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

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

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

Original language description

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.

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)

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2020

Confidentiality

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

Article name in the collection

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

ISBN

978-1-4503-8833-7

ISSN

—

e-ISSN

—

Number of pages

8

Pages from-to

1-8

Publisher name

Association of computing machinery

Place of publication

New York

Event location

Virtual Event, Ireland

Event date

Aug 25, 2020

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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