$P^2$ESA : privacy-preserving environmental sensor-based authentication

Krašovec, Andraž (author), Baldini, Gianmarco (author), Pejović, Veljko (author)

The presence of Internet of Things (IoT) devices in modern working and living environments is growing rapidly. The data collected in such environments enable us to model users’ behaviour and consequently identify and authenticate them. However, these data may contain information about the user’s current activity, emotional state, or other aspects that are not relevant for authentication. In this work, we employ adversarial deep learning techniques to remove privacy-revealing information from the data while keeping the authentication performance levels almost intact. Furthermore, we develop and apply various techniques to offload the computationally weak edge devices that are part of the machine learning pipeline at training and inference time. Our experiments, conducted on two multimodal IoT datasets, show that P2ESA can be efficiently deployed and trained, and with user identification rates of between 75.85% and 93.31% (c.f. 6.67% baseline), can represent a promising support solution for authentication, while simultaneously fully obfuscating sensitive information.

• Language: English
• Keywords: behavioural authentication, edge computing, ubiquitous sensing
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FRI - Faculty of Computer and Information Science
• Publication status: Published
• Publication version: Version of Record
• Year: 2025
• Number of pages: 20 str.
• Numbering: Vol. 25, iss. 15, art. 4842
• PID: 20.500.12556/RUL-171169
• UDC: 004
• ISSN on article: 1424-8220
• DOI: 10.3390/s25154842
• COBISS.SI-ID: 245655043
• Publication date in RUL: 14.08.2025

Record is a part of a journal

• Title: Sensors
• Shortened title: Sensors
• Publisher: MDPI
• ISSN: 1424-8220
• COBISS.SI-ID: 10176278

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

• Language: Slovenian
• Keywords: vedenjska avtentikacija, robno računalništvo, vseprisotno zaznavanje

Projects

• Funder: ARIS - Slovenian Research and Innovation Agency
• Project number: N2-0393
• Name: Približno računanje za prilagodljivo porazdeljeno umetno inteligenco

• Funder: ARIS - Slovenian Research and Innovation Agency
• Project number: J2-3047
• Name: Kontekstno-odvisno približno računanje na mobilnih napravah