TY - GEN
T1 - What Do You See? Transforming Fault Injection Target Characterizations
AU - Krček, Marina
N1 - Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
PY - 2022
Y1 - 2022
N2 - In fault injection attacks, the first step is to evaluate the target behavior for various fault injection parameters. Showing the results of such a characterization (commonly known as target cartography) is informative and allows researchers to assess the target’s behavior better. Additionally, it helps understand the performance of new search methods or attacks. Thus, publishing obtained results is essential to provide relevant information for reproducibility and benchmarking, improving state-of-the-art results and general security. Unfortunately, publishing the results also allows malicious parties to reverse engineer the information and potentially mount an attack easier. This work discusses how various transformations can be used to occlude sensitive information but, at the same time, still be useful for interested researchers. Our results show that even simple 2D transformations, such as rotation, scaling, and shifting, significantly increase the effort required to reverse engineer the transformed data but maintain the interesting data distribution. Consequently, this work provides a method to allow publishers to share more data in a confidential setting.
AB - In fault injection attacks, the first step is to evaluate the target behavior for various fault injection parameters. Showing the results of such a characterization (commonly known as target cartography) is informative and allows researchers to assess the target’s behavior better. Additionally, it helps understand the performance of new search methods or attacks. Thus, publishing obtained results is essential to provide relevant information for reproducibility and benchmarking, improving state-of-the-art results and general security. Unfortunately, publishing the results also allows malicious parties to reverse engineer the information and potentially mount an attack easier. This work discusses how various transformations can be used to occlude sensitive information but, at the same time, still be useful for interested researchers. Our results show that even simple 2D transformations, such as rotation, scaling, and shifting, significantly increase the effort required to reverse engineer the transformed data but maintain the interesting data distribution. Consequently, this work provides a method to allow publishers to share more data in a confidential setting.
KW - 2D Transformations
KW - Fault injection
KW - Target characterization
UR - http://www.scopus.com/inward/record.url?scp=85145258325&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-22829-2_10
DO - 10.1007/978-3-031-22829-2_10
M3 - Conference contribution
AN - SCOPUS:85145258325
SN - 978-3-031-22828-5
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 165
EP - 184
BT - Security, Privacy, and Applied Cryptography Engineering - 12th International Conference, SPACE 2022, Proceedings
A2 - Batina, Lejla
A2 - Picek, Stjepan
A2 - Mondal, Mainack
PB - Springer Science and Business Media Deutschland GmbH
T2 - 12th International Conference on Security, Privacy, and Applied Cryptography Engineering, SPACE 2022
Y2 - 9 December 2022 through 12 December 2022
ER -