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.

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  1. Laeuchli, Jesse; Ramírez-Cruz, Yunior; Trujillo-Rasua, Rolando: Analysis of centrality measures under differential privacy models (2022)
  2. Bassily, Raef; Nissim, Kobbi; Smith, Adam; Steinke, Thomas; Stemmer, Uri; Ullman, Jonathan: Algorithmic stability for adaptive data analysis (2021)
  3. Kroll, Martin: On density estimation at a fixed point under local differential privacy (2021)
  4. Pastore, Adriano; Gastpar, Michael: Locally differentially-private randomized response for discrete distribution learning (2021)
  5. Wu, Shuhui; Yu, Mengqing; Ahmed, Moushira Abdallah Mohamed; Qian, Yaguan; Tao, Yuanhong: FL-MAC-RDP: federated learning over multiple access channels with Rényi differential privacy (2021)
  6. Altafini, Claudio: A system-theoretic framework for privacy preservation in continuous-time multiagent dynamics (2020)
  7. Hoshino, Nobuaki: A firm foundation for statistical disclosure control (2020)
  8. Ito, Shinsuke; Terada, Masayuki: An evaluation of anonymization methods for creating detailed geographical data (2020)
  9. Liu, Xiao-Kang; Zhang, Ji-Feng; Wang, Jimin: Differentially private consensus algorithm for continuous-time heterogeneous multi-agent systems (2020)
  10. McGlinchey, Aisling; Mason, Oliver: Some novel aspects of the positive linear observer problem: differential privacy and optimal (l_1) sensitivity (2020)
  11. Zhou, Yaqin; Tang, Shaojie: Differentially private distributed learning (2020)
  12. Alvim, Mário S.; Chatzikokolakis, Konstantinos; McIver, Annabelle; Morgan, Carroll; Palamidessi, Catuscia; Smith, Geoffrey: An axiomatization of information flow measures (2019)
  13. 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)
  14. Guo, Taolin; Luo, Junzhou; Dong, Kai; Yang, Ming: Locally differentially private item-based collaborative filtering (2019)
  15. Li, Xiaoguang; Li, Hui; Zhu, Hui; Huang, Muyang: The optimal upper bound of the number of queries for Laplace mechanism under differential privacy (2019)
  16. McIver, A. K.; Morgan, C. C.; Rabehaja, T.: Program algebra for quantitative information flow (2019)
  17. 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)
  18. Vo-Huu, Triet Dang; Blass, Erik-Oliver; Noubir, Guevara: EPiC: efficient privacy-preserving counting for MapReduce (2019)
  19. Yang, Jing; Li, Xiaoye; Sun, Zhenlong; Zhang, Jianpei: A differential privacy framework for collaborative filtering (2019)
  20. 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)

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