scholarly journals Low-Complexity Weak Pseudorandom Functions in $$\mathtt {AC}0[\mathtt {MOD}2]$$

2021 ◽  
pp. 487-516
Author(s):  
Elette Boyle ◽  
Geoffroy Couteau ◽  
Niv Gilboa ◽  
Yuval Ishai ◽  
Lisa Kohl ◽  
...  
Keyword(s):  
Low Complexity ◽  
Pseudorandom Functions

scholarly journals Exploring Crypto-Physical Dark Matter and Learning with Physical Rounding

2020 ◽  
pp. 373-401
Author(s):  
Sébastien Duval ◽  
Pierrick Méaux ◽  
Charles Momin ◽  
François-Xavier Standaert
Keyword(s):  
Dark Matter ◽  
Low Complexity ◽  
Linear Functions ◽  
Learning Problems ◽  
Prime Field ◽  
Side Channel Analysis ◽  
Pseudorandom Functions ◽  
Channel Analysis ◽  
Pseudorandom Function ◽  
Standard Learning

State-of-the-art re-keying schemes can be viewed as a tradeoff between efficient but heuristic solutions based on binary field multiplications, that are only secure if implemented with a sufficient amount of noise, and formal but more expensive solutions based on weak pseudorandom functions, that remain secure if the adversary accesses their output in full. Recent results on “crypto dark matter” (TCC 2018) suggest that low-complexity pseudorandom functions can be obtained by mixing linear functions over different small moduli. In this paper, we conjecture that by mixing some matrix multiplications in a prime field with a physical mapping similar to the leakage functions exploited in side-channel analysis, we can build efficient re-keying schemes based on “crypto-physical dark matter”, that remain secure against an adversary who can access noise-free measurements. We provide first analyzes of the security and implementation properties that such schemes provide. Precisely, we first show that they are more secure than the initial (heuristic) proposal by Medwed et al. (AFRICACRYPT 2010). For example, they can resist attacks put forward by Belaid et al. (ASIACRYPT 2014), satisfy some relevant cryptographic properties and can be connected to a “Learning with Physical Rounding” problem that shares some similarities with standard learning problems. We next show that they are significantly more efficient than the weak pseudorandom function proposed by Dziembowski et al. (CRYPTO 2016), by exhibiting hardware implementation results.

Low Complexity Hybrid Smart Antenna with Directional Elements over Frequency Selective Fading Channel

2011 ◽  
Vol E94-B (12) ◽  
pp. 3610-3613 ◽  
Author(s):  
Juinn-Horng DENG ◽  
Nuri CELIK ◽  
Zhengqing YUN ◽  
Magdy F. ISKANDER
Keyword(s):  
Fading Channel ◽  
Smart Antenna ◽  
Low Complexity ◽  
Selective Fading ◽  
Frequency Selective Fading ◽  
Frequency Selective

Training Sequence Design for Low Complexity Channel Estimation in Transmit Diversity TDS-OFDM System

2009 ◽  
Vol E92-B (6) ◽  
pp. 2308-2311 ◽  
Author(s):  
Fang YANG ◽  
Kewu PENG ◽  
Jun WANG ◽  
Jian SONG ◽  
Zhixing YANG
Keyword(s):  
Channel Estimation ◽  
Transmit Diversity ◽  
Low Complexity ◽  
Training Sequence ◽  
Ofdm System ◽  
Sequence Design ◽  
Training Sequence Design

A Low-Complexity Sparse Channel Estimation Method for OFDM Systems

2010 ◽  
Vol E93-B (8) ◽  
pp. 2211-2214
Author(s):  
Bin SHENG ◽  
Pengcheng ZHU ◽  
Xiaohu YOU ◽  
Lan CHEN
Keyword(s):  
Channel Estimation ◽  
Estimation Method ◽  
Low Complexity ◽  
Ofdm Systems ◽  
Sparse Channel Estimation ◽  
Sparse Channel

Low-Complexity Soft-ML Detection Algorithm for Modified-DCM in WiMedia UWB Systems

2013 ◽  
Vol E96.B (3) ◽  
pp. 910-913 ◽  
Author(s):  
Kilhwan KIM ◽  
Jangyong PARK ◽  
Jihun KOO ◽  
Yongsuk KIM ◽  
Jaeseok KIM
Keyword(s):  
Low Complexity ◽  
Detection Algorithm ◽  
Ml Detection
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