Composition attack against social network data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F18%3A50014555" target="_blank" >RIV/62690094:18450/18:50014555 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0167404818300051" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0167404818300051</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.cose.2018.01.002" target="_blank" >10.1016/j.cose.2018.01.002</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Composition attack against social network data

Popis výsledku v původním jazyce

The importance of social networks is growing with the fast development of social network technologies and the steady growth in their user communities. Given that the collection of data from social networks is essential for academic research and commercial applications, the prevention of leakage of sensitive information has become very crucial. The majority of anonymization techniques are focused on the threats associated with publishing one social network dataset. As most Internet users participate in more than one social network, a user&apos;s records are likely to appear in two published social network datasets. The level of anonymity of each dataset may present only a small security risk; however, there is no guarantee that a combination of the two datasets has the same level of anonymity. An attack on the privacy of an individual using two published datasets containing his/her records is called a composition attack. The composition attack was recently investigated as a threat to two relational datasets; however, it has not yet been considered as a potential danger to two datasets containing social network data. The novel contribution of this paper is that the composition attack is applied to anonymized social network data. A new algorithm for the composition attack is proposed and its usability is demonstrated with experiments using pairs of synthetic scale-free networks substituting real social networks. (C) 2018 Elsevier Ltd. All rights reserved.

Název v anglickém jazyce

Composition attack against social network data

Popis výsledku anglicky

The importance of social networks is growing with the fast development of social network technologies and the steady growth in their user communities. Given that the collection of data from social networks is essential for academic research and commercial applications, the prevention of leakage of sensitive information has become very crucial. The majority of anonymization techniques are focused on the threats associated with publishing one social network dataset. As most Internet users participate in more than one social network, a user&apos;s records are likely to appear in two published social network datasets. The level of anonymity of each dataset may present only a small security risk; however, there is no guarantee that a combination of the two datasets has the same level of anonymity. An attack on the privacy of an individual using two published datasets containing his/her records is called a composition attack. The composition attack was recently investigated as a threat to two relational datasets; however, it has not yet been considered as a potential danger to two datasets containing social network data. The novel contribution of this paper is that the composition attack is applied to anonymized social network data. A new algorithm for the composition attack is proposed and its usability is demonstrated with experiments using pairs of synthetic scale-free networks substituting real social networks. (C) 2018 Elsevier Ltd. All rights reserved.

Druh

J_imp - Článek v periodiku v databázi Web of Science

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

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název periodika

COMPUTERS &amp; SECURITY

ISSN

0167-4048

e-ISSN

—

Svazek periodika

74

Číslo periodika v rámci svazku

May

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

15

Strana od-do

115-129

Kód UT WoS článku

000428098500007

EID výsledku v databázi Scopus

2-s2.0-85041393771