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We design a scalable algorithm to privately generate location heatmaps over decentralized data from millions of user devices.
Randomized response: A survey technique for eliminating evasive answer bias
S. L. Warner · 1965
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The quadtree and related hierarchical data structures
H. Samet · 1984
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Finding hierarchical heavy hitters in data streams
G. Cormode, F. Korn, S. Muthukrishnan, and D. Srivastava · 2003
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Privacy preserving mining of association rules
A. Evfimievski, R. Srikant, R. Agrawal, and J. Gehrke · 2004
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Privacy as contextual integrity
H. Nissenbaum · 2004
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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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Cryptography from anonymity
Y. Ishai, E. Kushilevitz, R. Ostrovsky, and A. Sahai · 2006
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Computational differential privacy
I. Mironov, O. Pandey, O. Reingold, and S. Vadhan · 2009
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Discovering frequent patterns in sensitive data
R. Bhaskar, S. Laxman, A. Smith, and A. Thakurta · 2010
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What can we learn privately?
S. P. Kasiviswanathan, H. K. Lee, K. Nissim, S. Raskhodnikova, and A. Smith · 2011
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Differentially private spatial decompositions
G. Cormode, C. Procopiuc, D. Srivastava, E. Shen, and T. Yu · 2012
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Universally utility-maximizing privacy mechanisms
A. Ghosh, T. Roughgarden, and M. Sundararajan · 2012
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Mining frequent patterns with differential privacy
L. Bonomi and L. Xiong · 2013
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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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Secure multiparty aggregation with differential privacy: A comparative study
S. Goryczka, L. Xiong, and V. Sunderam · 2013
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Differentially private grids for geospatial data
W. Qardaji, W. Yang, and N. Li · 2013
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Differentially private learning of structured discrete distributions
I. Diakonikolas, M. Hardt, and L. Schmidt · 2015
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A comprehensive comparison of multiparty secure additions with differential privacy
S. Goryczka and L. Xiong · 2015
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Modeling user activity preference by leveraging user spatial temporal characteristics in lbsns
D. Yang, D. Zhang, V. W. Zheng, and Z. Yu · 2015
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Fairness versus efficiency of vaccine allocation strategies
M. Yi and A. Marathe · 2015
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Practical secure aggregation for federated learning on user-held data
K. Bonawitz, V. Ivanov, B. Kreuter, A. Marcedone, H. B. McMahan, S. Patel, D. Ramage, A. Segal, and K. Seth · 2016
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Discrete distribution estimation under local privacy
P. Kairouz, K. Bonawitz, and D. Ramage · 2016
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Differentially private frequent sequence mining
S. Xu, X. Cheng, S. Su, K. Xiao, and L. Xiong · 2016
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Privtree: A differentially private algorithm for hierarchical decompositions
J. Zhang, X. Xiao, and X. Xie · 2016
Cited alongside, same era.
Practical locally private heavy hitters
R. Bassily, K. Nissim, U. Stemmer, and A. G. Thakurta · 2017
Cited alongside, same era.
Prochlo: Strong privacy for analytics in the crowd
A. Bittau, U. Erlingsson, P. Maniatis, I. Mironov, A. Raghunathan, D. Lie, M. Rudominer, U. Kode, J. Tinnes, and B. Seefeld · 2017
Cited alongside, same era.
Prochlo: Strong privacy for analytics in the crowd
A. Bittau, Ú. Erlingsson, P. Maniatis, I. Mironov, A. Raghunathan, D. Lie, M. Rudominer, U. Kode, J. Tinnes, and B. Seefeld · 2017
Cited alongside, same era.
A Framework for Evaluating Epidemic Forecasts
F. Sadat Tabataba, P. Chakraborty, N. Ramakrishnan, S. Venkatramanan, J. Chen, B. Lewis, and M. Marathe · 2017
Cited alongside, same era.
