Membership Inference Attacks Against Indoor Location Models

Vahideh Moghtadaiee, Amir Fathalizadeh, Mina Alishahi

2024

Abstract

With the widespread adoption of location-based services and the increasing demand for indoor positioning systems, the need to protect indoor location privacy has become crucial. One metric used to assess a dataset’s resistance against leaking individuals’ information is the Membership Inference Attack (MIA). In this paper, we provide a comprehensive examination of MIA on indoor location privacy, evaluating their effectiveness in extracting sensitive information about individuals’ locations. We investigate the vulnerability of indoor location datasets under white-box and black-box attack settings. Additionally, we analyze MIA results after employing Differential Privacy (DP) to privatize the original indoor location training data. Our findings demonstrate that DP can act as a defense mechanism, especially against black-box MIA, reducing the efficiency of MIA on indoor location models. We conduct extensive experimental tests on three real-world indoor localization datasets to assess MIA in terms of the model architecture, the nature of the data, and the specific characteristics of the training datasets.

Paper Citation

in Harvard Style

Moghtadaiee V., Fathalizadeh A. and Alishahi M. (2024). Membership Inference Attacks Against Indoor Location Models. In Proceedings of the 21st International Conference on Security and Cryptography - Volume 1: SECRYPT; ISBN 978-989-758-709-2, SciTePress, pages 584-591. DOI: 10.5220/0012863100003767

in Bibtex Style

@conference{secrypt24,
author={Vahideh Moghtadaiee and Amir Fathalizadeh and Mina Alishahi},
title={Membership Inference Attacks Against Indoor Location Models},
booktitle={Proceedings of the 21st International Conference on Security and Cryptography - Volume 1: SECRYPT},
year={2024},
pages={584-591},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012863100003767},
isbn={978-989-758-709-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 21st International Conference on Security and Cryptography - Volume 1: SECRYPT
TI - Membership Inference Attacks Against Indoor Location Models
SN - 978-989-758-709-2
AU - Moghtadaiee V.
AU - Fathalizadeh A.
AU - Alishahi M.
PY - 2024
SP - 584
EP - 591
DO - 10.5220/0012863100003767
PB - SciTePress