Toward anonymizing IoT data streams via partitioning

Ankhbayar Otgonbayar, Zeeshan Pervez, Keshav Dahal

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    25 Citations (Scopus)
    298 Downloads (Pure)

    Abstract

    Internet-of-Things (IoT) devices are capable of capturing physiological measures, location and activity information, hence sharing sensed data can lead to privacy implications. Data anonymization provides solution to this problem, however, traditional anonymization approaches only provide privacy protection for data stream generated from a single entity. Since, a single entity can make use of multiple IoT devices at an instance, IoT data streams are not fixed in nature. As conventional data stream anonymization algorithms only work on fixed width data stream they cannot be applied to IoT. In this work, we propose an anonymization algorithm for publishing IoT data streams. Our approach anonymizes tuples with similar description in a single cluster under time based sliding window. It considers similarity of tuples when clustering, and provides solution to anonymize tuples with missing values using representative values. Our experiment on real dataset shows that the proposed algorithm publishes data with less information loss and runs faster compared to conventional anonymization approaches modified to run for IoT data streams.
    Original languageEnglish
    Title of host publication2016 IEEE 13th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)
    Place of PublicationPiscataway, NJ
    PublisherIEEE
    Number of pages6
    ISBN (Electronic)9781509028337
    ISBN (Print)9781509028344
    DOIs
    Publication statusPublished - 16 Jan 2017
    EventInternational Conference on Mobile Ad-Hoc and Smart Systems - Brasilia, Brazil
    Duration: 10 Oct 201613 Oct 2016
    Conference number: 13
    http://www.ene.unb.br/mass2016/

    Publication series

    Name
    PublisherIEEE
    ISSN (Electronic)2155-6814

    Conference

    ConferenceInternational Conference on Mobile Ad-Hoc and Smart Systems
    Abbreviated titleMASS 2016
    Country/TerritoryBrazil
    CityBrasilia
    Period10/10/1613/10/16
    Internet address

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