Eine Physical Unlonable Function (PUF) wertet Fertigungsschwankungen in einem Chip aus und erzeugt daraus ein individuelles Signal, das ähnlich wie ein eingescannter Fingerabdruck von Chip zu Chip stark und von Messung zu Messung leicht variiert.
Eine PUF erzeugt also ein verrauschtes Geheimnis reproduzierbar zur Laufzeit. Dieses Geheimnis kann entweder dazu genutzt werden, den Chip zu authentifizieren, oder es steht nach Entfernen des Rauschens als kryptografischer Schlüssel zur Verfügung.
Durch PUFs erhalten auch Systeme ohne sichere Schlüsselspeicher Zugang zu sicheren kryptografischen Schlüsseln.
Forschungsthemen
Neue PUF Primitive und Architekturen insbesonder auch Memristor-basierte PUFs
For a practical usage, we want the responses of Physical Unclonable Functions (PUFs) to be unpredictable for an attacker, but reproducible for a legitimate user—intuitive criteria which need to be specified in the form of statistical tests to be useful for a practical evaluation. Practical tests range from simple ones, e.g. calculating the bias of the responses (bit 1 should be as likely as a 0 overall), to more complex tests like estimating spatial correlations between response bits. Each checks for a different aspect, pointing towards particular classes of possible issues.
The aim of this work is to consolidate existing tooling for the assessment of PUFs from measurement data into a newly built framework. The targeted end result is a common test suite which is
generic regarding the concrete dataset, its data format, its dimensions, and the applicable tests,
extensible, i.e. includes the currently existing tests but can be easily adapted to cover additional ones, and
maintainable and auditable to allow for confidence in the correctness of the results.
Voraussetzungen
Required: Significant experience with Python and numpy, as well as Python bindings in compiled languages
Required: Experience in architecting extensible and maintainable software
Beneficial: Experience with analysis of multidimensional data
Beneficial: Background on statistical tests
Optional: Background knowledge on PUFs
Kontakt
If you are interested in this work, please contact me via email with a short CV and grade report. We will then arrange a short meeting where we can discuss the details.
Jonas Ruchti, M.Sc. Technical University of Munich, Chair of Security in Information Technology Room N1010 E-Mail: j.ruchti@tum.de
Betreuer:
Jonas Ruchti
Aktuelle Projekte in diesem Bereich
VE-FIDES (Knowhow-Schutz und Identifizierbarkeit von Elektronikkomponenten für vertrauenswürdige Produktionsketten) Unser Beitrag: Verwendung von PUFs zur Absicherung der Lieferkette. [Link zum Projekt]
APRIORI (Resilienz gegen Fehlerinjektionsangriffe für verbesserten Datenschutz von IoT-Endgeräten): Unser Beitrag: Fehlerangriffe auf PUFs und Schutzmechanismen; Projektleitung [Link zum Projekt]
6G Zukunftslabor Bayern – 6G Future Lab Bavaria: Verwendung von PUFs in 6G Netzwerken. [Link zum Projekt]
Ausgewählte Veröffentlichungen
Lars Tebelmann, Ulrich Kühne, Jean-Luc Danger, and Michael Pehl, Analysis and Protection of the Two-Metric Helper Data Scheme, International Workshop on Constructive Side-Channel Analysis and Secure Design. Springer, Cham, 2021. [Download from eprint]
Emanuele Strieder, Christoph Frisch, and Michael Pehl, Machine Learning of Physical Unclonable Functions using Helper Data: Revealing a Pitfall in the Fuzzy Commitment Scheme, IACR Transactions on Cryptographic Hardware and Embedded Systems, 2021(2), 1-36. https://doi.org/10.46586/tches.v2021.i2.1-36i
Lars Tebelmann, Jean-Luc Danger, and Michael Pehl. Self-secured PUF: protecting the loop PUF by masking, International Workshop on Constructive Side-Channel Analysis and Secure Design. Springer, Cham, 2020. [Download from eprint]
Lars Tebelmann, Michael Pehl, and Vincent Immler. Side-channel analysis of the TERO PUF, International Workshop on Constructive Side-Channel Analysis and Secure Design. Springer, Cham, 2019. [Download from eprint]
Florian Wilde, Berndt Gammel, and Michael Pehl, Spatial Correlation Analysis on Physical Unclonable Functions, IEEE Transactions on Information Forensics and Security (Volume: 13 Issue: 6), 2018, pages: 1468-1480; Tool published with it
Lars Tebelmann, Michael Pehl, and Georg Sigl, EM side-channel analysis of BCH-based error correction for PUF-based key generation, Proceedings of the 2017 Workshop on Attacks and Solutions in Hardware Security. 2017.
Michael Pehl, Matthias Hiller, and Georg Sigl, Secret Key Generation for Physical Unclonable Functions – Secret Key Generation and Authentication, In: Information Theoretic Security and Privacy of Information Systems, Cambridge, 2017
Florian Wilde, Large Scale Characterization of SRAM on Infineon XMC Microcontrollers as PUF. 4th Workshop on Cryptography and Security in Computing Systems (CS2 2017) HIPEAC17 , 2017 Public PUF dataset published with it
Matthias Hiller, Meng-Day (Mandel) Yu, and Sigl, Georg, Cherry-Picking Reliable PUF Bits with Differential Sequence Coding, IEEE Transactions on Information Forensics and Security (Volume: 11 Issue: 9), 2016, pages: 2065-2076
Matthias Hiller, Meng-Day (Mandel) Yu, and Michael Pehl, Systematic Low Leakage Coding for Physical Unclonable Functions, ACM Symposium on Information, Computer and Communications Security (ASIACCS), 2015
Florian Wilde, Matthias Hiller, and Michael Pehl, Statistic-based security analysis of ring oscillator PUFs, 2014 International Symposium on Integrated Circuits (ISIC). IEEE, 2014.
Matthias Hiller, Dominik Merli, Frederic Stumpf, and Georg Sigl, Complementary IBS: Application Specific Error Correction for PUFs, IEEE International Symposium on Hardware-Oriented Security and Trust (HOST), pp. 1-6, June 2012.
