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Revisiting online anonymization algorithms to ensure location privacy

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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.

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Notes

  1. By mechanism, we refer to any kind of function or system used to query for data.

  2. A max-heap is used to keep the greatest element on top of the queue.

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Authors and Affiliations

  1. Universidad del Pacífico, Av. Salaverry 2020, Lima, Peru

    Miguel Nunez-del-Prado

  2. ESADE, Universitat Ramon Llull, Barcelona, Catalonia, Spain

    Jordi Nin

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  1. Miguel Nunez-del-Prado
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  2. Jordi Nin
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Correspondence to Jordi Nin.

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Nunez-del-Prado, M., Nin, J. Revisiting online anonymization algorithms to ensure location privacy. J Ambient Intell Human Comput 14, 15097–15108 (2023). https://doi.org/10.1007/s12652-019-01371-6

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