Concepedia

Differentially Private Linear Regression

Differentially Private Linear Regression is a regression analysis method that belongs to the class of differentially private machine learning techniques, distinguished by its incorporation of calibrated noise into the estimation process to ensure that the output model does not reveal sensitive information about individual data points in the training dataset.

23

Publications

750

Citations

74

Authors

33

Institutions

Publications per year

2017–2026

14

Parents

Publications

23

Differentially Private Feature Selection via Stability Arguments, and the Robustness of the Lasso

Abhradeep Thakurta, Adam Smith · Conference on Learning Theory · 2013 · 112 citations

, , +16

Secure Linear Regression on Vertically Partitioned Datasets.

Adrià Gascón, Phillipp Schoppmann, Borja Balle et al. · IACR Cryptology ePrint Archive · 2016 · 99 citations

, , +14

Nearly-optimal private LASSO

Kunal Talwar, Abhradeep Thakurta, Li Zhang · Neural Information Processing Systems · 2015 · 94 citations

, , +15

Secure and Differentially Private Logistic Regression for Horizontally Distributed Data

Miran Kim, Junghye Lee, Lucila Ohno‐Machado et al. · IEEE Transactions on Information Forensics and Security · 2019 · 76 citations · Full text

, , +17

Differentially Private Significance Tests for Regression Coefficients

Andrés F. Barrientos, Jerome P. Reiter, Ashwin Machanavajjhala et al. · Journal of Computational and Graphical Statistics · 2018 · 38 citations

, , +16

1–5 of 23

Authors

74

  1. AS
    Adam Smith

    Pennsylvania State University, United States

    3 publications · 149 citations

    3

    h-index

  2. AT
    Abhradeep Thakurta

    Google (United States), United States

    2 publications · 206 citations

    2

    h-index

  3. JP
    Jerome P. Reiter

    Duke University, United States

    2 publications · 62 citations

    2

    h-index

  4. AM
    Ashwin Machanavajjhala

    Duke University, United States

    2 publications · 62 citations

    2

    h-index

  5. OS
    Or Sheffet

    Carnegie Mellon University, United States

    2 publications · 30 citations

    2

    h-index

1–5 of 74

Institutions

33

  1. Duke University

    Durham, United States

    2 h-index

    8 publications

    248 citations

  2. Xidian University

    Xi'an, China

    2 h-index

    7 publications

    124 citations

  3. Harvard University

    Cambridge, United States

    2 h-index

    11 publications

    146 citations

  4. Carnegie Mellon University

    Pittsburgh, United States

    2 h-index

    3 publications

    79 citations

  5. Stony Brook University

    Stony Brook, United States

    1 h-index

    1 publications

    16 citations

  6. Zhongnan University of Economics and Law

    Wuhan, China

    1 h-index

    2 publications

    26 citations

  7. Yahoo (United Kingdom)

    London, United Kingdom

    1 h-index

    1 publications

    25 citations

  8. Northeastern University

    Boston, United States

    1 h-index

    4 publications

    52 citations

1–8 of 33

Venues

  1. arXiv (Cornell University)

    Repository · 6 publications · 128 citations

    6

    h-index

  2. Information Sciences

    2 publications · 33 citations

    2

    h-index

  3. Transactions on data privacy

    1 publications · 20 citations

    1

    h-index

  4. IEICE Transactions on Information and Systems

    1 publications · 17 citations

    1

    h-index

  5. Algorithmic Learning Theory

    Conference · 1 publications · 12 citations

    1

    h-index

1–5