Driving an NP-Complete problem with Combinatorial Decomposition to generate a unique and irreversible bitstring from a single integer seed value
S. P. Christmas, R. M. Leidich

TL;DR
This paper presents a novel cryptographic method that generates a long, unique, and irreversible bitstring from a seed using combinatorial decomposition, leveraging NP-complete problem complexity for security.
Contribution
The paper introduces a new scheme combining combinatorial decomposition and destructive functions to produce irreversible bitstrings from a seed, enhancing cryptographic security.
Findings
The method produces unique bitstrings from a seed of arbitrary length.
Reversing the process is as hard as solving NP-complete Subset-Sum.
The approach offers potential for cryptographic applications with high security.
Abstract
Generation of an (arbitrarily) long string of bits unique to a given finite-length numerical seed is of great value in the field of random number generation, computer simulations, and other areas of computer science. Extending this idea such that the bitstring cannot be reverse-engineered to recover the original seed value extends the value of such a system to the field of cryptography, as the string can be used directly as an encryption mask, or as the input to some other cryptographic function. The longer the string that can be generated, the closer the system would come to the ideal cryptographic case of the One Time Pad. In this paper we propose a scheme for taking an initial seed (nominally a 128-bit integer, but not restricted to such), and expanding this into a unique bitstring of a length determined by a limit cycle that makes it useful in practical applications. We utilize…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAlgorithms and Data Compression · Parallel Computing and Optimization Techniques · Logic, programming, and type systems
