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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

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

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název statě ve sborníku

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

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

"Article number 9322670"

Název nakladatele

IEEE

Místo vydání

Piscataway

Místo konání akce

Hachijo Island, Japan

Datum konání akce

5. 12. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—