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Recently, a number of approaches and techniques have been introduced for reporting software statistics with strong privacy guarantees.
B. Jayaraman and D. Evans, “Evaluating differentially private machine learning in practice,” in USENIX Security Symposium , 2019, pp. 1895–1912
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G. Cormode and S. Muthukrishnan, “An improved data stream summary: the count-min sketch and its applications,” Journal of Algorithms , vol. 55, no. 1, pp. 58–75, 2005
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C. Dwork, F. McSherry, K. Nissim, and A. Smith, “Calibrating noise to sensitivity in private data analysis,” in Proc. of the Third Conf. on Theory of Cryptography (TCC) , 2006, pp. 265–284. [Online]. Available: http://dx.doi.org/10.1007/11681878_14
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C. Dwork, “Differential privacy,” in Proc. of the 33rd International Conf. on Automata, Languages and Programming—Volume Part II (ICALP) , 2006, pp. 1–12
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C. Dwork, K. Kenthapadi, F. McSherry, I. Mironov, and M. Naor, “Our data, ourselves: Privacy via distributed noise generation,” in Advances in Cryptology—EUROCRYPT , 2006, pp. 486–503
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C. Dwork, G. N. Rothblum, and S. Vadhan, “Boosting and differential privacy,” in Proc. of the 51st Annual IEEE Symp. on Foundations of Computer Science (FOCS) , 2010, pp. 51–60
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O. Williams and F. McSherry, “Probabilistic inference and differential privacy,” in Advances in Neural Information Processing Systems , 2010, pp. 2451–2459
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S. Song, K. Chaudhuri, and A. D. Sarwate, “Stochastic gradient descent with differentially private updates,” in 2013 IEEE Global Conference on Signal and Information Processing . IEEE, 2013, pp. 245–248
2013
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2013
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Ú. Erlingsson, V. Pihur, and A. Korolova, “RAPPOR: Randomized aggregatable privacy-preserving ordinal response,” in Proc. of the 2014 ACM Conf. on Computer and Communications Security (CCS’14) . ACM, 2014, pp. 1054–1067
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C. Dwork and A. Roth, “The algorithmic foundations of differential privacy,” Foundations and Trends in Theoretical Computer Science , vol. 9, no. 3–4, pp. 211–407, 2014
2014
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R. Bassily, A. Smith, and A. Thakurta, “Private empirical risk minimization: Efficient algorithms and tight error bounds,” in Proc. of the 2014 IEEE 55th Annual Symp. on Foundations of Computer Science (FOCS) , 2014, pp. 464–473
2014
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R. Bassily and A. Smith, “Local, private, efficient protocols for succinct histograms,” in Proc. of the Forty-Seventh Annual ACM Symp. on Theory of Computing (STOC’15) , 2015, pp. 127–135
2015
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G. Fanti, V. Pihur, and Ú. Erlingsson, “Building a RAPPOR with the unknown: Privacy-preserving learning of associations and data dictionaries,” Proc. on Privacy Enhancing Technologies (PoPETS) , vol. 2016, no. 3, pp. 41–61, 2016
2016
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M. Bun and T. Steinke, “Concentrated differential privacy: Simplifications, extensions, and lower bounds,” in Theory of Cryptography Conference . Springer, 2016, pp. 635–658
2016
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P. Kairouz, S. Oh, and P. Viswanath, “Extremal mechanisms for local differential privacy,” Journal of Machine Learning Research , vol. 17, no. 17, pp. 1–51, 2016. [Online]. Available: http://jmlr.org/papers/v17/15-135.html
2016
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M. Abadi, A. Chu, I. J. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, and L. Zhang, “Deep learning with differential privacy,” in Proc. of the 2016 ACM SIGSAC Conf. on Computer and Communications Security (CCS’16) , 2016, pp. 308–318
2016
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J. Valentino-DeVries, “Uncovering what your phone knows,” The New York Times , Dec. 2018, https://www.nytimes.com/2018/12/14/reader-center/phone-data-location-investigation.html
2018
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Apple’s differential privacy team, “Learning with privacy at scale,” https://machinelearning.apple.com/docs/learning-with-privacy-at-scale/appledifferentialprivacysystem.pdf, 2018
2018
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D. Lazar, Y. Gilad, and N. Zeldovich, “Karaoke: Distributed private messaging immune to passive traffic analysis,” in 13th USENIX Symp. on Operating Systems Design and Implementation (OSDI’18) , 2018, pp. 711–725
2018
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2018
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A. Bittau, Ú. Erlingsson, P. Maniatis, I. Mironov, A. Raghunathan, D. Lie, M. Rudominer, U. Kode, J. Tinnes, and B. Seefeld, “Prochlo: Strong privacy for analytics in the crowd,” in Proc. of the 26th ACM Symp. on Operating Systems Principles (SOSP’17) , 2017
2017
Cited alongside, same era.
