Abstract
This paper presents a fundamentally new approach to allowing learning algorithms to be applied to a dataset, while still keeping the records in the dataset confidential. Let D be the set of records to be kept private, and let E be a fixed set of records from a similar domain that is already public. The idea is to compute and publish a weight w(x) for each record x in E that measures how representative it is of the records in D. Data mining on E using these importance weights is then approximately equivalent to data mining directly on D. The dataset D is used by its owner to compute the weights, but not revealed in any other way.
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References
Blum, A., Dwork, C., McSherry, F., Nissim, K.: Practical privacy: the SuLQ framework. In: Proceedings of the 24th ACM Symposium on Principles of Database Systems, pp. 128–138. ACM Press, New York (2005)
Blum, A., Ligett, K., Roth, A.: A learning theory approach to non-interactive database privacy. In: Proceedings of the 40th Annual ACM Symposium on Theory of Computing, pp. 609–618. ACM Press, New York (2008)
Chaudhuri, K., Monteleoni, C.: Privacy-preserving logistic regression. In: Proceedings of the 22nd Annual Conference on Neural Information Processing Systems (NIPS), pp. 289–296 (2008)
Chaudhuri, K., Sarwate, A.D.: Privacy constraints in regularized convex optimization. Arxiv preprint arXiv:0907.1413 (2009)
Cortes, C., Mohri, M., Riley, M., Rostamizadeh, A.: Sample selection bias correction theory. In: Algorithmic Learning Theory, pp. 38–53. Springer, Heidelberg (2010)
Dwork, C.: Differential privacy: A survey of results. In: Agrawal, M., Du, D.-Z., Duan, Z., Li, A. (eds.) TAMC 2008. LNCS, vol. 4978, pp. 1–19. Springer, Heidelberg (2008)
Hastings, W.K.: Monte Carlo sampling methods using Markov chains and their applications. Biometrika 57(1), 97–109 (1970)
Kearns, M.: Efficient noise-tolerant learning from statistical queries. Journal of the ACM 45(6), 983–1006 (1998)
Press, W.H.: How to use Markov chain Monte Carlo to do difficult integrals (including those for normalizing constants) (2004), Draft working paper available at http://www.nr.com/whp/workingpapers.html
Scott, D.W.: Multivariate density estimation: Theory, practice, and visualization. Wiley-Interscience, Hoboken (1992)
Shimodaira, H.: Improving predictive inference under covariate shift by weighting the log-likelihood function. Journal of Statistical Planning and Inference 90(2), 227–244 (2000)
Smith, A., Elkan, C.: Making generative classifiers robust to selection bias. In: Proceedings of the SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp. 657–666. ACM Press, New York (2007)
Tsuboi, Y., Kashima, H., Bickel, S., Sugiyama, M.: Direct Density Ratio Estimation for Large-scale Covariate Shift Adaptation. Journal of Information Processing 17, 138–155 (2009)
Zadrozny, B.: Learning and evaluating classifiers under sample selection bias. In: Proceedings of the 21st International Conference on Machine Learning, pp. 903–910. ACM Press, New York (2004)
Zadrozny, B., Elkan, C.: Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers. In: Proceedings of the 18th International Conference on Machine Learning, pp. 609–616. Morgan Kaufmann, San Francisco (2001)
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Elkan, C. (2011). Preserving Privacy in Data Mining via Importance Weighting. In: Dimitrakakis, C., Gkoulalas-Divanis, A., Mitrokotsa, A., Verykios, V.S., Saygin, Y. (eds) Privacy and Security Issues in Data Mining and Machine Learning. PSDML 2010. Lecture Notes in Computer Science(), vol 6549. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19896-0_2
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DOI: https://doi.org/10.1007/978-3-642-19896-0_2
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