PrivateLR

PrivateLR: Differentially Private Regularized Logistic Regression. PrivateLR implements two differentially private algorithms for estimating L2-regularized logistic regression coefficients. A randomized algorithm F is epsilon-differentially private (C. Dwork, Differential Privacy, ICALP 2006), if |log(P(F(D) in S)) - log(P(F(D’) in S))| <= epsilon for any pair D, D’ of datasets that differ in exactly one element, any set S, and the randomness is taken over the choices F makes.


References in zbMATH (referenced in 79 articles , 1 standard article )

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  1. Bassily, Raef; Nissim, Kobbi; Smith, Adam; Steinke, Thomas; Stemmer, Uri; Ullman, Jonathan: Algorithmic stability for adaptive data analysis (2021)
  2. Kroll, Martin: On density estimation at a fixed point under local differential privacy (2021)
  3. Altafini, Claudio: A system-theoretic framework for privacy preservation in continuous-time multiagent dynamics (2020)
  4. Hoshino, Nobuaki: A firm foundation for statistical disclosure control (2020)
  5. Ito, Shinsuke; Terada, Masayuki: An evaluation of anonymization methods for creating detailed geographical data (2020)
  6. Liu, Xiao-Kang; Zhang, Ji-Feng; Wang, Jimin: Differentially private consensus algorithm for continuous-time heterogeneous multi-agent systems (2020)
  7. McGlinchey, Aisling; Mason, Oliver: Some novel aspects of the positive linear observer problem: differential privacy and optimal (l_1) sensitivity (2020)
  8. Zhou, Yaqin; Tang, Shaojie: Differentially private distributed learning (2020)
  9. Alvim, Mário S.; Chatzikokolakis, Konstantinos; McIver, Annabelle; Morgan, Carroll; Palamidessi, Catuscia; Smith, Geoffrey: An axiomatization of information flow measures (2019)
  10. Chatterjee, Tanima; DasGupta, Bhaskar; Mobasheri, Nasim; Srinivasan, Venkatkumar; Yero, Ismael G.: On the computational complexities of three problems related to a privacy measure for large networks under active attack (2019)
  11. Guo, Taolin; Luo, Junzhou; Dong, Kai; Yang, Ming: Locally differentially private item-based collaborative filtering (2019)
  12. Li, Xiaoguang; Li, Hui; Zhu, Hui; Huang, Muyang: The optimal upper bound of the number of queries for Laplace mechanism under differential privacy (2019)
  13. McIver, A. K.; Morgan, C. C.; Rabehaja, T.: Program algebra for quantitative information flow (2019)
  14. Monedero, David Rebollo; Mezher, Ahmad Mohamad; Colomé, Xavier Casanova; Forné, Jordi; Soriano, Miguel: Efficient (k)-anonymous microaggregation of multivariate numerical data via principal component analysis (2019)
  15. Vo-Huu, Triet Dang; Blass, Erik-Oliver; Noubir, Guevara: EPiC: efficient privacy-preserving counting for MapReduce (2019)
  16. Yang, Jing; Li, Xiaoye; Sun, Zhenlong; Zhang, Jianpei: A differential privacy framework for collaborative filtering (2019)
  17. Barrientos, Andrés F.; Bolton, Alexander; Balmat, Tom; Reiter, Jerome P.; de Figueiredo, John M.; Machanavajjhala, Ashwin; Chen, Yan; Kneifel, Charley; DeLong, Mark: Providing access to confidential research data through synthesis and verification: an application to data on employees of the U.S. federal government (2018)
  18. Benedikt, Michael; Grau, Bernardo Cuenca; Kostylev, Egor V.: Logical foundations of information disclosure in ontology-based data integration (2018)
  19. Bringmann, Karl; Friedrich, Tobias; Krohmer, Anton: De-anonymization of heterogeneous random graphs in quasilinear time (2018)
  20. Katewa, Vaibhav; Pasqualetti, Fabio; Gupta, Vijay: On privacy vs. cooperation in multi-agent systems (2018)

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