H. Corrigan-Gibbs and D. Boneh, “Prio: Private, robust, and scalable computation of aggregate statistics,” in Proc. of the 14th USENIX Conf. on Networked Systems Design and Implementation (NSDI) , 2017, pp. 259–282
2017
Cited alongside, same era.
K. Bonawitz, V. Ivanov, B. Kreuter, A. Marcedone, H. B. McMahan, S. Patel, D. Ramage, A. Segal, and K. Seth, “Practical secure aggregation for privacy-preserving machine learning,” in Proc. of the 2017 ACM Conf. on Computer and Communications Security (CCS) , 2017, pp. 1175–1191
2017
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R. Bassily, K. Nissim, U. Stemmer, and A. Thakurta, “Practical locally private heavy hitters,” in Advances in Neural Information Processing Systems (NIPS) , 2017, pp. 2288–2296
2017
Cited alongside, same era.
2017
Cited alongside, same era.
A. Kwon, H. Corrigan-Gibbs, S. Devadas, and B. Ford, “Atom: Horizontally scaling strong anonymity,” in Proceedings of the 26th Symposium on Operating Systems Principles , ser. SOSP ’17, 2017
2017
Cited alongside, same era.
A. Smith, A. Thakurta, and J. Upadhyay, “Is interaction necessary for distributed private learning?” in 2017 IEEE Symposium on Security and Privacy (SP) . IEEE, 2017, pp. 58–77
2017
Cited alongside, same era.
2017
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J. C. Duchi, M. I. Jordan, and M. J. Wainwright, “Minimax optimal procedures for locally private estimation,” Journal of the American Statistical Association , vol. 113, no. 521, pp. 182–201, 2018
2018
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M. Bun, J. Nelson, and U. Stemmer, “Heavy hitters and the structure of local privacy,” in Proceedings of the 37th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems . ACM, 2018, pp. 435–447
2018
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S. Yeom, I. Giacomelli, M. Fredrikson, and S. Jha, “Privacy risk in machine learning: Analyzing the connection to overfitting,” in 2018 IEEE 31st Computer Security Foundations Symposium (CSF) , Jul. 2018, pp. 268–282
2018
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Ú. Erlingsson, V. Feldman, I. Mironov, A. Raghunathan, K. Talwar, and A. Thakurta, “Amplification by shuffling: From local to central differential privacy via anonymity,” in Proceedings of the Thirtieth Annual ACM-SIAM Symposium on Discrete Algorithms . SIAM, 2019, pp. 2468–2479
2019
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E. Roth, D. Noble, B. Hemenway Falk, and A. Haeberlen, “Honeycrisp: Large-scale differentially private aggregation without a trusted core,” in Proceedings of the 27th ACM Symposium on Operating Systems Principles (SOSP’19) , Oct. 2019
2019
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A. Cheu, A. Smith, J. Ullman, D. Zeber, and M. Zhilyaev, “Distributed differential privacy via shuffling,” in Annual International Conference on the Theory and Applications of Cryptographic Techniques . Springer, 2019, pp. 375–403
2019
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2019
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M. Lécuyer, R. Spahn, K. Vodrahalli, R. Geambasu, and D. Hsu, “Privacy accounting and quality control in the sage differentially private ml platform,” 2019
2019
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B. Balle, J. Bell, A. Gascon, and K. Nissim, “The privacy blanket of the shuffle model,” in Advances in Cryptology—CRYPTO , 2019
2019
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2019
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R. Bassily, V. Feldman, K. Talwar, and A. Guha Thakurta, “Private stochastic convex optimization with optimal rates,” in Advances in Neural Information Processing Systems 32 , 2019, pp. 11 279–11 288
2019
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Úlfar Erlingsson, I. Mironov, A. Raghunathan, and S. Song, “That which we call private,” 2019
2019
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