Privacy-preserving logistic regression

By Kamalika Chaudhuri, Claire Monteleoni. 2008

687 citations recorded by the source corpus.

Subfields: Safety, Robustness & Interpretability

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Citations in the corpus

Extracting Training Data from Large Language Models (2020, methodology): “An approach to minimizing memorization of training data is to apply differentially-private training techniques [1,9,43,60,64].”
The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks (2018, methodology): “y to distinguish between the case that a model was trained with or without a given secret in the training data. Differential privacy has been applied to several classes of machine learning algorithms [6], including neural networks [1]. In Section 8.3, empirically analyze the privacy gained by traini”
Membership Inference Attacks Against Machine Learning Models (2016, background): “Differential privacy [12] has been applied to linear and logistic regression [8], [37], support vector machines [28], risk minimization [5], [9], [34], deep learning [1], [30], learning an unknown probability distribution over a discrete population from random samples [10], and releasing hyper-param”
Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data (2016, neutral): “Attempts to provide differential privacy for machine learning models led to a series of efforts on shallow machine learning models, including work by Bassily et al. (2014); Chaudhuri & Monteleoni (2009); Pathak et al. (2011); Song et al. (2013), and Wainwright et al. (2012).”
Privacy in Deep Learning: A Survey (2020, methodology): “In the table, the following abbreviations have been used: ERM for Empirical Risk Minimization, GM for Generative Model, AE for Auto Encoder, LIR for Linear Regression, LOR for Logistic Regression, LM for Linear Means, FLD for Fisher’s Linear Discriminant, NB for Naive Bayes and RF for Random Forest.”
AdaCliP: Adaptive Clipping for Private SGD (2019, methodology): “Diﬀerential privacy for machine learning has been studied in various models including models with convex objectives [10, 11, 12] and more recently deep learning methods [13, 14, 15].”
Learning Privately over Distributed Features: An ADMM Sharing Approach (2019, background): “Chaudhuri and Monteleoni (2009) is one of the ﬁrst studies combing machine learning and differential privacy (DP), focusing on logistic regression.”
Federated Machine Learning (2019, neutral): “s require participants’ data to be secretly-shared among non-colluding servers. DifferentialPrivacy.AnotherlineofworkusetechniquesDifferentialPrivacy[18]ork-Anonymity [63] for data privacy protection [1, 12, 42, 61]. The methods of differential privacy, k-anonymity, and diversification [3] involve i”
Privacy Amplification by Iteration (2018, background): “The field of differentially private convex optimization spans almost a decade [15]–[27].”
Differentially-Private "Draw and Discard" Machine Learning (2018, background): “ML in the Trusted-Curator Model: Most prior work for differentially private machine learning assumes a trusted-curator model, where the data is ﬁrst collected by the company and only then a privacy-preserving computation is run on it [1], [48], [42], [9].”
Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption (2017, methodology): “ctions are the same regardeless of the presence or absence of an individual example in the dataset. This is usually achieved by adding properly calibrated noise to an algorithm or to the data itself [Chaudhuri and Monteleoni, 2009, Duchi et al., 2013]. While computationally efﬁcient, these technique”
Extremal Mechanisms for Local Differential Privacy (2014, methodology): “The typical recipe is to propose a differentially private mechanism inspired by [11, 13, 26, 20], and then establish its near-optimality by comparing the achievable utility to a converse, for example in principal component analysis [8, 5, 19, 24], linear queries [21, 18], logistic regression [7] and”
The Value of Collaboration in Convex Machine Learning with Differential Privacy (2019, methodology): “In [18], a privacypreserving regularized logistic regression algorithm is provided for learning from private databases by bounding the sensitivity of regularized logistic regression, and perturbing the learned classifier with noise proportional to the sensitivity.”
Differential Privacy Preservation for Deep Auto-Encoders: an Application of Human Behavior Prediction (2016, methodology): “, collaborative recommendation (McSherry and Mironov 2009), logistic regression (Chaudhuri and Monteleoni 2008), publishing contingency tables (Xiao, Wang, and Gehrke 2010), and spectral graph analysis (Wang, Wu, and Wu 2013) in social network analysis.”
To Drop or Not to Drop: Robustness, Consistency and Differential Privacy Properties of Dropout (2015, background): “In the last few years, design of differentially private optimization (learning) algorithms have received signiﬁcant attention Chaudhuri and Monteleoni [2008], Dwork and Lei [2009], Chaudhuri et al. [2011], Jain et al. [2012], Kifer et al. [2012], Duchi et al. [2013], Song et al. [2013], Jain and…”
Functional Mechanism: Regression Analysis under Differential Privacy (2012, methodology): “Note that, unlike previous work [4,5] which relies on some special properties of the objective function, our functional mechanism generally applies to all forms of optimization functions.”