@inproceedings{9ca0a9ccc4f24088b2edf2461ef115bf,
title = "Inferring Private Information in Wireless Sensor Networks",
abstract = "In wireless sensor networks, estimating a global parameter from locally obtained measurements via local interactions is known as the distributed parameter estimation problem. Solving these problems often require the deployment of distributed optimization algorithms that rely on a constant exchange of information among the sensor nodes. This makes such distributed algorithms vulnerable to attackers or malicious nodes that want to gain access to private information regarding the network. Based on the sliding mode control scheme, here we present a novel approach to infer sensitive information (e.g., gradient or private parameters of the local objective function) regarding a node of interest by intercepting the communication between the nodes. The effectiveness of the proposed approach is illustrated in a representative example of distributed event localization using an acoustic sensor network.",
keywords = "Distributed parameter estimation, distributed optimization, sliding control, wireless sensor network",
author = "Burbano-L, {Daniel A.} and Jemin George and Freeman, {Randy A} and Lynch, {Kevin M}",
year = "2019",
month = may,
day = "1",
doi = "10.1109/ICASSP.2019.8683597",
language = "English (US)",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "4310--4314",
booktitle = "2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings",
address = "United States",
note = "44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 ; Conference date: 12-05-2019 Through 17-05-2019",
}