AEGIS-ZK v2.0: A Verifiably Private, Merkle-Anchored, Zero-Knowledge Platform to Prevent and Defeat Extortion
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| Formato: | Recurso digital |
| Lenguaje: | inglés |
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2025
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| _version_ | 1866902048246595584 |
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| author | Ibrahim, Mohamed |
| author_facet | Ibrahim, Mohamed |
| contents | <p><strong>AEGIS-ZK v2.0 – A Verifiably Private, Merkle-Anchored, Zero-Knowledge Platform to Prevent and Defeat Extortion</strong><br><strong>DOI:</strong> 10.5281/zenodo.17317136<br><strong>Author:</strong> Mohamed Ibrahim — Independent Researcher (Sovereign Systems Architecture)<br><strong>Date:</strong> October 2025</p> <p><strong>Overview</strong><br>AEGIS-ZK v2.0 is a verifiable, privacy-preserving framework engineered to counter digital extortion through seven layers of zero-knowledge verification and Merkle-anchored evidence integrity.<br>It provides a rigorous mathematical, cryptographic, and economic foundation for measurable, reproducible cyber-resilience.</p> <p><strong>Included in this Release</strong><br>• Full white paper detailing theoretical models, cryptographic architecture, and quantitative validation.<br>• Open-source SDKs in TypeScript and Python implementing:<br> – Threat receipts and Merkle proofs<br> – Policy compliance circuits<br> – Proof-of-Removal and Impact verification<br> – Deterministic Monte Carlo simulation engine (10 million scenarios)<br>• Public verification artifacts: canonical SHA-256 hashes, policy DSL, sample Merkle proofs, and verifier JSONs.</p> <p><strong>Key Quantitative Results (from the paper)</strong><br>Monte Carlo runs: <strong>10 000 000 deterministic scenarios</strong> (seed = 20251011)<br>Discount rate: <strong>r = 3 %</strong><br>Median 10-year NPV savings: <strong>≈ 1.136 trillion USD</strong><br>Mean NPV savings: <strong>≈ 1.923 trillion USD</strong> (5th–95th percentile ≈ 0.185 T – 6.19 T)<br>Median lives saved by year 10: <strong>≈ 2 551</strong><br>Cumulative lives saved (10 years): <strong>≈ 20 164</strong><br>Sampling error (DKW 95 % bound): <strong>ε ≤ 4.295 × 10⁻⁴</strong><br>Canonical script SHA-256: <strong>4dd552339ecb5ebbf5fb06850d0fc953f4c0be1b4eb9851860193a6b1765b839</strong><br>Summary digest SHA-256: <strong>fbb331a9a7bf814f1de9246f2539f2c2e5b7f52fc26e3c9d15d651282435fbf3</strong></p> <p>All numerical values are fully reproducible using the provided SDK and simulation code.</p> <p><strong>Verification and Proof Architecture</strong><br>Seven Zero-Knowledge Verification Layers (ZKV-1 → ZKV-7) covering device attestation, evidence integrity, attribute proofs, policy compliance, and macro-impact validation.<br>Cryptographic stack based on <strong>Poseidon</strong>, <strong>PLONK</strong>, and <strong>Halo2</strong>, with anonymous credentials using <strong>BBS+</strong> and <strong>Idemix</strong>.<br>The proof-of-compute mechanism binds the seed, code hash, and summary digest to guarantee reproducibility and integrity.</p> <p><strong>Economic Methodologies</strong><br>The study integrates 17 complementary frameworks: Cost-Benefit, Cost-Effectiveness, SROI, ROA, Stackelberg games, Mechanism Design, Principal–Agent models, Externalities analysis, Input–Output networks, Portfolio optimization (CVaR/ES), Distributionally Robust Optimization (DRO), Causal Inference (DiD, Synthetic Controls), Bayesian Decision Analysis, Shapley Attribution, Value of Information, and Robust Decision Making (RDM).</p> <p><strong>Authorship and Independence</strong><br>This work was conducted independently, without institutional or financial sponsorship, as part of an open sovereign research initiative dedicated to advancing verifiable, privacy-preserving infrastructure.