Differentially Private M-Estimators

By Jing Lei. 2011

122 citations recorded by the source corpus.

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In Privacy & Security

Citations in the corpus

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”
Privacy in Pharmacogenetics: An End-to-End Case Study of Personalized Warfarin Dosing (2014, positive): “In order to select the number of bins, we use a heuristic given by Lei [32] and suggested by Vinterbo [44], which says that when numeric attributes are scaled to the interval [0, 1], the bin width is given by (log(n)/n) 1/(d+1) , where n = |D| and d is the dimension of D.”
Differential Privacy Preservation for Deep Auto-Encoders: an Application of Human Behavior Prediction (2016, methodology): “Nevertheless, even when we use all tuples in the dataset, the accuracies of FM, FP, and DPME are still lower than that of dPAH, dA, and TdA.”
Functional Mechanism: Regression Analysis under Differential Privacy (2012, methodology): “−∞ ∞ Lei [16] proposes a regression method that avoids conducting sensitivity analysis directly on the regression outputs.”
Local Privacy and Minimax Bounds: Sharp Rates for Probability Estimation (2013, methodology): “Lei [19] considers private M -estimators based on first performing a histogram density estimate, then using this to construct a second estimator; his estimator is not locally private, and the resulting M -estimators have sub-optimal convergence rates.”
Learning with Differential Privacy: Stability, Learnability and the Sufficiency and Necessity of ERM Principle (2015, background): “…and hence fits naturally in the general framework of statistical machine learning, e.g., see (Dwork & Lei, 2009; Wasserman & Zhou, 2010; Smith, 2011; Lei, 2011; Wang et al., 2015), as well as applications involving regression (Chaudhuri et al., 2011; Thakurta & Smith, 2013) and GWAS data (Yu et…”
Differential Privacy and Machine Learning: a Survey and Review (2014, methodology): “Some other papers [14, 50, 33, 40, 21, 31, 7, 27] use the distance of the parameters in private and non-private models when the models are parametric and have the same parameters.”
Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting (2017, background): “Although there has been a significant amount of work aimed at developing machine learning algorithms that satisfy definitions such as differential privacy [8], [10], [11], [12], [13], [14], the factors that bring about specific types of privacy risk in applications of standard machine learning algor”
Collect at Once, Use Effectively: Making Non-interactive Locally Private Learning Possible (2017, background): “Since then, there has been extensive literature studying the fundamental trade-oﬀs between diﬀerential privacy and accuracy for query answering [16, 15, 34, 40], machine learning [4, 5, 30, 39], and statistical inference [27, 31].”
Preserving differential privacy in convolutional deep belief networks (2017, methodology): “The dPAH model outperforms general methods for regression analysis under -diﬀerential privacy, i.e., functional mechanism [76], DPME [42], and ﬁlter-priority [18].”
Differentially private model selection with penalized and constrained likelihood (2016, methodology): “On the statistics side, research efforts include designing consistent and efficient differentially private point estimators (Dwork and Lei, 2009; Smith, 2011; Chaudhuri et al., 2011; Lei, 2011; Bassily et al., 2014; Karwa and Slavković, 2016), non-parametric density estimation (Wasserman and Zhou, 2”
The optimal mechanism in differential privacy (2012, unknown): “, [10], [11], [12], [13], [14], [5], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40], [41], [42], [43], [44], [45], [46].”
Convergence Rates for Differentially Private Statistical Estimation (2012, background): “Lei (2011) considers M-estimation, and provides a simple and elegant diﬀerentially-private M-estimator which is statistically consistent.”
On the Power of Multiple Anonymous Messages (2019, unknown): “This notion has been studied in previous work on DP including by [73,42] (for more on related work, see Section 6 below).”
Differentially Private Assouad, Fano, and Le Cam (2020, background): “Previous literature has studied various problems and established utility privacy trade-oﬀ bounds, including distribution estimation, hypothesis testing, property estimation, empirical risk minimization, etc [CMS11, Lei11, BST14, DHS15, CDK17, ASZ18, KLSU19, ADR18, AKSZ18].”
The Unintended Consequences of Overfitting: Training Data Inference Attacks (2017, background): “…there has been a signiﬁcant amount of work aimed at developing machine learning algorithms that satisfy deﬁnitions such as diﬀerential privacy [8, 10, 11, 12, 13, 14], the factors that bring about spe-ciﬁc types of privacy risk in applications of standard machine learning algorithms are not well…”