[{"publication_identifier":{"issn":["2471-2566","2471-2574"]},"publication_status":"published","intvolume":"        26","page":"1-36","citation":{"apa":"Arias-Cabarcos, P., Fallahi, M., Habrich, T., Schulze, K., Becker, C., &#38; Strufe, T. (2023). Performance and Usability Evaluation of Brainwave Authentication Techniques with Consumer Devices. <i>ACM Transactions on Privacy and Security</i>, <i>26</i>(3), 1–36. <a href=\"https://doi.org/10.1145/3579356\">https://doi.org/10.1145/3579356</a>","mla":"Arias-Cabarcos, Patricia, et al. “Performance and Usability Evaluation of Brainwave Authentication Techniques with Consumer Devices.” <i>ACM Transactions on Privacy and Security</i>, vol. 26, no. 3, Association for Computing Machinery (ACM), 2023, pp. 1–36, doi:<a href=\"https://doi.org/10.1145/3579356\">10.1145/3579356</a>.","bibtex":"@article{Arias-Cabarcos_Fallahi_Habrich_Schulze_Becker_Strufe_2023, title={Performance and Usability Evaluation of Brainwave Authentication Techniques with Consumer Devices}, volume={26}, DOI={<a href=\"https://doi.org/10.1145/3579356\">10.1145/3579356</a>}, number={3}, journal={ACM Transactions on Privacy and Security}, publisher={Association for Computing Machinery (ACM)}, author={Arias-Cabarcos, Patricia and Fallahi, Matin and Habrich, Thilo and Schulze, Karen and Becker, Christian and Strufe, Thorsten}, year={2023}, pages={1–36} }","short":"P. Arias-Cabarcos, M. Fallahi, T. Habrich, K. Schulze, C. Becker, T. Strufe, ACM Transactions on Privacy and Security 26 (2023) 1–36.","chicago":"Arias-Cabarcos, Patricia, Matin Fallahi, Thilo Habrich, Karen Schulze, Christian Becker, and Thorsten Strufe. “Performance and Usability Evaluation of Brainwave Authentication Techniques with Consumer Devices.” <i>ACM Transactions on Privacy and Security</i> 26, no. 3 (2023): 1–36. <a href=\"https://doi.org/10.1145/3579356\">https://doi.org/10.1145/3579356</a>.","ieee":"P. Arias-Cabarcos, M. Fallahi, T. Habrich, K. Schulze, C. Becker, and T. Strufe, “Performance and Usability Evaluation of Brainwave Authentication Techniques with Consumer Devices,” <i>ACM Transactions on Privacy and Security</i>, vol. 26, no. 3, pp. 1–36, 2023, doi: <a href=\"https://doi.org/10.1145/3579356\">10.1145/3579356</a>.","ama":"Arias-Cabarcos P, Fallahi M, Habrich T, Schulze K, Becker C, Strufe T. Performance and Usability Evaluation of Brainwave Authentication Techniques with Consumer Devices. <i>ACM Transactions on Privacy and Security</i>. 2023;26(3):1-36. doi:<a href=\"https://doi.org/10.1145/3579356\">10.1145/3579356</a>"},"volume":26,"author":[{"last_name":"Arias-Cabarcos","full_name":"Arias-Cabarcos, Patricia","first_name":"Patricia"},{"first_name":"Matin","last_name":"Fallahi","full_name":"Fallahi, Matin"},{"first_name":"Thilo","last_name":"Habrich","full_name":"Habrich, Thilo"},{"first_name":"Karen","full_name":"Schulze, Karen","last_name":"Schulze"},{"full_name":"Becker, Christian","last_name":"Becker","first_name":"Christian"},{"last_name":"Strufe","full_name":"Strufe, Thorsten","first_name":"Thorsten"}],"date_updated":"2023-10-14T12:12:42Z","doi":"10.1145/3579356","type":"journal_article","status":"public","user_id":"92804","_id":"48063","issue":"3","year":"2023","date_created":"2023-10-14T12:11:55Z","publisher":"Association for Computing Machinery (ACM)","title":"Performance and Usability Evaluation of Brainwave Authentication Techniques with Consumer Devices","publication":"ACM Transactions on Privacy and Security","abstract":[{"text":"<jats:p>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.