CRYPTO '99 Proceedings of the 19th Annual International Cryptology Conference on Advances in Cryptology
ElectroMagnetic Analysis (EMA): Measures and Counter-Measures for Smart Cards
E-SMART '01 Proceedings of the International Conference on Research in Smart Cards: Smart Card Programming and Security
Timing Attacks on Implementations of Diffie-Hellman, RSA, DSS, and Other Systems
CRYPTO '96 Proceedings of the 16th Annual International Cryptology Conference on Advances in Cryptology
IPA: A New Class of Power Attacks
CHES '99 Proceedings of the First International Workshop on Cryptographic Hardware and Embedded Systems
Electromagnetic Analysis: Concrete Results
CHES '01 Proceedings of the Third International Workshop on Cryptographic Hardware and Embedded Systems
K-means clustering via principal component analysis
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Power Analysis Attacks: Revealing the Secrets of Smart Cards (Advances in Information Security)
Power Analysis Attacks: Revealing the Secrets of Smart Cards (Advances in Information Security)
A tutorial on spectral clustering
Statistics and Computing
CHES '08 Proceeding sof the 10th international workshop on Cryptographic Hardware and Embedded Systems
Comparative Evaluation of Rank Correlation Based DPA on an AES Prototype Chip
ISC '08 Proceedings of the 11th international conference on Information Security
CHES '09 Proceedings of the 11th International Workshop on Cryptographic Hardware and Embedded Systems
Template attacks in principal subspaces
CHES'06 Proceedings of the 8th international conference on Cryptographic Hardware and Embedded Systems
Randomized Algorithms for Matrices and Data
Foundations and Trends® in Machine Learning
Power analysis of atmel cryptomemory --- recovering keys from secure EEPROMs
CT-RSA'12 Proceedings of the 12th conference on Topics in Cryptology
CT-RSA'12 Proceedings of the 12th conference on Topics in Cryptology
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Spectral methods, ranging from traditional Principal Components Analysis to modern Laplacian matrix factorization, have proven to be a valuable tool for a wide range of diverse data mining applications. Commonly these methods are stated as optimization problems and employ the extremal (maximal or minimal) eigenvectors of a certain input matrix for deriving the appropriate statistical inferences. Interestingly, recent studies have questioned this "modus operandi" and revealed that useful information may also be present within low-order eigenvectors whose mass is concentrated (localized) in a small part of their indexes. An application context where localized low-order eigenvectors have been successfully employed is "Differential Power Analysis" (DPA). DPA is a well studied side-channel attack on cryptographic hardware devices (such as smart cards) that employs statistical analysis of the device's power consumption in order to retrieve the secret key of the cryptographic algorithm. In this work we propose a data mining (clustering) formulation of the DPA process and also provide a theoretical model that justifies and explains the utility of low-order eigenvectors. In our data mining formulation, we consider that the key-relevant information is modelled as a "low-signal" pattern that is embedded in a "high-noise" dataset. In this respect our results generalize beyond DPA and are applicable to analogous low-signal, hidden pattern problems. The experimental results using power trace measurements from a programmable smart card, verify our approach empirically.