Abstract
Privacy budget management plays an important role when applying differential privacy, as it sets an upper limit in the ability to utilise the private database. In this paper, we explore the possibility of extending the total allocated privacy budget, taking into consideration the data consumer characteristics and the data utilisation context. To this end, we first study the problem of privacy budget distribution in adaptive multi-data consumer differential privacy use cases. Then, we present an extension of the classic differential privacy formal model that allows taking into consideration data consumers’ information disclosure risk when distributing the privacy budget among them. Finally, we define a method that allows to optimally distribute a given privacy budget among a private database’s data consumers.
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Notes
- 1.
The rationale behind that is that the anonymization would be less relevant when the dataset does not contain personal data.
References
Adams, A.: The implications of users’ multimedia privacy perceptions on communication and information privacy policies. In: Proceedings of Telecommunications Policy Research Conference, Washington DC, p. 20 (1999)
Barak, B., Chaudhuri, K., Dwork, C., Kale, S., McSherry, F., Talwar, K.: Privacy, accuracy, and consistency too: a holistic solution to contingency table release. In: Proceedings of the Twenty-Sixth ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, pp. 273–282. ACM (2007)
Barker, K., et al.: A data privacy taxonomy. In: Sexton, A.P. (ed.) BNCOD 2009. LNCS, vol. 5588, pp. 42–54. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-02843-4_7
Chalermsook, P., Chuzhoy, J., Kannan, S., Khanna, S.: Improved hardness results for profit maximization pricing problems with unlimited supply. In: Gupta, A., Jansen, K., Rolim, J., Servedio, R. (eds.) APPROX/RANDOM -2012. LNCS, vol. 7408, pp. 73–84. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-32512-0_7
Cormode, G., Procopiuc, M., Srivastava, D., Tran, T.T.: Differentially private publication of sparse data. arXiv preprint arXiv:1103.0825 (2011)
Dankar, F.K., Badji, R.: A risk-based framework for biomedical data sharing. J. Biomed. Inform. 66, 231–240 (2017)
Dix, A.J.: Information processing, context and privacy. In: INTERACT, pp. 15–20 (1990)
Dwork, C., McSherry, F., Nissim, K., Smith, A.: Calibrating noise to sensitivity in private data analysis. In: Halevi, S., Rabin, T. (eds.) TCC 2006. LNCS, vol. 3876, pp. 265–284. Springer, Heidelberg (2006). https://doi.org/10.1007/11681878_14
Dwork, C., McSherry, F., Nissim, K., Smith, A.: Calibrating noise to sensitivity in private data analysis. In: Halevi, S., Rabin, T. (eds.) TCC 2006. LNCS, vol. 3876, pp. 265–284. Springer, Heidelberg (2006). https://doi.org/10.1007/11681878_14
Dwork, C., Roth, A., et al.: The algorithmic foundations of differential privacy. Found. Trends® Theor. Comput. Sci. 9(3–4), 211–407 (2014)
El Emam, K.: Risk-based de-identification of health data. IEEE Secur. Priv. 3, 64–67 (2010)
El Emam, K.: Guide to the De-identification of Personal Health Information. Auerbach Publications, Boca Raton (2013)
El Emam, K., Dankar, F.K., Vaillancourt, R., Roffey, T., Lysyk, M.: Evaluating the risk of re-identification of patients from hospital prescription records. Can. J. Hosp. Pharm. 62(4), 307 (2009)
Hardt, M., Talwar, K.: On the geometry of differential privacy. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing, pp. 705–714. ACM (2010)
Hay, M., Rastogi, V., Miklau, G., Suciu, D.: Boosting the accuracy of differentially private histograms through consistency. Proc. VLDB Endow. 3(1–2), 1021–1032 (2010)
Lederer, S., Dey, A.K., Mankoff, J.: A conceptual model and a metaphor of everyday privacy in ubiquitous. Technical report, Berkeley, CA, USA (2002)
Lessig, L.: The architecture of privacy. Vand. J. Ent. L. Prac. 1, 56 (1999)
Li, C., Hay, M., Rastogi, V., Miklau, G., McGregor, A.: Optimizing linear counting queries under differential privacy. In: Proceedings of the Twenty-Ninth ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, pp. 123–134. ACM (2010)
McSherry, F.D.: Privacy integrated queries: an extensible platform for privacy-preserving data analysis. In: Proceedings of the 2009 ACM SIGMOD International Conference on Management of data, pp. 19–30. ACM (2009)
Roy, I., Setty, S.T., Kilzer, A., Shmatikov, V., Witchel, E.: Airavat: security and privacy for mapreduce. NSDI 10, 297–312 (2010)
Xiao, X., Wang, G., Gehrke, J.: Differential privacy via wavelet transforms. IEEE Trans. Knowl. Data Eng. 23(8), 1200–1214 (2011)
Xu, J., Zhang, Z., Xiao, X., Yang, Y., Yu, G., Winslett, M.: Differentially private histogram publication. VLDB J. 22(6), 797–822 (2013)
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Bkakria, A., Tasidou, A., Cuppens-Boulahia, N., Cuppens, F., Bouattour, F., Ben Fredj, F. (2019). Optimal Distribution of Privacy Budget in Differential Privacy. In: Zemmari, A., Mosbah, M., Cuppens-Boulahia, N., Cuppens, F. (eds) Risks and Security of Internet and Systems. CRiSIS 2018. Lecture Notes in Computer Science(), vol 11391. Springer, Cham. https://doi.org/10.1007/978-3-030-12143-3_18
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