Anonymization of geosocial network data by the (k, l)-degree method with location entropy edge selection

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F20%3A50016937" target="_blank" >RIV/62690094:18450/20:50016937 - isvavai.cz</a>

Výsledek na webu

<a href="https://dl.acm.org/doi/pdf/10.1145/3407023.3409184" target="_blank" >https://dl.acm.org/doi/pdf/10.1145/3407023.3409184</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/3407023.3409184" target="_blank" >10.1145/3407023.3409184</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Anonymization of geosocial network data by the (k, l)-degree method with location entropy edge selection

Popis výsledku v původním jazyce

Geosocial networks (GSNs) have become an important branch of location-based services since sharing information among friends is the additional feature to provide information based on the user&apos;s current location. The growing popularity of location-based services contribute to the development of highly customized and flexible utilities. However, providing customized services relates to collecting and storing a large amount of users&apos; information. In this paper, we focus on the privacy-preserving concern in publishing GSN datasets. We introduce a new (k, l)-degree anonymization method to prevent the re-identification attack in the published GSN dataset. The presented method anonymizes users&apos; social relationships as well as location-based information in GSN. We propose the new (k, l)-degree anonymization algorithm which modifies the network structure with a sequence of edge editing operations. GSN is newly represented by the combination of social network describing social ties between users and affiliation network linking users with their checked-in locations. Furthermore, we innovatively use the location entropy metric in the proposed GSN anonymization method. The location entropy measures the importance of the visited locations in the edge selection procedure of the (k, l)-degree anonymization algorithm. We explore the usability of the algorithm by running experiments on real-world geosocial network datasets, Gowalla and Brightkite.

Název v anglickém jazyce

Anonymization of geosocial network data by the (k, l)-degree method with location entropy edge selection

Popis výsledku anglicky

Geosocial networks (GSNs) have become an important branch of location-based services since sharing information among friends is the additional feature to provide information based on the user&apos;s current location. The growing popularity of location-based services contribute to the development of highly customized and flexible utilities. However, providing customized services relates to collecting and storing a large amount of users&apos; information. In this paper, we focus on the privacy-preserving concern in publishing GSN datasets. We introduce a new (k, l)-degree anonymization method to prevent the re-identification attack in the published GSN dataset. The presented method anonymizes users&apos; social relationships as well as location-based information in GSN. We propose the new (k, l)-degree anonymization algorithm which modifies the network structure with a sequence of edge editing operations. GSN is newly represented by the combination of social network describing social ties between users and affiliation network linking users with their checked-in locations. Furthermore, we innovatively use the location entropy metric in the proposed GSN anonymization method. The location entropy measures the importance of the visited locations in the edge selection procedure of the (k, l)-degree anonymization algorithm. We explore the usability of the algorithm by running experiments on real-world geosocial network datasets, Gowalla and Brightkite.

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

Proceedings of the 15th International Conference on Availability, Reliability and Security

ISBN

978-1-4503-8833-7

ISSN

—

e-ISSN

—

Počet stran výsledku

8

Strana od-do

1-8

Název nakladatele

Association of computing machinery

Místo vydání

New York

Místo konání akce

Virtual Event, Ireland

Datum konání akce

25. 8. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—