Abstract
Privacy-preserving data publishing is an important problem that has been the focus of extensive study. The state-of-the-art privacy model for this problem is differential privacy, which offers a strong degree of privacy protection without making restrictive assumptions about the adversary. In this paper, we review two methods, Privelet and PrivBayes, for improving utility in differentially private data publishing. Privelet utilizes wavelet transforms to ensure that any range-count query can be answered with noise variance that is polylogarithmic to the size of the input data domain. Meanwhile, PrivBayes employs Bayesian networks to publish high-dimensional datasets without incurring prohibitive computation overheads or excessive noise injection.
Material based on [17] and [18] appearing in TKDE and SIGMOD’14, respectively.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Sarwate, A.D., Chaudhuri, K.: Signal processing and machine learning with differential privacy: theory, algorithms, and challenges (September 2013)
Backstrom, L., Dwork, C., Kleinberg, J.M.: Wherefore art thou r3579x?: anonymized social networks, hidden patterns, and structural steganography, pp. 181–190 (2007)
Barbaro, M., Zeller, T.: A face is exposed for AOL searcher no. 4417749. New York Times, August 9 (2006)
Calandrino, J.A., Kilzer, A., Narayanan, A., Felten, E.W., Shmatikov, V.: “You might also like:” privacy risks of collaborative filtering. In: IEEE Symposium on Security and Privacy, pp. 231–246 (2011)
Chakrabarti, K., Garofalakis, M.N., Rastogi, R., Shim, K.: Approximate query processing using wavelets 10(2-3), 199–223 (2001)
Dwork, C.: Differential privacy. In: Bugliesi, M., Preneel, B., Sassone, V., Wegener, I. (eds.) ICALP 2006. LNCS, vol. 4052, pp. 1–12. Springer, Heidelberg (2006)
Dwork, C.: Differential privacy in new settings. In: SODA, pp. 174–183 (2010)
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)
Fung, B.C.M., Wang, K., Chen, R., Yu, P.S.: Privacy-preserving data publishing: A survey of recent developments. ACM Comput. Surv. 42(4) (2010)
Koller, D., Friedman, N.: Probabilistic Graphical Models: Principles and Techniques. MIT Press (2009)
Narayanan, A., Paskov, H., Gong, N.Z., Bethencourt, J., Stefanov, E., Shin, E.C.R., Song, D.: On the feasibility of internet-scale author identification. In: IEEE Symposium on Security and Privacy, pp. 300–314 (2012)
Narayanan, A., Shmatikov, V.: Robust de-anonymization of large sparse datasets. In: IEEE Symposium on Security and Privacy, pp. 111–125 (2008)
Narayanan, A., Shmatikov, V.: De-anonymizing social networks. In: IEEE Symposium on Security and Privacy, pp. 173–187 (2009)
Srivatsa, M., Hicks, M.: Deanonymizing mobility traces: using social network as a side-channel. In: ACM Conference on Computer and Communications Security, pp. 628–637 (2012)
Stollnitz, E.J., Derose, T.D., Salesin, D.H.: Wavelets for computer graphics: theory and applications. Morgan Kaufmann Publishers Inc. (1996)
Sweeney, L.: k-anonymity: A model for protecting privacy. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 10(5), 557–570 (2002)
Xiao, X., Wang, G., Gehrke, J.: Differential privacy via wavelet transforms. TKDE 23(8), 1200–1214 (2011)
Zhang, J., Cormode, G., Procopiuc, C.M., Srivastava, D., Xiao, X.: Privbayes: Private data release via bayesian networks. In: SIGMOD (2014)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Xiao, X. (2014). Differentially Private Data Release: Improving Utility with Wavelets and Bayesian Networks. In: Chen, L., Jia, Y., Sellis, T., Liu, G. (eds) Web Technologies and Applications. APWeb 2014. Lecture Notes in Computer Science, vol 8709. Springer, Cham. https://doi.org/10.1007/978-3-319-11116-2_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-11116-2_3
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11115-5
Online ISBN: 978-3-319-11116-2
eBook Packages: Computer ScienceComputer Science (R0)