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 61 articles , 1 standard article )

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  1. Alvim, Mário S.; Chatzikokolakis, Konstantinos; McIver, Annabelle; Morgan, Carroll; Palamidessi, Catuscia; Smith, Geoffrey: An axiomatization of information flow measures (2019)
  2. 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)
  3. McIver, A. K.; Morgan, C. C.; Rabehaja, T.: Program algebra for quantitative information flow (2019)
  4. Yang, Jing; Li, Xiaoye; Sun, Zhenlong; Zhang, Jianpei: A differential privacy framework for collaborative filtering (2019)
  5. 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)
  6. Benedikt, Michael; Grau, Bernardo Cuenca; Kostylev, Egor V.: Logical foundations of information disclosure in ontology-based data integration (2018)
  7. Bringmann, Karl; Friedrich, Tobias; Krohmer, Anton: De-anonymization of heterogeneous random graphs in quasilinear time (2018)
  8. Katewa, Vaibhav; Pasqualetti, Fabio; Gupta, Vijay: On privacy vs. cooperation in multi-agent systems (2018)
  9. Lu, Yang; Zhu, Minghui: Privacy preserving distributed optimization using homomorphic encryption (2018)
  10. Reineke, Jan; Salinger, Alejandro: On the smoothness of paging algorithms (2018)
  11. Rinott, Yosef; O’Keefe, Christine M.; Shlomo, Natalie; Skinner, Chris: Confidentiality and differential privacy in the dissemination of frequency tables (2018)
  12. Wu, Yi-Chin; Raman, Vasumathi; Rawlings, Blake C.; Lafortune, Stéphane; Seshia, Sanjit A.: Synthesis of obfuscation policies to ensure privacy and utility (2018)
  13. Gursoy, Mehmet Emre; Inan, Ali; Nergiz, Mehmet Ercan; Saygin, Yucel: Differentially private nearest neighbor classification (2017)
  14. Hamm, Jihun: Minimax filter: learning to preserve privacy from inference attacks (2017)
  15. Huang, Cheng; Lu, Rongxing; Choo, Kim-Kwang Raymond: Secure and flexible cloud-assisted association rule mining over horizontally partitioned databases (2017)
  16. Li, Meng; Zhu, Liehuang; Zhang, Zijian; Xu, Rixin: Achieving differential privacy of trajectory data publishing in participatory sensing (2017)
  17. McIver, A. K.; Morgan, C. C.; Rabehaja, T.: Algebra for quantitative information flow (2017)
  18. Nie, Weilin; Wang, Cheng: Perturbation of convex risk minimization and its application in differential private learning algorithms (2017)
  19. Nozari, Erfan; Tallapragada, Pavankumar; Cortés, Jorge: Differentially private average consensus: obstructions, trade-offs, and optimal algorithm design (2017)
  20. Yang, Jiannan; Cao, Yongzhi; Wang, Hanpin: Differential privacy in probabilistic systems (2017)

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