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 46 articles , 1 standard article )

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  1. Wu, Yi-Chin; Raman, Vasumathi; Rawlings, Blake C.; Lafortune, Stéphane; Seshia, Sanjit A.: Synthesis of obfuscation policies to ensure privacy and utility (2018)
  2. Huang, Cheng; Lu, Rongxing; Choo, Kim-Kwang Raymond: Secure and flexible cloud-assisted association rule mining over horizontally partitioned databases (2017)
  3. McIver, A.K.; Morgan, C.C.; Rabehaja, T.: Algebra for quantitative information flow (2017)
  4. Nie, Weilin; Wang, Cheng: Perturbation of convex risk minimization and its application in differential private learning algorithms (2017)
  5. Nozari, Erfan; Tallapragada, Pavankumar; Cortés, Jorge: Differentially private average consensus: obstructions, trade-offs, and optimal algorithm design (2017)
  6. Sadhya, Debanjan; Singh, Sanjay Kumar: Privacy risks ensuing from cross-matching among databases: a case study for soft biometrics (2017)
  7. Yang, Jiannan; Cao, Yongzhi; Wang, Hanpin: Differential privacy in probabilistic systems (2017)
  8. Barthe, Gilles; Crespo, Juan Manuel; Kunz, César: Product programs and relational program logics (2016)
  9. Derbeko, Philip; Dolev, Shlomi; Gudes, Ehud; Sharma, Shantanu: Security and privacy aspects in MapReduce on clouds: a survey (2016)
  10. Gazeau, Ivan; Miller, Dale; Palamidessi, Catuscia: Preserving differential privacy under finite-precision semantics (2016)
  11. Kairouz, Peter; Oh, Sewoong; Viswanath, Pramod: Extremal mechanisms for local differential privacy (2016)
  12. Wang, Weina; Ying, Lei; Zhang, Junshan: Buying data from privacy-aware individuals: the effect of negative payments (2016)
  13. Yan, Shen; Pan, Shiran; Zhao, Yuhang; Zhu, Wen-Tao: Towards privacy-preserving data mining in online social networks: distance-grained and item-grained differential privacy (2016)
  14. Boreale, Michele; Pampaloni, Francesca: Quantitative information flow under generic leakage functions and adaptive adversaries (2015)
  15. Boreale, Michele; Paolini, Michela: Worst- and average-case privacy breaches in randomization mechanisms (2015)
  16. Brunel, Aloïs: Quantitative classical realizability (2015)
  17. ElSalamouny, Ehab; Chatzikokolakis, Konstantinos; Palamidessi, Catuscia: Generalized differential privacy: regions of priors that admit robust optimal mechanisms (2014)
  18. Giurgiu, Andrei; Guerraoui, Rachid; Huguenin, Kévin; Kermarrec, Anne-Marie: Computing in social networks (2014)
  19. Hajian, Sara; Domingo-Ferrer, Josep; Farràs, Oriol: Generalization-based privacy preservation and discrimination prevention in data publishing and mining (2014)
  20. Kifer, Daniel; Machanavajjhala, Ashwin: Pufferfish: a framework for mathematical privacy definitions (2014)

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