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Christof Paar

Researcher at Max Planck Society

Publications -  409
Citations -  23389

Christof Paar is an academic researcher from Max Planck Society. The author has contributed to research in topics: Cryptography & Encryption. The author has an hindex of 69, co-authored 399 publications receiving 21790 citations. Previous affiliations of Christof Paar include University of Massachusetts Amherst & University of Duisburg-Essen.

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Proceedings ArticleDOI

Insights into the mind of a trojan designer: the challenge to integrate a trojan into the bitstream

TL;DR: In this paper, the authors present an improved methodology for bitstream file format reversing and introduce a novel idea for Trojan insertion, which can be used to infiltrate FPGAs in a non-invasive manner after shipment.
Book ChapterDOI

The First Thorough Side-Channel Hardware Trojan

TL;DR: This work presents a mechanism which shows how easily a stealthy hardware Trojan can be inserted in a provably-secure side-channel analysis protected implementation, leading to successful key recovery attacks.
Book ChapterDOI

Evaluating Resistance of MCML Technology to Power Analysis Attacks Using a Simulation-Based Methodology

TL;DR: This paper explores the resistance of MOS Current Mode Logic (MCML) against attacks based on the observation of the power consumption and found that it is suitable to protect cryptographic hardware from Differential Power Analysis and similar side-channel attacks.
Journal ArticleDOI

On digital signatures in ad hoc networks

TL;DR: This work analyses the performance of RSA- and ECDSA-based digital signature schemes in the context of multi-hop ad hoc networks and draws some recommendations to strengthen the security engineering process for dependable distributed systems over the wireless network.

Efficient Authentication Mechanisms for Navigation Systems - a Radio-Navigation Case Study

TL;DR: An efficient authentication mechanism especially designed for navigation systems that is based upon the Timed Efficient Stream Loss-Tolerant Authentication (TESLA) algorithm is introduced and the use of adjusted TESLA in eLORAN is proposed.