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2019 | OriginalPaper | Buchkapitel

The Privacy Blanket of the Shuffle Model

verfasst von : Borja Balle, James Bell, Adrià Gascón, Kobbi Nissim

Erschienen in: Advances in Cryptology – CRYPTO 2019

Verlag: Springer International Publishing

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Abstract

This work studies differential privacy in the context of the recently proposed shuffle model. Unlike in the local model, where the server collecting privatized data from users can track back an input to a specific user, in the shuffle model users submit their privatized inputs to a server anonymously. This setup yields a trust model which sits in between the classical curator and local models for differential privacy. The shuffle model is the core idea in the Encode, Shuffle, Analyze (ESA) model introduced by Bittau et al. (SOPS 2017). Recent work by Cheu et al. (EUROCRYPT 2019) analyzes the differential privacy properties of the shuffle model and shows that in some cases shuffled protocols provide strictly better accuracy than local protocols. Additionally, Erlingsson et al. (SODA 2019) provide a privacy amplification bound quantifying the level of curator differential privacy achieved by the shuffle model in terms of the local differential privacy of the randomizer used by each user.
In this context, we make three contributions. First, we provide an optimal single message protocol for summation of real numbers in the shuffle model. Our protocol is very simple and has better accuracy and communication than the protocols for this same problem proposed by Cheu et al. Optimality of this protocol follows from our second contribution, a new lower bound for the accuracy of private protocols for summation of real numbers in the shuffle model. The third contribution is a new amplification bound for analyzing the privacy of protocols in the shuffle model in terms of the privacy provided by the corresponding local randomizer. Our amplification bound generalizes the results by Erlingsson et al. to a wider range of parameters, and provides a whole family of methods to analyze privacy amplification in the shuffle model.

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Fußnoten
1
Of which, in this paper, we only consider the non-interactive version for simplicity.
 
