Literature DB >> 29686070

Cryptographic hashing using chaotic hydrodynamics.

William Gilpin1.   

Abstract

Fluids may store and manipulate information, enabling complex applications ranging from digital logic gates to algorithmic self-assembly. While controllable hydrodynamic chaos has previously been observed in viscous fluids and harnessed for efficient mixing, its application to the manipulation of digital information has been sparsely investigated. We show that chaotic stirring of a viscous fluid naturally produces a characteristic signature of the stirring process in the arrangement of particles in the fluid, and that this signature directly satisfies the requirements for a cryptographic hash function. This includes strong divergence between similar stirring protocols' hashes and avoidance of collisions (identical hashes from distinct stirs), which are facilitated by noninvertibility and a broad chaotic attractor that samples many points in the fluid domain. The hashing ability of the chaotic fluidic map implicates several unexpected mechanisms, including incomplete mixing at short time scales that produces a hyperuniform hash distribution. We investigate the dynamics of hashing using interparticle winding statistics, and find that hashing starts with large-scale winding of kinetically disjoint regions of the chaotic attractor, which gradually gives way to smaller scale braiding of single-particle trajectories. In addition to providing a physically motivated approach to implementing and analyzing deterministic chaotic maps for cryptographic applications, we anticipate that our approach has applications in microfluidic proof-of-work systems and characterizing large-scale turbulent flows from sparse tracer data.

Keywords:  braiding; encryption; fluid dynamics; mixing; nonlinear dynamics

Year:  2018        PMID: 29686070      PMCID: PMC5948985          DOI: 10.1073/pnas.1721852115

Source DB:  PubMed          Journal:  Proc Natl Acad Sci U S A        ISSN: 0027-8424            Impact factor:   11.205


  15 in total

1.  Chaotic mixer for microchannels.

Authors:  Abraham D Stroock; Stephan K W Dertinger; Armand Ajdari; Igor Mezic; Howard A Stone; George M Whitesides
Journal:  Science       Date:  2002-01-25       Impact factor: 47.728

2.  Local density fluctuations, hyperuniformity, and order metrics.

Authors:  Salvatore Torquato; Frank H Stillinger
Journal:  Phys Rev E Stat Nonlin Soft Matter Phys       Date:  2003-10-29

3.  Chaotic mixing in cross-channel micromixers.

Authors:  P Tabeling; M Chabert; A Dodge; C Jullien; F Okkels
Journal:  Philos Trans A Math Phys Eng Sci       Date:  2004-05-15       Impact factor: 4.226

4.  Controlling chaos.

Authors: 
Journal:  Phys Rev Lett       Date:  1990-03-12       Impact factor: 9.161

5.  Emergent Hyperuniformity in Periodically Driven Emulsions.

Authors:  Joost H Weijs; Raphaël Jeanneret; Rémi Dreyfus; Denis Bartolo
Journal:  Phys Rev Lett       Date:  2015-09-03       Impact factor: 9.161

6.  Finite-space Lyapunov exponents and pseudochaos.

Authors:  Ljupco Kocarev; Janusz Szczepanski
Journal:  Phys Rev Lett       Date:  2004-12-01       Impact factor: 9.161

7.  Measuring topological chaos.

Authors:  Jean-Luc Thiffeault
Journal:  Phys Rev Lett       Date:  2005-03-04       Impact factor: 9.161

8.  Coding/decoding and reversibility of droplet trains in microfluidic networks.

Authors:  Michael J Fuerstman; Piotr Garstecki; George M Whitesides
Journal:  Science       Date:  2007-01-04       Impact factor: 47.728

9.  Electric fields yield chaos in microflows.

Authors:  Jonathan D Posner; Carlos L Pérez; Juan G Santiago
Journal:  Proc Natl Acad Sci U S A       Date:  2012-08-20       Impact factor: 11.205

10.  Information transfer and behavioural inertia in starling flocks.

Authors:  Alessandro Attanasi; Andrea Cavagna; Lorenzo Del Castello; Irene Giardina; Tomas S Grigera; Asja Jelić; Stefania Melillo; Leonardo Parisi; Oliver Pohl; Edward Shen; Massimiliano Viale
Journal:  Nat Phys       Date:  2014-09-01       Impact factor: 20.034

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