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Authors: Andrei Kazlouski 1 ; 2 ; Thomas Marchioro 1 ; 2 and Evangelos Markatos 1 ; 2

Affiliations: 1 Computer Science Department, University of Crete, Greece ; 2 Institute of Computer Science, Foundation for Research and Technology Hellas, Greece

Keyword(s): Privacy, De-anonymization, Data Inference, Wearable Devices.

Abstract: Recently, there has been a significant surge of lifelogging experiments, where the activity of few participants is monitored for a number of days through fitness trackers. Data from such experiments can be aggregated in datasets and released to the research community. To protect the privacy of the participants, fitness datasets are typically anonymized by removing personal identifiers such as names, e-mail addresses, etc. However, although seemingly correct, such straightforward approaches are not sufficient. In this paper we demonstrate how an adversary can still de-anonymize individuals in lifelogging datasets. We show that users’ privacy can be compromised by two approaches: (i) through the inference of physical parameters such as gender, height, and weight; and/or (ii) via the daily routine of participants. Both methods rely solely on fitness data such as steps, burned calories, and covered distance to obtain insights on the users in the dataset. We train several inference models , and leverage them to de-anonymize users in public lifelogging datasets. Between our two approaches we achieve 93.5% re-identification rate of participants. Furthermore, we reach 100% success rate for people with highly distinct physical attributes (e.g., very tall, overweight, etc.). (More)

CC BY-NC-ND 4.0

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Paper citation in several formats:
Kazlouski, A.; Marchioro, T. and Markatos, E. (2022). What your Fitbit Says about You: De-anonymizing Users in Lifelogging Datasets. In Proceedings of the 19th International Conference on Security and Cryptography - SECRYPT; ISBN 978-989-758-590-6; ISSN 2184-7711, SciTePress, pages 341-348. DOI: 10.5220/0011268600003283

@conference{secrypt22,
author={Andrei Kazlouski. and Thomas Marchioro. and Evangelos Markatos.},
title={What your Fitbit Says about You: De-anonymizing Users in Lifelogging Datasets},
booktitle={Proceedings of the 19th International Conference on Security and Cryptography - SECRYPT},
year={2022},
pages={341-348},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011268600003283},
isbn={978-989-758-590-6},
issn={2184-7711},
}

TY - CONF

JO - Proceedings of the 19th International Conference on Security and Cryptography - SECRYPT
TI - What your Fitbit Says about You: De-anonymizing Users in Lifelogging Datasets
SN - 978-989-758-590-6
IS - 2184-7711
AU - Kazlouski, A.
AU - Marchioro, T.
AU - Markatos, E.
PY - 2022
SP - 341
EP - 348
DO - 10.5220/0011268600003283
PB - SciTePress