Abstract
This chapter presents the preliminary of differential privacy. It includes the basic concept of differential privacy, the notion of global sensitivity, local sensitivity, and principle mechanisms that can preserve differential privacy. To make the theory accessible, an example is proposed to illustrate these concepts. In addition, utility measurements are discussed in this chapter.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
A. Beimel, K. Nissim, and U. Stemmer. Private learning and sanitization: Pure vs. approximate differential privacy. CoRR, abs/1407.2674, 2014.
A. Blum, K. Ligett, and A. Roth. A learning theory approach to non-interactive database privacy. In STOC, pages 609–618, 2008.
C. Dwork. A firm foundation for private data analysis. Commun. ACM, 54(1):86–95, 2011.
C. Dwork, K. Kenthapadi, F. McSherry, I. Mironov, and M. Naor. Our data, ourselves: Privacy via distributed noise generation. In EUROCRYPT, pages 486–503, 2006.
C. Dwork, F. McSherry, K. Nissim, and A. D. Smith. Calibrating noise to sensitivity in private data analysis. In TCC, pages 265–284, 2006.
C. Dwork and A. Roth. The algorithmic foundations of differential privacy. Found. Trends Theor. Comput. Sci., 9:211–407, Aug. 2014.
A. Haeberlen, B. C. Pierce, and A. Narayan. Differential privacy under fire. 2011.
P. Kairouz, S. Oh, and P. Viswanath. The composition theorem for differential privacy. In ICML, pages 1376–1385, 2015.
F. McSherry. Privacy integrated queries: An extensible platform for privacy-preserving data analysis. Commun. ACM, 53(9), 2010.
F. McSherry and K. Talwar. Mechanism design via differential privacy. In FOCS, pages 94–103, 2007.
J. Murtagh and S. Vadhan. The complexity of computing the optimal composition of differential privacy. CoRR, abs/1507.03113, 2015.
K. Nissim, S. Raskhodnikova, and A. Smith. Smooth sensitivity and sampling in private data analysis. In STOC, pages 75–84, 2007.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this chapter
Cite this chapter
Zhu, T., Li, G., Zhou, W., Yu, P.S. (2017). Preliminary of Differential Privacy. In: Differential Privacy and Applications. Advances in Information Security, vol 69. Springer, Cham. https://doi.org/10.1007/978-3-319-62004-6_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-62004-6_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-62002-2
Online ISBN: 978-3-319-62004-6
eBook Packages: Computer ScienceComputer Science (R0)