A hybrid approach to privacy-preserving federated learning
S. Truex, N. Baracaldo, A. Anwar, T. Steinke, H. Ludwig, R. Zhang, and Y. Zhou · 2019
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Improving utility and security of the shuffler-based differential privacy
T. Wang, B. Ding, M. Xu, Z. Huang, C. Hong, J. Zhou, N. Li, and S. Jha · 2019
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Ldpart: effective location-record data publication via local differential privacy
X. Zhao, Y. Li, Y. Yuan, X. Bi, and G. Wang · 2019
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Separating local & shuffled differential privacy via histograms
V. Balcer and A. Cheu · 2020
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Private summation in the multi-message shuffle model
B. Balle, J. Bell, A. Gascón, and K. Nissim · 2020
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Secure single-server aggregation with (poly)logarithmic overhead
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F. Valovich and F. Alda · 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.
J. Acharya, Z. Sun, and H. Zhang · 2018
Cited alongside, same era.
Improving the gaussian mechanism for differential privacy: Analytical calibration and optimal denoising
B. Balle and Y.-X. Wang · 2018
Cited alongside, same era.
Heavy hitters and the structure of local privacy
M. Bun, J. Nelson, and U. Stemmer · 2018
Cited alongside, same era.
Marginal release under local differential privacy
G. Cormode, T. Kulkarni, and D. Srivastava · 2018
Cited alongside, same era.
High resolution population density maps
N. Fulk · 2018
Cited alongside, same era.
J. H. Bell, K. A. Bonawitz, A. Gascón, T. Lepoint, and M. Raykova · 2020
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Optimal influenza vaccine distribution with equity
S. Enayati and O. Y. Özaltın · 2020
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Encode, shuffle, analyze privacy revisited: Formalizations and empirical evaluation
Ú. Erlingsson, V. Feldman, I. Mironov, A. Raghunathan, S. Song, K. Talwar, and A. Thakurta · 2020
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Hiding among the clones: A simple and nearly optimal analysis of privacy amplification by shuffling
V. Feldman, A. McMillan, and K. Talwar · 2020
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Pure differentially private summation from anonymous messages
B. Ghazi, N. Golowich, R. Kumar, P. Manurangsi, R. Pagh, and A. Velingker · 2020
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Private counting from anonymous messages: Near-optimal accuracy with vanishing communication overhead
B. Ghazi, R. Kumar, P. Manurangsi, and R. Pagh · 2020
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Private aggregation from fewer anonymous messages
B. Ghazi, P. Manurangsi, R. Pagh, and A. Velingker · 2020
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Shuffled model of federated learning: Privacy, communication and accuracy trade-offs
A. M. Girgis, D. Data, S. Diggavi, P. Kairouz, and A. T. Suresh · 2020
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Fastsecagg: Scalable secure aggregation for privacy-preserving federated learning, 2020
S. Kadhe, N. Rajaraman, O. O. Koyluoglu, and K. Ramchandran · 2020
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Federated analytics: Collaborative data science without data collection
D. Ramage and S. Mazzocchi · 2020
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Federated heavy hitters discovery with differential privacy
W. Zhu, P. Kairouz, B. McMahan, H. Sun, and W. Li · 2020
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The skellam mechanism for differentially private federated learning
N. Agarwal, P. Kairouz, and Z. Liu · 2021
Closest in time.
Connecting robust shuffle privacy and pan-privacy
V. Balcer, A. Cheu, M. Joseph, and J. Mao · 2021
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On the power of multiple anonymous messages
B. Ghazi, N. Golowich, R. Kumar, R. Pagh, and A. Velingker · 2021
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Shuffled model of differential privacy in federated learning
A. Girgis, D. Data, S. Diggavi, P. Kairouz, and A. T. Suresh · 2021
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Frequency estimation under multiparty differential privacy: One-shot and streaming
Z. Huang, Y. Qiu, K. Yi, and G. Cormode · 2021
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The distributed discrete gaussian mechanism for federated learning with secure aggregation, 2021
P. Kairouz, Z. Liu, and T. Steinke · 2021
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