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Differential privacy (DP) is a formal notion for quantifying the privacy loss of algorithms.
Scalable and differentially private distributed aggregation in the shuffled model
Ghazi, B., Pagh, R., and Velingker, A · 1906
Earlier work this paper cites.
Eine informationstheoretische ungleichung und ihre anwendung auf beweis der ergodizitaet von markoffschen ketten
Csiszár, I · 1964
Earlier work this paper cites.
Randomized response: A survey technique for eliminating evasive answer bias
Warner, S. L · 1965
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A general class of coefficients of divergence of one distribution from another
Ali, S. M. and Silvey, S. D · 1966
Earlier work this paper cites.
Information-type measures of difference of probability distributions and indirect observation
Csiszár, I · 1967
Earlier work this paper cites.
An Introduction to Probability Theory and Its Applications , volume 1
Feller, W · 1968
Earlier work this paper cites.
The Art of Computer Programming, Volume II: Seminumerical Algorithms, 2nd Edition
Knuth, D. E · 1981
Earlier work this paper cites.
Efficient noise-tolerant learning from statistical queries
Kearns, M · 1998
Earlier work this paper cites.
The Laplace Distribution and Generalizations: A Revisit with Applications to Communications, Economics, Engineering, and Finance
Kotz, S., Kozubowski, T., and Podgorski, K · 2001
Earlier work this paper cites.
Practical privacy: the SuLQ framework
Blum, A., Dwork, C., McSherry, F., and Nissim, K · 2005
Earlier work this paper cites.
Cryptography from anonymity
Ishai, Y., Kushilevitz, E., Ostrovsky, R., and Sahai, A · 2006
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Distributed private data analysis: Simultaneously solving how and what
Beimel, A., Nissim, K., and Omri, E · 2008
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What can we learn privately?
Kasiviswanathan, S. P., Lee, H. K., Nissim, K., Rashkodnikova, S., and Smith, A · 2008
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Optimal lower bound for differentially private multi-party aggregation
Chan, T. H., Shi, E., and Song, D · 2012
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Universally utility-maximizing privacy mechanisms
Ghosh, A., Roughgarden, T., and Sundararajan, M · 2012
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Beyond differential privacy: Composition theorems and relational logic for f f -divergences between probabilistic programs
Barthe, G. and Olmedo, F · 2013
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The Algorithmic Foundations of Differential Privacy
Dwork, C., Roth, A., et al · 2014
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RAPPOR: Randomized aggregatable privacy-preserving ordinal response
Erlingsson, Ú., Pihur, V., and Korolova, A · 2014
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1-bit stochastic gradient descent and its application to data-parallel distributed training of speech DNNs
Seide, F., Fu, H., Droppo, J., Li, G., and Yu, D · 2014
Cited alongside, same era.
How Google tricks itself to protect Chrome user privacy
Shankland, S · 2014
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Local, private, efficient protocols for succinct histograms
Bassily, R. and Smith, A · 2015
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A comprehensive comparison of multiparty secure additions with differential privacy
Goryczka, S. and Xiong, L · 2015
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Apple’s “differential privacy” is about collecting your data – but not your data
Greenberg, A · 2016
Cited alongside, same era.
Discrete distribution estimation under local privacy
Kairouz, P., Bonawitz, K., and Ramage, D · 2016
Distributed differential privacy via shuffling
Cheu, A., Smith, A. D., Ullman, J., Zeber, D., and Zhilyaev, M · 2019
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Amplification by shuffling: From local to central differential privacy via anonymity
Erlingsson, Ú., Feldman, V., Mironov, I., Raghunathan, A., Talwar, K., and Thakurta, A · 2019
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Advances and open problems in federated learning
Kairouz, P., McMahan, H. B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A. N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., D’Oliveira, R. G. L., Rouayheb, S. E., Evans, D., Gardner, J., Garrett, Z., Gascón, A., Ghazi, B., Gibbons, P. B., Gruteser, M., Harchaoui, Z., He, C., He, L., Huo, Z., Hutchinson, B., Hsu, J., Jaggi, M., Javidi, T., Joshi, G., Khodak, M., Konečný, J., Korolova, A., Koushanfar, F., Koyejo, S., Lepoint, T., Liu, Y., Mittal, P., Mohri, M., Nock, R., Özgür, A., Pagh, R., Raykova, M., Qi, H., Ramage, D., Raskar, R., Song, D., Song, W., Stich, S. U., Sun, Z., Suresh, A. T., Tramèr, F., Vepakomma, P., Wang, J., Xiong, L., Xu, Z., Yang, Q., Yu, F. X., Yu, H., and Zhao, S · 2019
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PrivateSQL: a differentially private SQL query engine
Kotsogiannis, I., Tao, Y., He, X., Fanaeepour, M., Machanavajjhala, A., Hay, M., and Miklau, G · 2019
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Integrated public use microdata series (IPUMS) USA: Version 9.0 [dataset]
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Cited alongside, same era.
Federated learning: Strategies for improving communication efficiency
Konečnỳ, J., McMahan, H. B., Yu, F. X., Richtárik, P., Suresh, A. T., and Bacon, D · 2016
Cited alongside, same era.
f f -divergence inequalities
Sason, I. and Verdu, S · 2016
Cited alongside, same era.
Learning with privacy at scale
Apple Differential Privacy Team · 2017
Cited alongside, same era.
Practical locally private heavy hitters
Bassily, R., Nissim, K., Stemmer, U., and Thakurta, A. G · 2017
Cited alongside, same era.
Prochlo: Strong privacy for analytics in the crowd
Bittau, A., Erlingsson, Ú., Maniatis, P., Mironov, I., Raghunathan, A., Lie, D., Rudominer, M., Kode, U., Tinnés, J., and Seefeld, B · 2017
Cited alongside, same era.
A short note on Poisson tail bounds, 2017
Canonne, C · 2017
Cited alongside, same era.
Ruggles, S., Flood, S., Goeken, R., Grover, J., Meyer, E., Pacas, J., and Sobek, M · 2019
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Differentially private anonymized histograms
Suresh, A. T · 2019
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MURS: Practical and robust privacy amplification with multi-party differential privacy
Wang, T., Xu, M., Ding, B., Zhou, J., Li, N., and Jha, S · 2019
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Differentially private SQL with bounded user contribution
Wilson, R. J., Zhang, C. Y., Lam, W., Desfontaines, D., Simmons-Marengo, D., and Gipson, B · 2019
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Separating local & shuffled differential privacy via histograms
Balcer, V. and Cheu, A · 2020
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Private summation in the multi-message shuffle model
Balle, B., Bell, J., Gascón, A., and Nissim, K · 2020
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Encode, shuffle, analyze privacy revisited: Formalizations and empirical evaluation
Erlingsson, Ú., Feldman, V., Mironov, I., Raghunathan, A., Song, S., Talwar, K., and Thakurta, A · 2020
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Locally private k k -means clustering
Stemmer, U · 2020
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Connecting robust shuffle privacy and pan-privacy
Balcer, V., Cheu, A., Joseph, M., and Mao, J · 2021
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Locally private k-means in one round
Chang, A., Ghazi, B., Kumar, R., and Manurangsi, P · 2021
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On distributed differential privacy and counting distinct elements
Chen, L., Ghazi, B., Kumar, R., and Manurangsi, P · 2021
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Differentially private histograms in the shuffle model from fake users
Cheu, A. and Zhilyaev, M · 2021
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Differentially private aggregation in the shuffle model:almost central accuracy in almost a single message
Ghazi, B., Kumar, R., Manurangsi, P., Pagh, R., and Sinha, A · 2021
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