Title
Revisiting online anonymization algorithms to ensure location privacy
Date Issued
01 January 2019
Access level
metadata only access
Resource Type
journal article
Author(s)
Publisher(s)
Springer Nature
Abstract
Individuals are continually observed and monitored by many location-based services, such as social networks, telecommunication companies, mobile networks, etc. The resulting streams of data, which are usually analyzed in real time, can reveal sensitive information about individuals, e.g. home/work location or private mobility patterns. Therefore, there is a need for stream processing algorithms able to anonymize datasets in real time to ensure certain privacy guarantees, but at the same time keeping a low error. In this paper, we describe how statistical disclosure control (SDC) methods can be applied to a Call Detail Record (CDR) database in a stream fashion to mask location information efficiently. Besides, we also provide some experimental results over a real database.
Language
English
OCDE Knowledge area
Ciencias de la computación
Subjects
Scopus EID
2-s2.0-85068769433
Source
Journal of Ambient Intelligence and Humanized Computing
ISSN of the container
18685137
Sources of information:
Directorio de Producción Científica
Scopus