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2022 | OriginalPaper | Chapter

7. Change in Formalism (F)

Authors : Balázs Pejó, Damien Desfontaines

Published in: Guide to Differential Privacy Modifications

Publisher: Springer International Publishing

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Abstract

Differential privacy uses indistinguishability to compare the distribution of outputs given two neighboring inputs. Other formalisms have been proposed, some of which model the attacker more explicitly. This chapter of the Brief provides an overview of the corresponding notions.

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previous chapter Background Knowledge (B)
next chapter Knowledge Gain Relativization (R)
Footnotes
1
Note that the original formalization used in [8] was more abstract, and it used polynomially bounded adversaries what we introduce in Chap. 9.
 
2
Another definition with the same name is introduced in [10], we mention it in Chap. 11 .
 
3
Another definition with the same name is introduced in [15], we mention it later in this chapter.
 
4
Another definition with the same name is introduced in [14], we mention it earlier in this chapter.
 
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Kairouz, P., Oh, S., Viswanath, P.: The composition theorem for differential privacy. IEEE Trans. Inf. Theory (2017)
3.
Dong, J., Roth, A., Su, W.: Gaussian differential privacy. J. R. Stat. Soc. (2021)
4.
Asoodeh, S., Liao, J., Calmon, F.P., Kosut, O., Sankar, L.: Three variants of differential privacy: lossless conversion and applications. IEEE J. Sel. Areas Inf. Theory 2(1), 208–222 (2021)
5.
Kaissis, G., Knolle, M., Jungmann, F., Ziller, A., Usynin, D., Rueckert, D.: A unified interpretation of the gaussian mechanism for differential privacy through the sensitivity index. arXiv preprintarXiv:​2109.​10528 (2021)
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Zheng, Q., Chen, S., Long, Q., Su, W.: Federated f-differential privacy. In: International Conference on Artificial Intelligence and Statistics, pp. 2251–2259. PMLR (2021)
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Brendan McMahan, H., et al.: Advances and open problems in federated learning. Found. Trends Mach. Learn. 14(1) (2021)
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Liu, J., Xiong, L., Luo, J.: Semantic security: privacy definitions revisited. Trans. Data Privacy (2013)
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Li, N., Qardaji, W., Su, D., Wu, Y., Yang, W.: Membership privacy: a unifying framework for privacy definitions. In: Proceedings of the 2013 ACM SIGSAC Conference on Computer & Communications Security. ACM (2013)
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Sablayrolles, A., Douze, M., Schmid, C., Ollivier, Y., Jégou, H.: White-box vs black-box: Bayes optimal strategies for membership inference. In: International Conference on Machine Learning, pp. 5558–5567. PMLR (2019)
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Dong, K., Guo, T., Ye, H., Li, X., Ling, Z.: On the limitations of existing notions of location privacy. Future Gener. Comput. Syst. (2018)
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Dong, K., Tao, Z., Xia, X., Chen, Z., Yang, M.: Preserving privacy for discrete location information. In: 2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design (CSCWD), pp. 360–365. IEEE (2021)
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Rastogi, V., Hay, M., Miklau, G., Suciu, D.: Relationship privacy: output perturbation for queries with joins. In: Proceedings of the Twenty-Eighth ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems
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du Pin Calmon, F., Fawaz, N.: Privacy against statistical inference. In: 2012 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton). IEEE (2012)
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Bhaskar, R., Bhowmick, A., Goyal, V., Laxman, S., Thakurta, A.: Noiseless database privacy. In: International Conference on the Theory and Application of Cryptology and Information Security. Springer (2011)
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Ganta, S.R., Kasiviswanathan, S.P., Smith, A.: Composition attacks and auxiliary information in data privacy. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM (2008)
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Kasiviswanathan, S.P., Smith, A.: On the ‘semantics’ of differential privacy: a bayesian formulation. J. Privacy Confid. (2014)
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Dwork, C.: Differential privacy. In: Proceedings of the 33rd International Conference on Automata, Languages and Programming. ACM (2006)
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Ghosh, A., Kleinberg, R.: Inferential privacy guarantees for differentially private mechanisms. In: 8th Innovations in Theoretical Computer Science Conference (ITCS 2017). Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik (2017)
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Durfee, D., Rogers, R.M.: Practical differentially private top-k selection with pay-what-you-get composition. In: Advances in Neural Information Processing Systems (2019)
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Desfontaines, D., Mohammadi, E., Krahmer, E., Basin, D.: Differential privacy with partial knowledge. arXiv preprintarXiv:​1905.​00650 (2019)
Metadata
Title
Change in Formalism (F)
Authors
Balázs Pejó
Damien Desfontaines
Copyright Year
2022
Book
Guide to Differential Privacy Modifications

Print ISBN: 978-3-030-96397-2

Electronic ISBN: 978-3-030-96398-9

Copyright Year: 2022

DOI
https://doi.org/10.1007/978-3-030-96398-9_7

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