</jats:p>\r\n          <jats:p />","lang":"eng"}],"language":[{"iso":"eng"}],"keyword":["Safety","Risk","Reliability and Quality","General Computer Science"]},{"year":"2022","citation":{"ieee":"A. Fischer, B. Fuhry, J. Kußmaul, J. Janneck, F. Kerschbaum, and E. Bodden, “Computation on Encrypted Data Using Dataflow Authentication,” <i>ACM Transactions on Privacy and Security</i>, vol. 25, no. 3, pp. 1–36, 2022, doi: <a href=\"https://doi.org/10.1145/3513005\">10.1145/3513005</a>.","chicago":"Fischer, Andreas, Benny Fuhry, Jörn Kußmaul, Jonas Janneck, Florian Kerschbaum, and Eric Bodden. “Computation on Encrypted Data Using Dataflow Authentication.” <i>ACM Transactions on Privacy and Security</i> 25, no. 3 (2022): 1–36. <a href=\"https://doi.org/10.1145/3513005\">https://doi.org/10.1145/3513005</a>.","ama":"Fischer A, Fuhry B, Kußmaul J, Janneck J, Kerschbaum F, Bodden E. Computation on Encrypted Data Using Dataflow Authentication. <i>ACM Transactions on Privacy and Security</i>. 2022;25(3):1-36. doi:<a href=\"https://doi.org/10.1145/3513005\">10.1145/3513005</a>","mla":"Fischer, Andreas, et al. “Computation on Encrypted Data Using Dataflow Authentication.” <i>ACM Transactions on Privacy and Security</i>, vol. 25, no. 3, Association for Computing Machinery (ACM), 2022, pp. 1–36, doi:<a href=\"https://doi.org/10.1145/3513005\">10.1145/3513005</a>.","short":"A. Fischer, B. Fuhry, J. Kußmaul, J. Janneck, F. Kerschbaum, E. Bodden, ACM Transactions on Privacy and Security 25 (2022) 1–36.","bibtex":"@article{Fischer_Fuhry_Kußmaul_Janneck_Kerschbaum_Bodden_2022, title={Computation on Encrypted Data Using Dataflow Authentication}, volume={25}, DOI={<a href=\"https://doi.org/10.1145/3513005\">10.1145/3513005</a>}, number={3}, journal={ACM Transactions on Privacy and Security}, publisher={Association for Computing Machinery (ACM)}, author={Fischer, Andreas and Fuhry, Benny and Kußmaul, Jörn and Janneck, Jonas and Kerschbaum, Florian and Bodden, Eric}, year={2022}, pages={1–36} }","apa":"Fischer, A., Fuhry, B., Kußmaul, J., Janneck, J., Kerschbaum, F., &#38; Bodden, E. (2022). Computation on Encrypted Data Using Dataflow Authentication. <i>ACM Transactions on Privacy and Security</i>, <i>25</i>(3), 1–36. <a href=\"https://doi.org/10.1145/3513005\">https://doi.org/10.1145/3513005</a>"},"page":"1-36","intvolume":"        25","publication_status":"published","publication_identifier":{"issn":["2471-2566","2471-2574"]},"issue":"3","title":"Computation on Encrypted Data Using Dataflow Authentication","doi":"10.1145/3513005","date_updated":"2022-06-09T10:29:19Z","publisher":"Association for Computing Machinery (ACM)","author":[{"first_name":"Andreas","last_name":"Fischer","full_name":"Fischer, Andreas"},{"last_name":"Fuhry","full_name":"Fuhry, Benny","first_name":"Benny"},{"first_name":"Jörn","last_name":"Kußmaul","full_name":"Kußmaul, Jörn"},{"full_name":"Janneck, Jonas","last_name":"Janneck","first_name":"Jonas"},{"last_name":"Kerschbaum","full_name":"Kerschbaum, Florian","first_name":"Florian"},{"orcid":"0000-0003-3470-3647","last_name":"Bodden","full_name":"Bodden, Eric","id":"59256","first_name":"Eric"}],"date_created":"2022-06-09T10:28:03Z","volume":25,"abstract":[{"text":"<jats:p>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.</jats:p>\r\n          <jats:p>\r\n            We introduce the concept of\r\n            <jats:italic>dataflow authentication</jats:italic>\r\n            (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\r\n            <jats:inline-formula content-type=\"math/tex\">\r\n              <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\( 12.55 \\,\\mathrm{m}\\mathrm{s} \\)</jats:tex-math>\r\n            </jats:inline-formula>\r\n            . Our protected scheduler is capable of updating the encrypted charging plan in approximately 1.06 seconds.\r\n          </jats:p>","lang":"eng"}],"status":"public","type":"journal_article","publication":"ACM Transactions on Privacy and Security","keyword":["Safety","Risk","Reliability and Quality","General Computer Science"],"language":[{"iso":"eng"}],"_id":"31844","user_id":"15249","department":[{"_id":"76"}]}]