By Philipp Grabher, Johann Großschädl, Simon Hoerder, Kimmo Järvinen, Dan Page (auth.), Bart Preneel, Tsuyoshi Takagi (eds.)
This booklet constitutes the lawsuits of the thirteenth overseas Workshop on Cryptographic and Embedded platforms, CHES 2011, held in Nara, Japan, from September 28 till October 1, 2011.
The 32 papers offered including 1 invited speak have been conscientiously reviewed and chosen from 119 submissions. The papers are geared up in topical sections named: FPGA implementation; AES; elliptic curve cryptosystems; lattices; part channel assaults; fault assaults; light-weight symmetric algorithms, PUFs; public-key cryptosystems; and hash functions.
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Extra info for Cryptographic Hardware and Embedded Systems – CHES 2011: 13th International Workshop, Nara, Japan, September 28 – October 1, 2011. Proceedings
C2 ... Ck p1 p2k ... p2 ... p1 Fig. 6. The TRNG system implementation with a PI controller on FPGA The PDLs are depicted as gray triangles which provide the finest and most granular level of control over the delays. If the resulting delay difference from one PDL is equal to δ, the effective input/output delay of a PDL, D(i), for the binary input i would be: D(i) = i × dc + (1 − i) × (dc + δ). (3) where dc is a constant delay value. Each programmable delay block consists of two PDLs. The control input of top PDL inside each block is the complement of the bottom PDL control input in order to make a differential programmable delay structure.
The results of the NIST randomness test from running on megabytes of data is shown in Table 1. edu/ mm7/trng/. FPGA-Based True Random Number Generation Using Circuit Metastability 31 Table 1 includes the results of the NIST statistical test suite on megabytes of collected data after counter-based filtering and von Neumann correction are performed on the TRNG output bitstream. Due to the large bias in the probabilities, most of the randomness failed when the test was run on the output bitstream before the filtering and correction were carried out.
Timing Disarrangement : DPA attacks operate on a high number of (keydependent) data points that are assumed to be sampled at exactly the same point in time. , due to clock jitter or other operational variations. An eﬀective countermeasure is to further randomize or shuﬄe the points in time when such attackable operations are processed. Of course, this method can also be overcome [3,5], requiring the attacker to use advanced ﬁltering functions beyond simple peak alignment, such as complex integration and windowing methods.