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Balle, B., Bell, J., Gascón, A., Nissim, K.: Differentially private summation with multi-message shuffling. CoRR abs/1906.09116
Original
1906
Earlier work this paper cites.
Ghazi, B., Pagh, R., Velingker, A.: Scalable and differentially private distributed aggregation in the shuffled model (2019), http://arxiv.org/abs/1906.08320
Original
1906
Earlier work this paper cites.
Balle, B., Bell, J., Gascón, A., Nissim, K.: Improved summation from shuffling http://arxiv.org/abs/1909.11225
Original
1909
Earlier work this paper cites.
Kearns, M.: Efficient noise-tolerant learning from statistical queries. JACM (1998)
1998
Earlier work this paper cites.
Ishai, Y., Kushilevitz, E., Ostrovsky, R., Sahai, A.: Cryptography from anonymity. In: IEEE Symposium on Foundations of Computer Science (FOCS), (2006)
2006
Earlier work this paper cites.
Mishra, N., Sandler, M.: Privacy via pseudorandom sketches. In: PODS (2006)
2006
Earlier work this paper cites.
Cormode, G., Garofalakis, M., Haas, P.J., Jermaine, C., et al.: Synopses for massive data: Samples, histograms, wavelets, sketches. Foundations and Trends in Databases 4
2011
Earlier work this paper cites.
Kenthapadi, K., Korolova, A., Mironov, I., Mishra, N.: Privacy via the johnson-lindenstrauss transform (2012), http://arxiv.org/abs/1204.2606
Original
2012
Earlier work this paper cites.

Similar

Goryczka, S., Xiong, L., Sunderam, V.: Secure multiparty aggregation with differential privacy: A comparative study. In: EDBT/ICDT 2013 Workshops
2013
Cited alongside, same era.
Woodruff, D.P., et al.: Sketching as a tool for numerical linear algebra. Foundations and Trends in Theoretical Computer Science 10
2014
Cited alongside, same era.
Abadi, M., Chu, A., Goodfellow, I., McMahan, H.B., Mironov, I., Talwar, K., Zhang, L.: Deep learning with differential privacy. In: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security. pp. 308–318. ACM (2016)
2016
Cited alongside, same era.
Konečnỳ, J., McMahan, H.B., Yu, F.X., Richtárik, P., Suresh, A.T., Bacon, D.: Federated learning: Strategies for improving communication efficiency. arXiv preprint arXiv:1610.05492 (2016)
Original
2016
Cited alongside, same era.
McMahan, H.B., Moore, E., Ramage, D., Hampson, S., et al.: Communication-efficient learning of deep networks from decentralized data. arXiv preprint arXiv:1602.05629 (2016)

Then

Corrigan-Gibbs, H., Boneh, D.: Prio: Private, robust, and scalable computation of aggregate statistics. In: NSDI (2017)
2017
Later among the works it cites.
McMahan, H.B., Ramage, D.: Federated learning: Collaborative machine learning without centralized training data. Google AI Blog (April 2017), https://ai.googleblog.com/2017/04/federated-learning-collaborative.html
2017
Later among the works it cites.
Reyzin, L., Smith, A.D., Yakoubov, S.: Turning hate into love: Homomorphic ad hoc threshold encryption for scalable mpc. IACR Cryptology ePrint Archive 2018
2018
Later among the works it cites.
Balle, B., Bell, J., Gascón, A., Nissim, K.: 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. pp. 638–667 (2019)
2019
Closest in time.

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2019

Private Aggregation from Fewer Anonymous Messages

Ghazi, Badih, Manurangsi, Pasin, Pagh, Rasmus et al.

Understand

Consider the setup where $n$ parties are each given a number $x_i \in \mathbb{F}_q$ and the goal is to compute the sum $\sum_i x_i$ in a secure fashion and with as little communication as possible.

We study this problem in the anonymized model of Ishai et al.
(FOCS 2006) where each party may broadcast anonymous messages on an insecure channel.
We present a new analysis of the one-round "split and mix" protocol of Ishai et al.

Built on

Balle, B., Bell, J., Gascón, A., Nissim, K.: Differentially private summation with multi-message shuffling. CoRR abs/1906.09116
Original
1906
Earlier work this paper cites.
Ghazi, B., Pagh, R., Velingker, A.: Scalable and differentially private distributed aggregation in the shuffled model (2019), http://arxiv.org/abs/1906.08320
Original
1906
Earlier work this paper cites.
Balle, B., Bell, J., Gascón, A., Nissim, K.: Improved summation from shuffling http://arxiv.org/abs/1909.11225
Original
1909
Earlier work this paper cites.
Kearns, M.: Efficient noise-tolerant learning from statistical queries. JACM (1998)
1998
Earlier work this paper cites.
Ishai, Y., Kushilevitz, E., Ostrovsky, R., Sahai, A.: Cryptography from anonymity. In: IEEE Symposium on Foundations of Computer Science (FOCS), (2006)
2006
Earlier work this paper cites.
Mishra, N., Sandler, M.: Privacy via pseudorandom sketches. In: PODS (2006)
2006
Earlier work this paper cites.
Cormode, G., Garofalakis, M., Haas, P.J., Jermaine, C., et al.: Synopses for massive data: Samples, histograms, wavelets, sketches. Foundations and Trends in Databases 4
2011
Earlier work this paper cites.
Kenthapadi, K., Korolova, A., Mironov, I., Mishra, N.: Privacy via the johnson-lindenstrauss transform (2012), http://arxiv.org/abs/1204.2606
Original
2012
Earlier work this paper cites.

