High-degree noise addition method for the κ-degree anonymization algorithm

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F20%3A50017852" target="_blank" >RIV/62690094:18450/20:50017852 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/9322670" target="_blank" >https://ieeexplore.ieee.org/document/9322670</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/SCISISIS50064.2020.9322670" target="_blank" >10.1109/SCISISIS50064.2020.9322670</a>

Result language
angličtina
Original language name
High-degree noise addition method for the κ-degree anonymization algorithm
Original language description
Social network datasets are a valuable source of information for academic researches as well as business and marketing studies. Since social network datasets contain personal and sensitive information of their users, sharing the data with a third party gives rise to many privacy-preserving issues. The k -degree anonymization was developed to protect the users of social networks from the re-identification attack by modifying the network structure with a sequence of edge editing operations. In this paper, we introduce a novel approach for noise addition operation in the well-known k -degree anonymization algorithm k -DA. We propose the high-degree noise addition method that modifies the degree sequence anonymized by the degree anonymization procedure of k -DA before it is processed by the graph construction procedure of k -DA. Our proposed method significantly reduces the number of necessary repetitions of the graph constructing algorithm and positively affects the efficiency and runtime of the whole k -DA algorithm. Moreover, we show that the proposed high-degree noise addition algorithm improves k -DA in terms of data utility. We demonstrate its usability by running experiments on 13 different real-world social network datasets.
Czech name
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Czech description
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Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
2020 Joint 11th International Conference on Soft Computing and Intelligent Systems and 21st International Symposium on Advanced Intelligent Systems (SCIS-ISIS)
ISBN
978-1-72819-732-6
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
"Article number 9322670"
Publisher name
IEEE
Place of publication
Piscataway
Event location
Hachijo Island, Japan
Event date
Dec 5, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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