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We present a privacy system that leverages differential privacy to protect LinkedIn members' data while also providing audience engagement insights to enable marketing analytics related applications.
1905
Earlier work this paper cites.
1909
Earlier work this paper cites.
2001
Earlier work this paper cites.
C. Dwork, F. McSherry, K. Nissim, and A. Smith, “Calibrating noise to sensitivity in private data analysis,” in Proceedings of the Third Theory of Cryptography Conference , 2006, pp. 265–284
2006
Earlier work this paper cites.
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) , 2006
2006
Earlier work this paper cites.
F. McSherry and K. Talwar, “Mechanism design via differential privacy,” in 48th Annual Symposium on Foundations of Computer Science , 2007
2007
Earlier work this paper cites.
F. McSherry, “Privacy integrated queries,” Communications of the ACM , vol. 53, pp. 89–97, September 2010
2010
Earlier work this paper cites.
L. Qiao, K. Surlaker, S. Das, T. Quiggle, B. Schulman, B. Ghosh, A. Curtis, O. Seeliger, Z. Zhang, A. Auradar, C. Beaver, G. Brandt, M. Gandhi, K. Gopalakrishna, W. Ip, S. Jgadish, S. Lu, A. Pachev, A. Ramesh, A. Sebastian, R. Shanbhag, S. Subramaniam, Y. Sun, S. Topiwala, C. Tran, J. Westerman, and D. Zhang, “On brewing fresh espresso: Linkedin’s distributed data serving platform,” in Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data , ser. SIGMOD ’13. New York, NY, USA: Association for Computing Machinery, 2013, p. 1135–1146. [Online]. Available: https://doi.org/10.1145/2463676.2465298
2013
Earlier work this paper cites.
U. Erlingsson, V. Pihur, and A. Korolova, “Rappor: Randomized aggregatable privacy-preserving ordinal response,” in Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security , ser. CCS ’14. New York, NY, USA: ACM, 2014, pp. 1054–1067. [Online]. Available: http://doi.acm.org/10.1145/2660267.2660348
2014
Earlier work this paper cites.
2016
Cited alongside, same era.
J. Murtagh and S. Vadhan, “The complexity of computing the optimal composition of differential privacy,” in Proceedings, Part I, of the 13th International Conference on Theory of Cryptography - Volume 9562 , ser. TCC 2016-A. Berlin, Heidelberg: Springer-Verlag, 2016, pp. 157–175. [Online]. Available: https://doi.org/10.1007/978-3-662-49096-9_7
2016
Cited alongside, same era.
2016
Cited alongside, same era.
Apple Differential Privacy Team, “Learning with privacy at scale,” 2017, available at https://machinelearning.apple.com/2017/12/06/learning-with-privacy-at-scale.html
K. Kenthapadi and T. T. L. Tran, “PriPeARL: A framework for privacy-preserving analytics and reporting at Linkedin,” in Proceedings of the 27th ACM International Conference on Information and Knowledge Management , ser. CIKM ’18. New York, NY, USA: ACM, 2018, pp. 2183–2191
2018
Later among the works it cites.
M. Guevara, “Google developers,” Sep 2019. [Online]. Available: https://developers.googleblog.com/2019/09/enabling-developers-and-organizations.html
2019
Later among the works it cites.
I. Kotsogiannis, Y. Tao, X. He, M. Fanaeepour, A. Machanavajjhala, M. Hay, and G. Miklau, “Privatesql: a differentially private SQL query engine,” Proceedings of the VLDB Endowment , vol. 12, pp. 1371–1384, 07 2019
2019
Later among the works it cites.
J. Kahan, “Linkedin,” Sep 2019. [Online]. Available: https://www.linkedin.com/pulse/microsoft-harvards-institute-quantitative-social-science -john-kahan/
2019
Later among the works it cites.
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2017
Cited alongside, same era.
B. Ding, J. Kulkarni, and S. Yekhanin, “Collecting telemetry data privately,” December 2017. [Online]. Available: https://www.microsoft.com/en-us/research/publication/collecting-telemetry-data-privately/
2017
Cited alongside, same era.
