@article{48063,
abstract = {{Brainwaves have demonstrated to be unique enough across individuals to be useful as biometrics. They also provide promising advantages over traditional means of authentication, such as resistance to external observability, revocability, and intrinsic liveness detection. However, most of the research so far has been conducted with expensive, bulky, medical-grade helmets, which offer limited applicability for everyday usage. With the aim to bring brainwave authentication and its benefits closer to real world deployment, we investigate brain biometrics with consumer devices. We conduct a comprehensive measurement experiment and user study that compare five authentication tasks on a user sample up to 10 times larger than those from previous studies, introducing three novel techniques based on cognitive semantic processing. Furthermore, we apply our analysis on high-quality open brainwave data obtained with a medical-grade headset, to assess the differences. We investigate both the performance, security, and usability of the different options and use this evidence to elicit design and research recommendations. Our results show that it is possible to achieve Equal Error Rates as low as 7.2% (a reduction between 68–72% with respect to existing approaches) based on brain responses to images with current inexpensive technology. We show that the common practice of testing authentication systems only with known attacker data is unrealistic and may lead to overly optimistic evaluations. With regard to adoption, users call for simpler devices, faster authentication, and better privacy.
}},
author = {{Arias-Cabarcos, Patricia and Fallahi, Matin and Habrich, Thilo and Schulze, Karen and Becker, Christian and Strufe, Thorsten}},
issn = {{2471-2566}},
journal = {{ACM Transactions on Privacy and Security}},
keywords = {{Safety, Risk, Reliability and Quality, General Computer Science}},
number = {{3}},
pages = {{1--36}},
publisher = {{Association for Computing Machinery (ACM)}},
title = {{{Performance and Usability Evaluation of Brainwave Authentication Techniques with Consumer Devices}}},
doi = {{10.1145/3579356}},
volume = {{26}},
year = {{2023}},
}
@article{48058,
author = {{Winkel, Fabian and Deuse-Kleinsteuber, Johannes and Böcker, Joachim}},
issn = {{0018-9529}},
journal = {{IEEE Transactions on Reliability}},
keywords = {{Electrical and Electronic Engineering, Safety, Risk, Reliability and Quality}},
pages = {{1--14}},
publisher = {{Institute of Electrical and Electronics Engineers (IEEE)}},
title = {{{Run-to-Failure Relay Dataset for Predictive Maintenance Research With Machine Learning}}},
doi = {{10.1109/tr.2023.3255786}},
year = {{2023}},
}
@article{46487,
author = {{Zander, K.K. and Nguyen, D. and Mirbabaie, Milad and Garnett, S.T.}},
issn = {{2212-4209}},
journal = {{International Journal of Disaster Risk Reduction}},
keywords = {{Geology, Safety Research, Geotechnical Engineering and Engineering Geology, Building and Construction}},
publisher = {{Elsevier BV}},
title = {{{Aware but not prepared: understanding situational awareness during the century flood in Germany in 2021}}},
doi = {{10.1016/j.ijdrr.2023.103936}},
volume = {{96}},
year = {{2023}},
}
@article{31844,
abstract = {{Encrypting data before sending it to the cloud ensures data confidentiality but requires the cloud to compute on encrypted data. Trusted execution environments, such as Intel SGX enclaves, promise to provide a secure environment in which data can be decrypted and then processed. However, vulnerabilities in the executed program give attackers ample opportunities to execute arbitrary code inside the enclave. This code can modify the dataflow of the program and leak secrets via SGX side channels. Fully homomorphic encryption would be an alternative to compute on encrypted data without data leaks. However, due to its high computational complexity, its applicability to general-purpose computing remains limited. Researchers have made several proposals for transforming programs to perform encrypted computations on less powerful encryption schemes. Yet current approaches do not support programs making control-flow decisions based on encrypted data.
