Hide and Mine in Strings: Hardness, Algorithms, and Experiments

Data sanitization and frequent pattern mining are two well-studied topics in data mining. Data sanitization is the process of disguising (hiding) confidential information in a given dataset. Typically, this process incurs some utility loss that should be minimized. Frequent pattern mining is the pro...

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Published in:	IEEE transactions on knowledge and data engineering Vol. 35; no. 6; pp. 5948 - 5963
Main Authors:	Bernardini, Giulia, Conte, Alessio, Gourdel, Garance, Grossi, Roberto, Loukides, Grigorios, Pisanti, Nadia, Pissis, Solon P., Punzi, Giulia, Stougie, Leen, Sweering, Michelle
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.06.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Institute of Electrical and Electronics Engineers
Subjects:	Algorithms Bioinformatics Computer Science Context Data integrity Data mining Data privacy data sanitization Datasets DNA frequent pattern mining Genomics Greedy algorithms Hardness Integer programming knowledge hiding Linear programming Pattern analysis Polynomials Privacy Resists string algorithms Strings
ISSN:	1041-4347, 1558-2191
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Abstract	Data sanitization and frequent pattern mining are two well-studied topics in data mining. Data sanitization is the process of disguising (hiding) confidential information in a given dataset. Typically, this process incurs some utility loss that should be minimized. Frequent pattern mining is the process of obtaining all patterns occurring frequently enough in a given dataset. Our work initiates a study on the fundamental relation between data sanitization and frequent pattern mining in the context of sequential (string) data. Current methods for string sanitization hide confidential patterns. This, however, may lead to spurious patterns that harm the utility of frequent pattern mining. The main computational problem is to minimize this harm. Our contribution here is as follows. 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 realistic assumptions on the input parameters. We also complement the integer linear programming algorithms with a greedy heuristic. Third, we present an extensive experimental study, using both synthetic and real-world datasets, that demonstrates the effectiveness and efficiency of our methods. Beyond sanitization, the process of missing value replacement may also lead to spurious patterns. Interestingly, our results apply in this context as well. We show that, unlike popular approaches, our methods can fill missing values in genomic sequences, while preserving the accuracy of frequent pattern mining.
AbstractList	Data sanitization and frequent pattern mining are two well-studied topics in data mining. Data sanitization is the process of disguising (hiding) confidential information in a given dataset. Typically, this process incurs some utility loss that should be minimized. Frequent pattern mining is the process of obtaining all patterns occurring frequently enough in a given dataset. Our work initiates a study on the fundamental relation between data sanitization and frequent pattern mining in the context of sequential (string) data. Current methods for string sanitization hide confidential patterns. This, however, may lead to spurious patterns that harm the utility of frequent pattern mining. The main computational problem is to minimize this harm. Our contribution here is as follows. 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 realistic assumptions on the input parameters. We also complement the integer linear programming algorithms with a greedy heuristic. Third, we present an extensive experimental study, using both synthetic and real-world datasets, that demonstrates the effectiveness and efficiency of our methods. Beyond sanitization, the process of missing value replacement may also lead to spurious patterns. Interestingly, our results apply in this context as well. We show that, unlike popular approaches, our methods can fill missing values in genomic sequences, while preserving the accuracy of frequent pattern mining. Data sanitization and frequent pattern mining are two well-studied topics in data mining. Our work initiates a study on the fundamental relation between data sanitization and frequent pattern mining in the context of sequential (string) data. Current methods for string sanitization hide confidential patterns. This, however, may lead to spurious patterns that harm the utility of frequent pattern mining. The main computational problem is to minimize this harm. Our contribution here is as follows. 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 realistic assumptions on the input parameters. We complement the integer linear programming algorithms with a greedy heuristic. Third, we present an extensive experimental study, using both synthetic and real-world datasets, that demonstrates the effectiveness and efficiency of our methods. Beyond sanitization, the process of missing value replacement may also lead to spurious patterns. Interestingly, our results apply in this context as well.
Author	Conte, Alessio Sweering, Michelle Pissis, Solon P. Bernardini, Giulia Punzi, Giulia Gourdel, Garance Grossi, Roberto Loukides, Grigorios Stougie, Leen Pisanti, Nadia
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Snippet	Data sanitization and frequent pattern mining are two well-studied topics in data mining. Data sanitization is the process of disguising (hiding) confidential... Data sanitization and frequent pattern mining are two well-studied topics in data mining. Our work initiates a study on the fundamental relation between data...
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SubjectTerms	Algorithms Bioinformatics Computer Science Context Data integrity Data mining Data privacy data sanitization Datasets DNA frequent pattern mining Genomics Greedy algorithms Hardness Integer programming knowledge hiding Linear programming Pattern analysis Polynomials Privacy Resists string algorithms Strings
Title	Hide and Mine in Strings: Hardness, Algorithms, and Experiments
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