Optimal schemes for discrete distribution estimation under local differential privacy

By Min Ye, Alexander Barg. 2017

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Citations in the corpus

Breaking the Communication-Privacy-Accuracy Trilemma (2020, methodology): “If we further assume X are drawn from some discrete distribution p, then the problem falls into distribution estimation under local differential privacy [9], [13], [34], [36], [42], [43], [44], [45], [46] and limited communication [11], [25], [45], [46], [47], [48], [49], [50].”
Optimal Schemes for Discrete Distribution Estimation Under Locally Differential Privacy (2017, background): “This paper was presented in part at the 2017 International Symposium on Information Theory [1].”
Context-Aware Generative Adversarial Privacy (2017, background): “However, training machine learning models on randomized data with DP guarantees often leads to a signi cantly reduced utility and comes with a tremendous hit in sample complexity [18, 19, 20, 29, 37, 42, 43, 47, 64, 82, 87, 93, 94] in the desired leakage regimes.”
Asymptotically optimal private estimation under mean square loss (2017, background): “oof of Proposition V.12 37 Appendix I: Proof of (80) 38 Appendix J: Relation to Local Asymptotic Normality and Fisher information matrix 39 References 41 I. INTRODUCTION This paper continues our work [13]. The context of the problem that we consider is related to a major challenge in the statistical”
Generative Adversarial Privacy: A Data-Driven Approach to Information-Theoretic Privacy (2018, background): “For example, learning population level histograms under local DP suffers from a stupendous increase in sample complexity by a factor proportional to the size of the dictionary [6, 16, 34].”
Calibrate: Frequency Estimation and Heavy Hitter Identification with Local Differential Privacy via Incorporating Prior Knowledge (2018, unknown): “Local differential privacy (LDP) [27], [5], [20], [22], [30], [3], [12], [13], [34], [29], [23], [8], [24], [14], [6], [28], [32], [35], [1], a privacy protection mechanism based on differential privacy [15], can address both challenges.”