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MetaBayes: Bayesian Meta-Interpretative Learning Using Higher-Order Stochastic Refinement Read chapter Read first chapter

2014 | OriginalPaper | Chapter

On Differentially Private Inductive Logic Programming

Authors : Chen Zeng, Eric Lantz, Jeffrey F. Naughton, David Page

Published in: Inductive Logic Programming

Publisher: Springer Berlin Heidelberg

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Abstract

We consider differentially private inductive logic programming. We begin by formulating the problem of guarantee differential privacy to inductive logic programming, and then prove the theoretical difficulty of simultaneously providing good utility and good privacy in this task. While our analysis proves that in general this is very difficult, it leaves a glimmer of hope in that when the size of the training data is large or the search tree for hypotheses is “short” and “narrow,” we might be able to get meaningful results. To prove our intuition, we implement a differentially private version of Aleph, and our experimental results show that our algorithm is able to produce accurate results for those two cases.

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previous chapter MetaBayes: Bayesian Meta-Interpretative Learning Using Higher-Order Stochastic Refinement
next chapter Learning Through Hypothesis Refinement Using Answer Set Programming
Footnotes
1
This formulation uses non-monotonic semantics.
 
2
In the differential privacy literature, databases are typically thought of as single tables. In a multi-relational setting, the proper definition of “neighboring” might change. For example, in a medical domain a neighboring database would remove one patient along with their respective prescriptions and diagnoses from other tables.
 
3
An alternative is to relax the privacy definition from \(\epsilon \)-differential privacy to (\(\epsilon ,\delta \))-differential privacy. In this context, \(\delta \) (unlike the symbol’s use in Sect. 4) refers to the probability that the algorithm violates the \(\epsilon \)-differential privacy guarantee. We do not explore it here as it makes the already burdensome utility bounds much worse.
 
Literature
1.
Muggleton, S., de Raedt, L.: Inductive logic programming: theory and methods. J. Logic Program. 19, 629–679 (1994)CrossRef
2.
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) CrossRef
3.
Williams, O., McSherry, F.: Probabilistic inference and differential privacy. In: Advances in Neural Information Processing Systems, vol. 23 (2010)
4.
Rubinstein, B.I.P., Bartlett, P.L., Huang, L., Taft, N.: Learning in a large function space: privacy-preserving mechanisms for SVM learning. CoRR (2009)
5.
Ghosh, A., Roughgarden, T., Sundararajan, M.: Universally utility-maximizing privacy mechanisms. In: STOC (2009)
6.
Dwork, C., Mcsherry, F., Nissim, K., Smith, A.: Calibrating noise to sensitivity in private data analysis. In: TCS (2006)
7.
8.
Zeng, C., Naughton, J.F., Cai, J.Y.: On differentially private frequent itemset mining. Proc. VLDB Endow. 6(1), 25–36 (2012)CrossRef
9.
Dehaspe, L., Raedt, L.D.: Mining association rules in multiple relations. In. Proceedings of the 7th International Workshop on Inductive Logic Programming (1997)
10.
Ullman, J.: Answering \(n^{2+o(1)}\) counting queries with differential privacy is hard. CoRR abs/1207.6945 (2012)
11.
12.
Muggleton, S.: Inverse entailment and progol. New Gener. Comput. 13, 245–286 (1995)CrossRef
13.
De Raedt, L.: Statistical relational learning: an inductive logic programming perspective. In: Jorge, A.M., Torgo, L., Brazdil, P.B., Camacho, R., Gama, J. (eds.) PKDD 2005. LNCS (LNAI), vol. 3721, pp. 3–5. Springer, Heidelberg (2005)
Metadata
Title
On Differentially Private Inductive Logic Programming
Authors
Chen Zeng
Eric Lantz
Jeffrey F. Naughton
David Page
Copyright Year
2014
Publisher
Springer Berlin Heidelberg
Book
Inductive Logic Programming

Print ISBN: 978-3-662-44922-6

Electronic ISBN: 978-3-662-44923-3

Copyright Year: 2014

DOI
https://doi.org/10.1007/978-3-662-44923-3_2

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