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로컬 차분 프라이버시 실제 적용 사례연구 : 프라이버시 보존형 설문조사

정수용 홍도원 서창호 2020년

논문상세정보
    • 저자 정수용 홍도원 서창호
    • 제어번호 106588523
    • 학술지명 정보보호학회논문지
    • 권호사항 Vol. 30 No. 1 [ 2020 ]
    • 발행처 한국정보보호학회
    • 발행처 URL http://www.kiisc.or.kr/
    • 자료유형 학술저널
    • 수록면 141-156
    • 언어 Korean
    • 출판년도 2020
    • 등재정보 KCI등재
    • 소장기관 숭실대학교 중앙도서관 홍익대학교 중앙도서관
    • 판매처
    유사주제 논문( 0)
인용/피인용

' 로컬 차분 프라이버시 실제 적용 사례연구 : 프라이버시 보존형 설문조사' 의 참고문헌

  • Utility-Optimized Local Differential Privacy Mechanisms for Distribution Estimation
    T. Murakami [2019]
  • The Presentation of a Web Survey, Nonresponse and Measurement Error among Members of Web Panel
  • The Effects of Appeals, Anonymity, and Feedback on Main Survey Response Patterns from Salespeople
    P. K. Tyagi [1989]
  • The Algorithmic Foundations of Differential Privacy
    C. Dwork [2014]
  • Regression Shrinkage and Selection via the Lasso
  • Randomized Response :A Survey Technique for Eliminating Evasive Answer Bias
  • RAPPOR : Randomized Aggregatable Privacy-Preserving Ordinal Response
  • Perspectives of Online Survey in Dermatology
    A. E. Arafa [2019]
  • Minimax Optimal Procedures for Locally Private Estimation
    J. C. Duchi [2018]
  • Marginal Release Under Local Differential Privacy
    G. Cormode [2018]
  • Local differential privacy for social network publishing
    Peng Liu [2020]
  • Local Differential Private Data Aggregation for Discrete Distribution Estimation
    S. Wang [2019]
  • LoPub :High-Dimensional Crowdsourced Data Publication With Local Differential Privacy
    X. Ren [2018]
  • Learning with Privacy at Scale
  • LDPard: Effective Location-Record Data Publication via Local Differential Privacy
    Z. Zhao [2019]
  • Introduction to Algorithm
  • Heavy Hitters and the Structure of Local Privacy
    M. Bum [2018]
  • Heavy Hitter Estimation over Set-Valued Data with Local Differential Privacy
    Z. Qin [2016]
  • Doing Surveys Online
    V. Toepoel [2015]
  • Differential Privacy under Continual Observation
    C. Dwork [2010]
  • Collecting Telemetry Data Privately
    B. Ding [2017]
  • Calibrating Noise to Sensitivity in Private Data Analysis
    C. Dwork [2006]
  • Calibrate:Frequency Estimation and Heavy Hitter Identification with Local Differential Privacy via Incorporating Prior Knowledge
    J. Jia [2019]
  • A Study on the Measurement Methods and Cases of Personal Information Leakage Risks of Private Companies
    G. H. Lee [2008]