)
    • Related Report
      2019 Annual Research Report
  • [Presentation] Privacy-Preserving Multiple Tensor Factorization for Synthesizing Large-Scale Location Traces2019

    • Author(s)
      Takao Murakami, Koki Hamada, Yusuke Kawamoto, Takuma Hatano
    • Organizer
      arXiv:1911.04226
    • Related Report
      2019 Annual Research Report
  • [Remarks] Designing a Location Trace Anonymization Contest

    • URL

      https://arxiv.org/abs/2107.10407

    • Related Report
      2021 Annual Research Report

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Published: 2019-04-18   Modified: 2023-01-30  

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