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Open Access Publications from the University of California

Noise Source Agnostic Entropy Extractor

  • Author(s): Shiledar, Ashutosh Devendra
  • Advisor(s): Yang, Chih-Kong Ken
  • et al.
Abstract

As the world becomes more connected and information flows rapidly between the billions

of devices, snooping of nodes or security risks of information transfer are increasing. As a result,

increasing levels of cryptography are required to keep private data safe and secure. Most

cryptographic techniques rely on obtaining random sequences as keys or ciphers to encode and

encrypt information. Dynamic environments lead to rapid establishment and teardown of sessions

in networked applications, requiring high rates of random sequences, and placing a burden on

random number generators (RNG) to deliver these sequences both quickly and securely. This

thesis presents a new type of entropy extractor to address these needs. The proposed

architecture introduces an architecture that uses a whitening filter in feedback to increase the

potential entropy extracted from the noise source. Due to its placement in the loop, the whitening

filter is implemented in the digital domain, allowing it to be reconfigured to work with a wide variety

of noise sources. This thesis shows how the architecture was developed and analyzes the effects

different component choices may have on the entropy extractor’s performance.

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