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.
| Original language | English |
|---|---|
| Pages (from-to) | 15097-15108 |
| Number of pages | 12 |
| Journal | Journal of Ambient Intelligence and Humanized Computing |
| Volume | 14 |
| Issue number | 11 |
| Early online date | 3 Jul 2019 |
| DOIs | |
| State | Published - Nov 2023 |
Bibliographical note
Publisher Copyright:© 2019, Springer-Verlag GmbH Germany, part of Springer Nature.
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 9 Industry, Innovation, and Infrastructure
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SDG 16 Peace, Justice and Strong Institutions
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SDG 17 Partnerships for the Goals
Keywords
- CDR privacy
- Location privacy
- Online privacy
- Stream anonymization
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