HAkAu: hybrid algorithm for effective k-automorphism anonymization of social networks

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F23%3A50020412" target="_blank" >RIV/62690094:18450/23:50020412 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/article/10.1007/s13278-023-01064-1" target="_blank" >https://link.springer.com/article/10.1007/s13278-023-01064-1</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s13278-023-01064-1" target="_blank" >10.1007/s13278-023-01064-1</a>

Result language

angličtina

Original language name

HAkAu: hybrid algorithm for effective k-automorphism anonymization of social networks

Original language description

Online social network datasets contain a large amount of various information about their users. Preserving users&apos; privacy while publishing or sharing datasets with third parties has become a challenging problem. The k-automorphism is the anonymization method that protects the social network dataset against any passive structural attack. It provides a higher level of protection than other k-anonymity methods, including k-degree or k-neighborhood techniques. In this paper, we propose a hybrid algorithm that effectively modifies the social network to the k-automorphism one. The proposed algorithm is based on the structure of the previously published k-automorphism KM algorithm. However, it solves the NP-hard subtask of finding isomorphic graph extensions with a genetic algorithm and employs the GraMi algorithm for finding frequent subgraphs. In the design of the genetic algorithm, we introduce the novel chromosome representation in which the length of the chromosome is independent of the size of the input network, and each individual in each generation leads to the k-automorphism solution. Moreover, we present a heuristic method for selecting the set of vertex disjoint subgraphs. To test the algorithm, we run experiments on a set of real social networks and use the SecGraph tool to evaluate our results in terms of protection against deanonymization attacks and preserving data utility. It makes our experimental results comparable with any future research.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Social Network Analysis and Mining

ISSN

1869-5450

e-ISSN

1869-5469

Volume of the periodical

13

Issue of the periodical within the volume

1

Country of publishing house

AT - AUSTRIA

Number of pages

21

Pages from-to

"Article Number: 63"

UT code for WoS article

000963155900001

EID of the result in the Scopus database

2-s2.0-85152619775