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When collecting information, local differential privacy (LDP) relieves the concern of privacy leakage from users' perspective, as user's private information is randomized before sent to the aggregator.
Randomized response: A survey technique for eliminating evasive answer bias
S. L. Warner · 1965
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Some properties of adding a smoothing step to the em algorithm
D. Nychka · 1990
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A smoothed em approach to indirect estimation problems, with particular reference to stereology and emission tomography
B. Silverman, M. Jones, J. Wilson, and D. Nychka · 1990
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A gentle tutorial of the em algorithm and its application to parameter estimation for gaussian mixture and hidden markov models
J. A. Bilmes et al · 1998
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Averaging, maximum penalized likelihood and bayesian estimation for improving gaussian mixture probability density estimates
D. Ormoneit and V. Tresp · 1998
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Calibrating noise to sensitivity in private data analysis
C. Dwork, F. McSherry, K. Nissim, and A. Smith · 2006
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Smoothing of, and parameter estimation from, noisy biophysical recordings
Q. J. Huys and L. Paninski · 2009
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Boosting the accuracy of differentially private histograms through consistency
M. Hay, V. Rastogi, G. Miklau, and D. Suciu · 2010
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Differential privacy via wavelet transforms
X. Xiao, G. Wang, and J. Gehrke · 2010
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Distributed optimization and statistical learning via the alternating direction method of multipliers
S. Boyd, N. Parikh, E. Chu, B. Peleato, J. Eckstein, et al · 2011
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Proximal splitting methods in signal processing
P. L. Combettes and J.-C. Pesquet · 2011
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Local-em and the ems algorithm
C.-P. S. Fan, J. Stafford, and P. E. Brown · 2011
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Understanding hierarchical methods for differentially private histograms
W. H. Qardaji, W. Yang, and N. Li · 2013
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RAPPOR: randomized aggregatable privacy-preserving ordinal response
Ú. Erlingsson, V. Pihur, and A. Korolova · 2014
Cited alongside, same era.
Local, private, efficient protocols for succinct histograms
R. Bassily and A. D. Smith · 2015
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Maximum likelihood postprocessing for differential privacy under consistency constraints
J. Lee, Y. Wang, and D. Kifer · 2015
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Building a RAPPOR with the unknown: Privacy-preserving learning of associations and data dictionaries
G. C. Fanti, V. Pihur, and Ú. Erlingsson · 2016
Cited alongside, same era.
Discrete distribution estimation under local privacy
P. Kairouz, K. Bonawitz, and D. Ramage · 2016
Cited alongside, same era.
Practical locally private heavy hitters
R. Bassily, K. Nissim, U. Stemmer, and A. G. Thakurta · 2017
Lopub: High-dimensional crowdsourced data publication with local differential privacy
X. Ren, C. Yu, W. Yu, S. Yang, X. Yang, J. A. McCann, and P. S. Yu · 2018
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Tlc trip record data
N. Taxi and L. Commission · 2018
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Privtrie: Effective frequent term discovery under local differential privacy
N. Wang, X. Xiao, Y. Yang, T. D. Hoang, H. Shin, J. Shin, and G. Yu · 2018
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Locally differentially private frequent itemset mining
T. Wang, N. Li, and S. Jha · 2018
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Optimal schemes for discrete distribution estimation under locally differential privacy
M. Ye and A. Barg · 2018
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Linear queries estimation with local differential privacy
R. Bassily · 2019
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Cited alongside, same era.
Collecting telemetry data privately
B. Ding, J. Kulkarni, and S. Yekhanin · 2017
Cited alongside, same era.
Learning with privacy at scale, 2017
A. D. P. Team · 2017
Cited alongside, same era.
Local private ordinal data distribution estimation
S. Wang, Y. Nie, P. Wang, H. Xu, W. Yang, and L. Huang · 2017
Cited alongside, same era.
Locally differentially private protocols for frequency estimation
T. Wang, J. Blocki, N. Li, and S. Jha · 2017
Cited alongside, same era.
Hadamard response: Estimating distributions privately, efficiently, and with little communication
J. Acharya, Z. Sun, and H. Zhang · 2018
Cited alongside, same era.
Minimax optimal procedures for locally private estimation
J. C. Duchi, M. I. Jordan, and M. J. Wainwright · 2018
Cited alongside, same era.
Calibrate: Frequency estimation and heavy hitter identification with local differential privacy via incorporating prior knowledge
J. Jia and N. Z. Gong · 2019
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Answering range queries under local differential privacy
T. Kulkarni, G. Cormode, and D. Srivastava · 2019
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Graphical-model based estimation and inference for differential privacy
R. McKenna, D. Sheldon, and G. Miklau · 2019
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Integrated public use microdata series: Version 9.0 [database], 2019
S. Ruggles, S. Flood, R. Goeken, J. Grover, E. Meyer, J. Pacas, and M. Sobek · 2019
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Answering multi-dimensional analytical queries under local differential privacy
T. Wang, B. Ding, J. Zhou, C. Hong, Z. Huang, N. Li, and S. Jha · 2019
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Consistent and accurate frequency oracles under local differential privacy
T. Wang, Z. Li, N. Li, M. Lopuhaä-Zwakenberg, and B. Skoric · 2019
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