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String data are often disseminated to support applications such aslocation-based service provision or DNA sequence analysis. This dissemination,however, may expose sensitive patterns that model confidential knowledge. Inthis paper, we consider the problem of sanitizing a string by concealing theoccurrences of sensitive patterns, while maintaining data utility, in twosettings that are relevant to many common string processing tasks.  In the first setting, we aim to generate the minimal-length string thatpreserves the order of appearance and frequency of all non-sensitive patterns.Such a string allows accurately performing tasks based on the sequential natureand pattern frequencies of the string. To construct such a string, we propose atime-optimal algorithm, TFS-ALGO. We also propose another time-optimalalgorithm, PFS-ALGO, which preserves a partial order of appearance ofnon-sensitive patterns but produces a much shorter string that can be analyzedmore efficiently. The strings produced by either of these algorithms areconstructed by concatenating non-sensitive parts of the input string. However,it is possible to detect the sensitive patterns by ``reversing'' theconcatenation operations. In response, we propose a heuristic, MCSR-ALGO, whichreplaces letters in the strings output by the algorithms with carefullyselected letters, so that sensitive patterns are not reinstated, implausiblepatterns are not introduced, and occurrences of spurious patterns areprevented. In the second setting, we aim to generate a string that is atminimal edit distance from the original string, in addition to preserving theorder of appearance and frequency of all non-sensitive patterns. To constructsuch a string, we propose an algorithm, ETFS-ALGO, based on solving specificinstances of approximate regular expression matching.

Combinatorial Algorithms for String Sanitization