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GAUGE: A Formal Framework for Measuring Cryptographic Security under Heterogeneous Adversary Cost Models

arXiv cs.CRby Bhanwar Gupta ()1 min read3 · 1 read
AI summary · glm-5.3Summaries by Vani AI

GAUGE frames cryptographic security as profiles over adversary cost models, certifying a ranking reversal between ML-KEM-512 and AES-128 from a 4–5% memory pricing shift.

GAUGE represents cryptographic security as a function over admissible adversary cost models (a security profile), proves profiles are piecewise-linear and concave, and establishes a rating trilemma when two profiles cross. A polynomial-time linear-programming procedure certifies whether the ranking of two schemes is robust, reverses under admissible models, or is genuinely incomparable. Applied to NIST post-quantum standards, the framework certifies a ML-KEM-512 versus AES-128 ranking reversal from a 4–5% shift in memory pricing and measures lattice-sieving cost drift of 9.79 bits per year over eight years. A hybrid X25519 + ML-KEM-768 handshake reduces combined-break probability twenty-fold at a 2.3 kilobyte cost.

Key facts
  • Security profiles over adversary cost models replace single bit-security numbers
  • LP procedure certifies robust, reversed, or incomparable scheme rankings
  • ML-KEM-512 vs AES-128 ranking reverses under 4–5% memory pricing shift
  • Lattice-sieving costs drift 9.79 bits per year over eight years
  • Hybrid X25519 + ML-KEM-768 cuts combined-break probability 20x for 2.3 KB
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From the original article

1 min read at arxiv.org

Standards bodies report cryptographic security as a single number of bits, but this value depends on the adversary cost model used to price time, memory, and quantum resources. Different conventions can therefore produce different rankings of cryptographic schemes. GAUGE represents security as a function over admissible cost models, called a security profile.

arXiv cs.CR

By Bhanwar Gupta · arxiv.org

Read the full article at arXiv cs.CR

A short excerpt; the article and its images belong to the publisher. Summary, key facts and assessment above are AI-written from the article and can be wrong.