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Local Differential Privacy (LDP) protects user privacy from the data collector.
Minima of functions of several variables with inequalities as side constraints
W. Karush · 1939
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Randomized response: A survey technique for eliminating evasive answer bias
S. L. Warner · 1965
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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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Boosting the accuracy of differentially private histograms through consistency
M. Hay, V. Rastogi, G. Miklau, and D. Suciu · 2010
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Differentially private data cubes: optimizing noise sources and consistency
B. Ding, M. Winslett, J. Han, and Z. Li · 2011
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Local privacy and statistical minimax rates
J. C. Duchi, M. I. Jordan, and M. J. Wainwright · 2013
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RAPPOR: randomized aggregatable privacy-preserving ordinal response
Ú. Erlingsson, V. Pihur, and A. Korolova · 2014
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Nonlinear programming
H. W. Kuhn and A. W. Tucker · 2014
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Priview: practical differentially private release of marginal contingency tables
W. Qardaji, W. Yang, and N. Li · 2014
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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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Discrete distribution estimation under local privacy
P. Kairouz, K. Bonawitz, and D. Ramage · 2016
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Heavy hitter estimation over set-valued data with local differential privacy
Z. Qin, Y. Yang, T. Yu, I. Khalil, X. Xiao, and K. Ren · 2016
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Mutual information optimally local private discrete distribution estimation
S. Wang, L. Huang, P. Wang, Y. Nie, H. Xu, W. Yang, X. Li, and C. Qiao · 2016
Cited alongside, same era.
Apple differential privacy team, learning with privacy at scale, 2017
2017
Cited alongside, same era.
Practical locally private heavy hitters
R. Bassily, K. Nissim, U. Stemmer, and A. G. Thakurta · 2017
Cited alongside, same era.
Collecting telemetry data privately
B. Ding, J. Kulkarni, and S. Yekhanin · 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.
Heavy hitters and the structure of local privacy
M. Bun, J. Nelson, and U. Stemmer · 2018
Calm: Consistent adaptive local marginal for marginal release under local differential privacy
Z. Zhang, T. Wang, N. Li, S. He, and J. Chen · 2018
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Hadamard response: Estimating distributions privately, efficiently, and with little communication
J. Acharya, Z. Sun, and H. Zhang · 2019
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Linear queries estimation with local differential privacy
R. Bassily · 2019
Closest in time.
Towards instance-optimal private query release
J. Blasiok, M. Bun, A. Nikolov, and T. Steinke · 2019
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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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Cited alongside, same era.
Marginal release under local differential privacy
G. Cormode, T. Kulkarni, and D. Srivastava · 2018
Cited alongside, same era.
Amplification by shuffling: From local to central differential privacy via anonymity
Ú. Erlingsson, V. Feldman, I. Mironov, A. Raghunathan, K. Talwar, and A. Thakurta · 2018
Cited alongside, same era.
Local differential privacy for evolving data
M. Joseph, A. Roth, J. Ullman, and B. Waggoner · 2018
Cited alongside, same era.
Lopub: High-dimensional crowdsourced data publication with local differential privacy
X. Ren, C.-M. Yu, W. Yu, S. Yang, X. Yang, J. A. McCann, and S. Y. Philip · 2018
Cited alongside, same era.
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
Cited alongside, same era.
Locally differentially private frequent itemset mining
T. Wang, N. Li, and S. Jha · 2018
Cited alongside, same era.
Closest in time.
Estimating numerical distributions under local differential privacy
Z. Li, T. Wang, M. Lopuhaä-Zwakenberg, B. Skoric, and N. Li · 2019
Closest in time.
Collecting and analyzing multidimensional data with local differential privacy
N. Wang, X. Xiao, Y. Yang, J. Zhao, S. C. Hui, H. Shin, J. Shin, and G. Yu · 2019
Closest in time.
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
Closest in time.
Locally differentially private heavy hitter identification
T. Wang, N. Li, and S. Jha · 2019
Closest in time.
Privkv: Key-value data collection with local differential privacy
Q. Ye, H. Hu, X. Meng, and H. Zheng · 2019
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Pckv: Locally differentially private correlated key-value data collection with optimized utility
X. Gu, M. Li, Y. Cheng, L. Xiong, and Y. Cao · 2020
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