Similar

Goryczka, S., Xiong, L., Sunderam, V.: Secure multiparty aggregation with differential privacy: A comparative study. In: EDBT/ICDT 2013 Workshops
2013
Cited alongside, same era.
Woodruff, D.P., et al.: Sketching as a tool for numerical linear algebra. Foundations and Trends in Theoretical Computer Science 10
2014
Cited alongside, same era.
Abadi, M., Chu, A., Goodfellow, I., McMahan, H.B., Mironov, I., Talwar, K., Zhang, L.: Deep learning with differential privacy. In: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security. pp. 308–318. ACM (2016)
2016
Cited alongside, same era.
Konečnỳ, J., McMahan, H.B., Yu, F.X., Richtárik, P., Suresh, A.T., Bacon, D.: Federated learning: Strategies for improving communication efficiency. arXiv preprint arXiv:1610.05492 (2016)
Original
2016
Cited alongside, same era.
McMahan, H.B., Moore, E., Ramage, D., Hampson, S., et al.: Communication-efficient learning of deep networks from decentralized data. arXiv preprint arXiv:1602.05629 (2016)

Then

Corrigan-Gibbs, H., Boneh, D.: Prio: Private, robust, and scalable computation of aggregate statistics. In: NSDI (2017)
2017
Later among the works it cites.
McMahan, H.B., Ramage, D.: Federated learning: Collaborative machine learning without centralized training data. Google AI Blog (April 2017), https://ai.googleblog.com/2017/04/federated-learning-collaborative.html
2017
Later among the works it cites.
Reyzin, L., Smith, A.D., Yakoubov, S.: Turning hate into love: Homomorphic ad hoc threshold encryption for scalable mpc. IACR Cryptology ePrint Archive 2018
2018
Later among the works it cites.
Balle, B., Bell, J., Gascón, A., Nissim, K.: 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. pp. 638–667 (2019)
2019
Closest in time.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Original

2016

Cited alongside, same era.

Melis, L., Danezis, G., Cristofaro, E.D.: Efficient private statistics with succinct sketches. In: NDSS (2016)

2016

Cited alongside, same era.

Bittau, A., Erlingsson, Ú., Maniatis, P., Mironov, I., Raghunathan, A., Lie, D., Rudominer, M., Kode, U., Tinnés, J., Seefeld, B.: Prochlo: Strong privacy for analytics in the crowd. In: Proceedings of the 26th Symposium on Operating Systems Principles, Shanghai, China, October 28-31, 2017. pp. 441–459 (2017)

2017

Cited alongside, same era.

Bonawitz, K., Ivanov, V., Kreuter, B., Marcedone, A., McMahan, H.B., Patel, S., Ramage, D., Segal, A., Seth, K.: Practical secure aggregation for privacy-preserving machine learning. In: Thuraisingham, B.M., Evans, D., Malkin, T., Xu, D. (eds.) Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, CCS 2017, Dallas, TX, USA, October 30 - November 03, 2017. pp. 1175–1191. ACM (2017)

2017

Cited alongside, same era.

Cheu, A., Smith, A.D., Ullman, J., Zeber, D., Zhilyaev, M.: Distributed differential privacy via shuffling. In: Ishai, Y., Rijmen, V. (eds.) 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. Lecture Notes in Computer Science, vol. 11476, pp. 375–403. Springer (2019)

2019

Closest in time.

Erlingsson, Ú., Feldman, V., Mironov, I., Raghunathan, A., Talwar, K., Thakurta, A.: Amplification by shuffling: From local to central differential privacy via anonymity. In: SODA. pp. 2468–2479 (2019)

2019

Closest in time.

Ghazi, B., Golowich, N., Kumar, R., Pagh, R., Velingker, A.: Private heavy hitters and range queries in the shuffled model. arXiv preprint arXiv:1908.11358 (2019)

Original

2019

Closest in time.

Wang, T., Xu, M., Ding, B., Zhou, J., Li, N., Jha, S.: Practical and robust privacy amplification with multi-party differential privacy. arXiv:1908.11515 (2019)

Original

2019

Closest in time.