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String Sanitization Under Edit Distance.

Authors: G. Bernardini (Giulia); G. Loukides (Grigorios); S. Pissis (Solon); M.J.M. Sweering (Michelle); H. Chen (Huiping); N. Pisanti (Nadia); L. Stougie (Leen);

String Sanitization Under Edit Distance.

Abstract

Let W be a string of length n over an alphabet Σ, k be a positive integer, and be a set of length-k substrings of W. The ETFS problem asks us to construct a string X_{ED} such that: (i) no string of occurs in X_{ED}; (ii) the order of all other length-k substrings over Σ is the same in W and in X_{ED}; and (iii) X_{ED} has minimal edit distance to W. When W represents an individual’s data and represents a set of confidential substrings, algorithms solving ETFS can be applied for utility-preserving string sanitization [Bernardini et al., ECML PKDD 2019]. Our first result here is an algorithm to solve ETFS in (kn2) time, which improves on the state of the art [Bernardini et al., arXiv 2019] by a factor of |Σ|. Our algorithm is based on a non-trivial modification of the classic dynamic programming algorithm for computing the edit distance between two strings. Notably, we also show that ETFS cannot be solved in (n^{2-δ}) time, for any δ>0, unless the strong exponential time hypothesis is false. To achieve this, we reduce the edit distance problem, which is known to admit the same conditional lower bound [Bringmann and Künnemann, FOCS 2015], to ETFS.

Countries
Germany, Italy, Italy, Italy, Italy, Italy, Netherlands
Keywords

String algorithms; data sanitization; edit distance; dynamic programming; conditional lower bound, String algorithms, dynamic programming, Edit distance, Conditional lower bound, edit distance, [INFO] Computer Science [cs], data sanitization, Dynamic programming, 004, String algorithm, Conditional lower bound; Data sanitization; Dynamic programming; Edit distance; String algorithms;, conditional lower bound, Data sanitization, Conditional lower bound; Data sanitization; Dynamic programming; Edit distance; String algorithms

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
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