Locally differentially-private distribution estimation

By Adriano Pastore, Michael C. Gastpar. 2016

19 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering

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Citations in the corpus

Optimal Schemes for Discrete Distribution Estimation Under Locally Differential Privacy (2017, background): “Existing Results The following two privatization schemes are the most well-known in the literature: the k-ary Randomized Aggregatable Privacy-Preserving Ordinal Response (k-RAPPOR) scheme [8], [9], and the k-ary Randomized Response (k-RR) scheme [10]–[12].”
Collecting and Analyzing Multidimensional Data with Local Differential Privacy (2019, background): “Various data analytics and machine learning problems have been studied under LDP, such as probability distribution estimation [15], [23], [30], [32], [42], heavy hitter discovery [4], [7], [33], [39], frequent new term discovery [37], frequency estimation [5], [38], frequent itemset mining [40], mar”
Local Differential Private Data Aggregation for Discrete Distribution Estimation (2019, background): “mechanisms, such as the multivariate randomized response [16], [32], [33], [34], [35] for multiple options, the binary randomized response [12], [15] on bitmaps, randomized 1-bit”
Optimal schemes for discrete distribution estimation under local differential privacy (2017, background): “Existing results: The following two privatization schemes are the most well-known in the literature: the k-ary Randomized Aggregatable Privacy-Preserving Ordinal Response (kRAPPOR) scheme [8], and the k-ary Randomized Response (k-RR) scheme [9]–[11].”
Local Private Hypothesis Testing: Chi-Square Tests (2017, methodology): “We then define local differential privacy, formalized by Raskhodnikova et al. (2008) and Dwork and Roth (2014), which does not require the subjects to release their raw data, rather each data entry is perturbed to prevent the true entry from being stored.”
Differentially Private Testing of Identity and Closeness of Discrete Distributions (2017, background): “There has been a line of work analyzing various statistical testing and estimation tasks under the notion of local diﬀerential privacy [WJD12, DJW13, EPK14, PG16, KBR16, YB17].”
Toward Distribution Estimation under Local Differential Privacy with Small Samples (2018, background): “, [3, 17, 25, 30, 31, 37, 40, 44]), mainly from the perspective of privacy metrics, obfuscation mechanisms, and statistical inference methods.”