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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Vydáno v:IEEE transactions on knowledge and data engineering Ročník 35; číslo 6; s. 5948 - 5963
Hlavní autoři: Bernardini, Giulia, Conte, Alessio, Gourdel, Garance, Grossi, Roberto, Loukides, Grigorios, Pisanti, Nadia, Pissis, Solon P., Punzi, Giulia, Stougie, Leen, Sweering, Michelle
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.06.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Institute of Electrical and Electronics Engineers
Témata:
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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Popis
Shrnutí: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.
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ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2022.3158063