A. N. Dajani, A. D. Lauger, P. E. Singer, D. Kifer, J. P. Reiter, A. Machanavajjhala, S. L. Garfinkel1, S. A. Dahl, M. Graham, V. Karwa, H. Kim, P. Leclerc, I. M. Schmutte, W. N. Sexton, L. Vilhuber, and J. M. Abowd, “The modernization of statistical disclosure limitation at the U.S. Census bureau,” 2017, available online at https://www2.census.gov/cac/sac/meetings/2017-09/statistical-disclosure-limitation.pdf
2017
Cited alongside, same era.
P. Kairouz, S. Oh, and P. Viswanath, “The composition theorem for differential privacy,” IEEE Transactions on Information Theory , vol. 63, no. 6, pp. 4037–4049, June 2017
2017
Cited alongside, same era.
J.-F. Im, K. Gopalakrishna, S. Subramaniam, M. Shrivastava, A. Tumbde, X. Jiang, J. Dai, S. Lee, N. Pawar, J. Li, and R. Aringunram, “Pinot: Realtime OLAP for 530 million users,” in Proceedings of the 2018 International Conference on Management of Data , ser. SIGMOD ’18. New York, NY, USA: ACM, 2018, pp. 583–594
2018
Cited alongside, same era.
N. Johnson, J. P. Near, and D. Song, “Towards practical differential privacy for SQL queries,” Proc. VLDB Endow. , vol. 11, no. 5, pp. 526–539, Jan. 2018
2018
Cited alongside, same era.
A. Kafka, “A distributed streaming platform.” [Online]. Available: kafka.apache.org/
Cited in the paper.
U. 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 , ser. SODA ’19. USA: Society for Industrial and Applied Mathematics, 2019, p. 2468–2479
2019
Later among the works it cites.
B. Balle, J. Bell, A. Gascón, and K. Nissim, “The privacy blanket of the shuffle model,” in Advances in Cryptology - CRYPTO 2019 - 39th Annual International Cryptology Conference, Santa Barbara, CA, USA, August 18-22, 2019, Proceedings, Part II , ser. Lecture Notes in Computer Science, A. Boldyreva and D. Micciancio, Eds., vol. 11693. Springer, 2019, pp. 638–667. [Online]. Available: https://doi.org/10.1007/978-3-030-26951-7_22
2019
Later among the works it cites.
A. Cheu, A. D. Smith, J. Ullman, D. Zeber, and M. Zhilyaev, “Distributed differential privacy via shuffling,” in Advances in Cryptology - EUROCRYPT 2019 - 38th Annual International Conference on the Theory and Applications of Cryptographic Techniques, Darmstadt, Germany, May 19-23, 2019, Proceedings, Part I , ser. Lecture Notes in Computer Science, Y. Ishai and V. Rijmen, Eds., vol. 11476. Springer, 2019, pp. 375–403. [Online]. Available: https://doi.org/10.1007/978-3-030-17653-2_13
2019
Later among the works it cites.
R. J. Wilson, C. Y. Zhang, W. Lam, D. Desfontaines, D. Simmons-Marengo, and B. Gipson, “Differentially private SQL with bounded user contribution,” Proceedings on Privacy Enhancing Technologies , vol. 2020, no. 2, pp. 230 – 250, 2020. [Online]. Available: https://content.sciendo.com/view/journals/popets/2020/2/article-p230.xml
2020
Closest in time.
A. Aktay, S. Bavadekar, G. Cossoul, J. Davis, D. Desfontaines, A. Fabrikant, E. Gabrilovich, K. Gadepalli, B. Gipson, M. Guevara, C. Kamath, M. Kansal, A. Lange, C. Mandayam, A. Oplinger, C. Pluntke, T. Roessler, A. Schlosberg, T. Shekel, S. Vispute, M. Vu, G. Wellenius, B. Williams, and R. J. Wilson, “Google covid-19 community mobility reports: Anonymization process description (version 1.0),” 2020
2020
Closest in time.