Toward anonymizing IoT data streams via partitioning

Ankhbayar Otgonbayar, Zeeshan Pervez, Keshav Dahal

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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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)
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
CountryBrazil
CityBrasilia
Period10/10/1613/10/16
Internet address

Fingerprint

Internet of things
Experiments

Cite this

Otgonbayar, A., Pervez, Z., & Dahal, K. (2017). Toward anonymizing IoT data streams via partitioning. In 2016 IEEE 13th International Conference on Mobile Ad Hoc and Sensor Systems (MASS) IEEE. https://doi.org/10.1109/MASS.2016.049
Otgonbayar, Ankhbayar ; Pervez, Zeeshan ; Dahal, Keshav. / Toward anonymizing IoT data streams via partitioning. 2016 IEEE 13th International Conference on Mobile Ad Hoc and Sensor Systems (MASS). IEEE, 2017.
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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.",
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Otgonbayar, A, Pervez, Z & Dahal, K 2017, Toward anonymizing IoT data streams via partitioning. in 2016 IEEE 13th International Conference on Mobile Ad Hoc and Sensor Systems (MASS). IEEE, International Conference on Mobile Ad-Hoc and Smart Systems, Brasilia, Brazil, 10/10/16. https://doi.org/10.1109/MASS.2016.049

Toward anonymizing IoT data streams via partitioning. / Otgonbayar, Ankhbayar; Pervez, Zeeshan; Dahal, Keshav.

2016 IEEE 13th International Conference on Mobile Ad Hoc and Sensor Systems (MASS). IEEE, 2017.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Otgonbayar A, Pervez Z, Dahal K. Toward anonymizing IoT data streams via partitioning. In 2016 IEEE 13th International Conference on Mobile Ad Hoc and Sensor Systems (MASS). IEEE. 2017 https://doi.org/10.1109/MASS.2016.049