We introduce the concept of
dataflow authentication
(DFAuth) to enable such programs. DFAuth prevents an adversary from arbitrarily deviating from the dataflow of a program. Our technique hence offers protections against the side-channel attacks described previously. We implemented two flavors of DFAuth, a Java bytecode-to-bytecode compiler, and an SGX enclave running a small and program-independent trusted code base. We applied DFAuth to a neural network performing machine learning on sensitive medical data and a smart charging scheduler for electric vehicles. Our transformation yields a neural network with encrypted weights, which can be evaluated on encrypted inputs in
\( 12.55 \,\mathrm{m}\mathrm{s} \)
. Our protected scheduler is capable of updating the encrypted charging plan in approximately 1.06 seconds.
}},
author = {{Fischer, Andreas and Fuhry, Benny and Kußmaul, Jörn and Janneck, Jonas and Kerschbaum, Florian and Bodden, Eric}},
issn = {{2471-2566}},
journal = {{ACM Transactions on Privacy and Security}},
keywords = {{Safety, Risk, Reliability and Quality, General Computer Science}},
number = {{3}},
pages = {{1--36}},
publisher = {{Association for Computing Machinery (ACM)}},
title = {{{Computation on Encrypted Data Using Dataflow Authentication}}},
doi = {{10.1145/3513005}},
volume = {{25}},
year = {{2022}},
}
@inproceedings{10779,
author = {{Guettatfi, Zakarya and Kermia, Omar and Khouas, Abdelhakim}},
booktitle = {{25th International Conference on Field Programmable Logic and Applications (FPL)}},
issn = {{1946-147X}},
keywords = {{embedded systems, field programmable gate arrays, operating systems (computers), scheduling, μC/OS-II, FPGAs, OS foundation, SafeRTOS, Xenomai, chip utilization ration, complex time constraints, embedded systems, hard real-time hardware task allocation, hard real-time hardware task scheduling, hardware-software real-time operating systems, partially reconfigurable field-programmable gate arrays, resource constraints, safety-critical RTOS, Field programmable gate arrays, Hardware, Job shop scheduling, Real-time systems, Shape, Software}},
publisher = {{Imperial College}},
title = {{{Over effective hard real-time hardware tasks scheduling and allocation}}},
doi = {{10.1109/FPL.2015.7293994}},
year = {{2015}},
}
@article{39483,
author = {{Vidor, F.F. and Wirth, G.I. and Hilleringmann, Ulrich}},
issn = {{0026-2714}},
journal = {{Microelectronics Reliability}},
keywords = {{Electrical and Electronic Engineering, Surfaces, Coatings and Films, Safety, Risk, Reliability and Quality, Condensed Matter Physics, Atomic and Molecular Physics, and Optics, Electronic, Optical and Magnetic Materials}},
number = {{12}},
pages = {{2760--2765}},
publisher = {{Elsevier BV}},
title = {{{Low temperature fabrication of a ZnO nanoparticle thin-film transistor suitable for flexible electronics}}},
doi = {{10.1016/j.microrel.2014.07.147}},
volume = {{54}},
year = {{2014}},
}
@inproceedings{38784,
abstract = {{This article presents the classification tree method for functional verification to close the gap from the specification of a test plan to SystemVerilog (Chandra and Chakrabarty, 2001) test bench generation. Our method supports the systematic development of test configurations and is based on the classification tree method for embedded systems (CTM/ES) (Chakrabarty et al., 2000) extending CTM/ES for random test generation as well as for functional coverage and property specification}},
author = {{Krupp, Alexander and Müller, Wolfgang}},
booktitle = {{Proceedings of the Design Automation & Test in Europe Conference}},
isbn = {{3-9810801-1-4}},
keywords = {{Classification tree analysis, System testing, Embedded system, Safety, Automatic testing, Automation}},
publisher = {{IEEE}},
title = {{{Classification Trees for Functional Coverage and Random Test Generation}}},
doi = {{10.1109/DATE.2006.243902}},
year = {{2006}},
}