We initiate a study on the fundamental relation between data sanitization (i.e., the process of hiding confidential information in a given dataset) and frequent pattern mining, in the context of sequential (string) data. Current methods for string sanitization hide confidential patterns introducing, however, a number of spurious patterns that may harm the utility of frequent pattern mining. The main computational problem is to minimize this harm. Our contribution here is twofold. First, we present several hardness results, for different variants of this problem, essentially showing that these variants cannot be solved or even be approximated in polynomial time. Second, we propose integer linear programming formulations for these variants and algorithms to solve them, which work in polynomial time under certain realistic assumptions on the problem parameters.

Hide and Mine in Strings: Hardness and Algorithms / G. Bernardini, A. Conte, G. Gourdel, R. Grossi, G. Loukides, N. Pisanti, S.P. Pissis, G. Punzi, L. Stougie, M. Sweering (PROCEEDINGS IEEE INTERNATIONAL CONFERENCE ON DATA MINING). - In: 2020 IEEE International Conference on Data Mining (ICDM)[s.l] : IEEE, 2020. - ISBN 978-1-7281-8316-9. - pp. 924-929 (( Intervento presentato al 20. convegno IEEE International Conference on Data Mining, ICDM 2020 tenutosi a Sorrento nel 2020 [10.1109/icdm50108.2020.00103].

Hide and Mine in Strings: Hardness and Algorithms

G. Bernardini
Primo
;
2020

Abstract

We initiate a study on the fundamental relation between data sanitization (i.e., the process of hiding confidential information in a given dataset) and frequent pattern mining, in the context of sequential (string) data. Current methods for string sanitization hide confidential patterns introducing, however, a number of spurious patterns that may harm the utility of frequent pattern mining. The main computational problem is to minimize this harm. Our contribution here is twofold. First, we present several hardness results, for different variants of this problem, essentially showing that these variants cannot be solved or even be approximated in polynomial time. Second, we propose integer linear programming formulations for these variants and algorithms to solve them, which work in polynomial time under certain realistic assumptions on the problem parameters.
Data privacy; Data sanitization; Frequent pattern mining; Knowledge hiding; String algorithms
Settore INFO-01/A - Informatica
2020
IEEE Computer Society
Book Part (author)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/1131861
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