2
Here we use \(\mathsf {Im}(\mathcal {R}')\) to denote the image of the local randomizer \(\mathcal {R}'\).
 
Literatur
1.
Zurück zum Zitat Balle, B., Barthe, G., Gaboardi, M.: Privacy amplification by subsampling: tight analyses via couplings and divergences. In: Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, Montréal, Canada, 3–8 December 2018, pp. 6280–6290 (2018) Balle, B., Barthe, G., Gaboardi, M.: Privacy amplification by subsampling: tight analyses via couplings and divergences. In: Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, Montréal, Canada, 3–8 December 2018, pp. 6280–6290 (2018)
3.
Zurück zum Zitat Balle, B., Wang, Y.X.: Improving the Gaussian mechanism for differential privacy: analytical calibration and optimal denoising. In: Proceedings of the 35th International Conference on Machine Learning, ICML (2018) Balle, B., Wang, Y.X.: Improving the Gaussian mechanism for differential privacy: analytical calibration and optimal denoising. In: Proceedings of the 35th International Conference on Machine Learning, ICML (2018)
4.
Zurück zum Zitat Barthe, G., Gaboardi, M., Grégoire, B., Hsu, J., Strub, P.: Proving differential privacy via probabilistic couplings. In: Symposium on Logic in Computer Science (LICS), pp. 749–758 (2016) Barthe, G., Gaboardi, M., Grégoire, B., Hsu, J., Strub, P.: Proving differential privacy via probabilistic couplings. In: Symposium on Logic in Computer Science (LICS), pp. 749–758 (2016)
5.
Zurück zum Zitat Barthe, G., Köpf, B., Olmedo, F., Béguelin, S.Z.: Probabilistic relational reasoning for differential privacy. In: Symposium on Principles of Programming Languages (POPL), pp. 97–110 (2012) Barthe, G., Köpf, B., Olmedo, F., Béguelin, S.Z.: Probabilistic relational reasoning for differential privacy. In: Symposium on Principles of Programming Languages (POPL), pp. 97–110 (2012)
8.
Zurück zum Zitat Bhowmick, A., Duchi, J., Freudiger, J., Kapoor, G., Rogers, R.: Protection against reconstruction and its applications in private federated learning. arXiv e-prints arXiv:1812.00984, December 2018 Bhowmick, A., Duchi, J., Freudiger, J., Kapoor, G., Rogers, R.: Protection against reconstruction and its applications in private federated learning. arXiv e-prints arXiv:​1812.​00984, December 2018
10.
Zurück zum Zitat Boucheron, S., Lugosi, G., Massart, P.: Concentration Inequalities: A Nonasymptotic Theory of Independence. Oxford University Press, Oxford (2013)CrossRef Boucheron, S., Lugosi, G., Massart, P.: Concentration Inequalities: A Nonasymptotic Theory of Independence. Oxford University Press, Oxford (2013)CrossRef
15.
Zurück zum Zitat Erlingsson, Ú., Feldman, V., Mironov, I., Raghunathan, A., Talwar, K., Thakurta, A.: Amplification by shuffling: from local to central differential privacy via anonymity. In: Proceedings of the Thirtieth Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 2468–2479. SIAM (2019)CrossRef Erlingsson, Ú., Feldman, V., Mironov, I., Raghunathan, A., Talwar, K., Thakurta, A.: Amplification by shuffling: from local to central differential privacy via anonymity. In: Proceedings of the Thirtieth Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 2468–2479. SIAM (2019)CrossRef
16.
Zurück zum Zitat Erlingsson, Ú., Pihur, V., Korolova, A.: RAPPOR: randomized aggregatable privacy-preserving ordinal response. In: Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security, Scottsdale, AZ, USA, 3–7 November 2014, pp. 1054–1067 (2014) Erlingsson, Ú., Pihur, V., Korolova, A.: RAPPOR: randomized aggregatable privacy-preserving ordinal response. In: Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security, Scottsdale, AZ, USA, 3–7 November 2014, pp. 1054–1067 (2014)
17.
Zurück zum Zitat Feldman, V., Mironov, I., Talwar, K., Thakurta, A.: Privacy amplification by iteration. In: 59th IEEE Annual Symposium on Foundations of Computer Science, FOCS 2018, Paris, France, 7–9 October 2018, pp. 521–532 (2018) Feldman, V., Mironov, I., Talwar, K., Thakurta, A.: Privacy amplification by iteration. In: 59th IEEE Annual Symposium on Foundations of Computer Science, FOCS 2018, Paris, France, 7–9 October 2018, pp. 521–532 (2018)
18.
Zurück zum Zitat Ishai, Y., Kushilevitz, E., Ostrovsky, R., Sahai, A.: Cryptography from anonymity. In: FOCS, pp. 239–248. IEEE Computer Society (2006) Ishai, Y., Kushilevitz, E., Ostrovsky, R., Sahai, A.: Cryptography from anonymity. In: FOCS, pp. 239–248. IEEE Computer Society (2006)
19.
Zurück zum Zitat Jaynes, E.T.: Probability Theory: The Logic of Science. Cambridge University Press, Cambridge (2003)CrossRef Jaynes, E.T.: Probability Theory: The Logic of Science. Cambridge University Press, Cambridge (2003)CrossRef
21.
Zurück zum Zitat Kairouz, P., Bonawitz, K., Ramage, D.: Discrete distribution estimation under local privacy. In: ICML, JMLR Workshop and Conference Proceedings, vol. 48, pp. 2436–2444 (2016). JMLR.org Kairouz, P., Bonawitz, K., Ramage, D.: Discrete distribution estimation under local privacy. In: ICML, JMLR Workshop and Conference Proceedings, vol. 48, pp. 2436–2444 (2016). JMLR.​org
22.
Zurück zum Zitat Kairouz, P., Oh, S., Viswanath, P.: Extremal mechanisms for local differential privacy. J. Mach. Learn. Res. 17, 17:1–17:51 (2016)MathSciNetMATH Kairouz, P., Oh, S., Viswanath, P.: Extremal mechanisms for local differential privacy. J. Mach. Learn. Res. 17, 17:1–17:51 (2016)MathSciNetMATH
23.
Zurück zum Zitat Kasiviswanathan, S.P., Lee, H.K., Nissim, K., Raskhodnikova, S., Smith, A.D.: What can we learn privately? In: 49th Annual IEEE Symposium on Foundations of Computer Science, FOCS 2008, Philadelphia, PA, USA, 25–28 October 2008, pp. 531–540. IEEE Computer Society (2008). https://doi.org/10.1109/FOCS.2008.27 Kasiviswanathan, S.P., Lee, H.K., Nissim, K., Raskhodnikova, S., Smith, A.D.: What can we learn privately? In: 49th Annual IEEE Symposium on Foundations of Computer Science, FOCS 2008, Philadelphia, PA, USA, 25–28 October 2008, pp. 531–540. IEEE Computer Society (2008). https://​doi.​org/​10.​1109/​FOCS.​2008.​27
24.
Zurück zum Zitat Lindvall, T.: Lectures on the Coupling Method. Courier Corporation, Chelmsford (2002) MATH Lindvall, T.: Lectures on the Coupling Method. Courier Corporation, Chelmsford (2002) MATH
25.
Zurück zum Zitat Team, A.D.P.: Learning with privacy at scale. Apple Mach. Learn. J. 1(9) (2017) Team, A.D.P.: Learning with privacy at scale. Apple Mach. Learn. J. 1(9) (2017)
Metadaten
Titel
The Privacy Blanket of the Shuffle Model
verfasst von
Borja Balle
James Bell
Adrià Gascón
Kobbi Nissim
Copyright-Jahr
2019
DOI
https://doi.org/10.1007/978-3-030-26951-7_22

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