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

Giulia Bernardini, Alessio Conte, Garance Gourdel, Roberto Grossi, Grigorios Loukides, Nadia Pisanti, Solon Pissis, Giulia Punzi, Leen Stougie, Michelle Sweering

Research output: Contribution to JournalArticleAcademicpeer-review


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.

Original languageEnglish
Pages (from-to)5948-5963
JournalIEEE Transactions on Knowledge and Data Engineering
Issue number6
Publication statusPublished - 2023

Bibliographical note

Publisher Copyright:


  • Bioinformatics
  • Data integrity
  • Data mining
  • Data privacy
  • Data sanitization
  • DNA
  • Frequent pattern mining
  • Genomics
  • Knowledge hiding
  • Privacy
  • Resists
  • String algorithms


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