</p> <p><strong>Licenses</strong><br>Document: Creative Commons Attribution 4.0 International (CC-BY-4.0)<br>Code and SDK: Apache License 2.0</p> <p><strong>Keywords</strong><br>zero-knowledge proofs, SNARKs, Merkle proofs, reproducibility, Monte Carlo, extortion prevention, sovereign systems, mechanism design, cryptographic governance</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17317136 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | AEGIS-ZK v2.0: A Verifiably Private, Merkle-Anchored, Zero-Knowledge Platform to Prevent and Defeat Extortion Ibrahim, Mohamed zero-knowledge proofs SNARKs Merkle proofs reproducibility Monte Carlo simulation extortion prevention sovereign systems mechanism design cryptographic governance privacy-preserving infrastructure verifiable computation open research economic modeling independent researcher AEGIS-ZK v2.0 <p><strong>AEGIS-ZK v2.0 – A Verifiably Private, Merkle-Anchored, Zero-Knowledge Platform to Prevent and Defeat Extortion</strong><br><strong>DOI:</strong> 10.5281/zenodo.17317136<br><strong>Author:</strong> Mohamed Ibrahim — Independent Researcher (Sovereign Systems Architecture)<br><strong>Date:</strong> October 2025</p> <p><strong>Overview</strong><br>AEGIS-ZK v2.0 is a verifiable, privacy-preserving framework engineered to counter digital extortion through seven layers of zero-knowledge verification and Merkle-anchored evidence integrity.<br>It provides a rigorous mathematical, cryptographic, and economic foundation for measurable, reproducible cyber-resilience.</p> <p><strong>Included in this Release</strong><br>• Full white paper detailing theoretical models, cryptographic architecture, and quantitative validation.<br>• Open-source SDKs in TypeScript and Python implementing:<br> – Threat receipts and Merkle proofs<br> – Policy compliance circuits<br> – Proof-of-Removal and Impact verification<br> – Deterministic Monte Carlo simulation engine (10 million scenarios)<br>• Public verification artifacts: canonical SHA-256 hashes, policy DSL, sample Merkle proofs, and verifier JSONs.</p> <p><strong>Key Quantitative Results (from the paper)</strong><br>Monte Carlo runs: <strong>10 000 000 deterministic scenarios</strong> (seed = 20251011)<br>Discount rate: <strong>r = 3 %</strong><br>Median 10-year NPV savings: <strong>≈ 1.136 trillion USD</strong><br>Mean NPV savings: <strong>≈ 1.923 trillion USD</strong> (5th–95th percentile ≈ 0.185 T – 6.19 T)<br>Median lives saved by year 10: <strong>≈ 2 551</strong><br>Cumulative lives saved (10 years): <strong>≈ 20 164</strong><br>Sampling error (DKW 95 % bound): <strong>ε ≤ 4.295 × 10⁻⁴</strong><br>Canonical script SHA-256: <strong>4dd552339ecb5ebbf5fb06850d0fc953f4c0be1b4eb9851860193a6b1765b839</strong><br>Summary digest SHA-256: <strong>fbb331a9a7bf814f1de9246f2539f2c2e5b7f52fc26e3c9d15d651282435fbf3</strong></p> <p>All numerical values are fully reproducible using the provided SDK and simulation code.</p> <p><strong>Verification and Proof Architecture</strong><br>Seven Zero-Knowledge Verification Layers (ZKV-1 → ZKV-7) covering device attestation, evidence integrity, attribute proofs, policy compliance, and macro-impact validation.<br>Cryptographic stack based on <strong>Poseidon</strong>, <strong>PLONK</strong>, and <strong>Halo2</strong>, with anonymous credentials using <strong>BBS+</strong> and <strong>Idemix</strong>.<br>The proof-of-compute mechanism binds the seed, code hash, and summary digest to guarantee reproducibility and integrity.</p> <p><strong>Economic Methodologies</strong><br>The study integrates 17 complementary frameworks: Cost-Benefit, Cost-Effectiveness, SROI, ROA, Stackelberg games, Mechanism Design, Principal–Agent models, Externalities analysis, Input–Output networks, Portfolio optimization (CVaR/ES), Distributionally Robust Optimization (DRO), Causal Inference (DiD, Synthetic Controls), Bayesian Decision Analysis, Shapley Attribution, Value of Information, and Robust Decision Making (RDM).</p> <p><strong>Authorship and Independence</strong><br>This work was conducted independently, without institutional or financial sponsorship, as part of an open sovereign research initiative dedicated to advancing verifiable, privacy-preserving infrastructure.</p> <p><strong>Licenses</strong><br>Document: Creative Commons Attribution 4.0 International (CC-BY-4.0)<br>Code and SDK: Apache License 2.0</p> <p><strong>Keywords</strong><br>zero-knowledge proofs, SNARKs, Merkle proofs, reproducibility, Monte Carlo, extortion prevention, sovereign systems, mechanism design, cryptographic governance</p> |
| title | AEGIS-ZK v2.0: A Verifiably Private, Merkle-Anchored, Zero-Knowledge Platform to Prevent and Defeat Extortion |
| topic | zero-knowledge proofs SNARKs Merkle proofs reproducibility Monte Carlo simulation extortion prevention sovereign systems mechanism design cryptographic governance privacy-preserving infrastructure verifiable computation open research economic modeling independent researcher AEGIS-ZK v2.0 |
| url | https://doi.org/10.5281/zenodo.17317136 |