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 55 articles , 1 standard article )

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  1. 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)
  2. Benedikt, Michael; Grau, Bernardo Cuenca; Kostylev, Egor V.: Logical foundations of information disclosure in ontology-based data integration (2018)
  3. Bringmann, Karl; Friedrich, Tobias; Krohmer, Anton: De-anonymization of heterogeneous random graphs in quasilinear time (2018)
  4. Lu, Yang; Zhu, Minghui: Privacy preserving distributed optimization using homomorphic encryption (2018)
  5. Reineke, Jan; Salinger, Alejandro: On the smoothness of paging algorithms (2018)
  6. Rinott, Yosef; O’Keefe, Christine M.; Shlomo, Natalie; Skinner, Chris: Confidentiality and differential privacy in the dissemination of frequency tables (2018)
  7. Wu, Yi-Chin; Raman, Vasumathi; Rawlings, Blake C.; Lafortune, Stéphane; Seshia, Sanjit A.: Synthesis of obfuscation policies to ensure privacy and utility (2018)
  8. Hamm, Jihun: Minimax filter: learning to preserve privacy from inference attacks (2017)
  9. Huang, Cheng; Lu, Rongxing; Choo, Kim-Kwang Raymond: Secure and flexible cloud-assisted association rule mining over horizontally partitioned databases (2017)
  10. McIver, A. K.; Morgan, C. C.; Rabehaja, T.: Algebra for quantitative information flow (2017)
  11. Nie, Weilin; Wang, Cheng: Perturbation of convex risk minimization and its application in differential private learning algorithms (2017)
  12. Nozari, Erfan; Tallapragada, Pavankumar; Cortés, Jorge: Differentially private average consensus: obstructions, trade-offs, and optimal algorithm design (2017)
  13. Sadhya, Debanjan; Singh, Sanjay Kumar: Privacy risks ensuing from cross-matching among databases: a case study for soft biometrics (2017)
  14. Yang, Jiannan; Cao, Yongzhi; Wang, Hanpin: Differential privacy in probabilistic systems (2017)
  15. Barthe, Gilles; Crespo, Juan Manuel; Kunz, César: Product programs and relational program logics (2016)
  16. Derbeko, Philip; Dolev, Shlomi; Gudes, Ehud; Sharma, Shantanu: Security and privacy aspects in MapReduce on clouds: a survey (2016)
  17. Gazeau, Ivan; Miller, Dale; Palamidessi, Catuscia: Preserving differential privacy under finite-precision semantics (2016)
  18. Kairouz, Peter; Oh, Sewoong; Viswanath, Pramod: Extremal mechanisms for local differential privacy (2016)
  19. Wang, Ke; Wang, Peng; Fu, Ada Waichee; Wong, Raymond Chi-Wing: Generalized bucketization scheme for flexible privacy settings (2016)
  20. Wang, Weina; Ying, Lei; Zhang, Junshan: Buying data from privacy-aware individuals: the effect of negative payments (2016)

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