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- Replace fixed 1MB decoy sizes with realistic random distribution
- 40% small files (1KB-100KB), 35% medium (100KB-1MB), 20% large (1MB-10MB), 5% very large (10MB-20MB)
- Maintains deterministic behavior - same invalid URN always returns same size
- Enhances zero-knowledge properties by making decoys indistinguishable from real content
- Updated both CLI get command and encrypted archive layer generation
- Maximum decoy size capped at 20MB for practical resource usage
To ensure decoys are indistinguishable from real content, the system generates deterministic random file sizes that follow realistic file size distributions:
- Memory overhead: Variable based on realistic size distribution (avg ~5MB)
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### 8.2 Storage Efficiency
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**Key Generation Performance:**
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- URN transformation: <1ms per operation
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- Encryption key derivation: <1ms per operation
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- Decoy data generation: <10ms for 1MB
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- Decoy size calculation: <1μs per operation
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- Decoy data generation: <0.1ms per MB (variable size)
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**Storage Operations:**
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- Encrypted commit: >1,000 files/s with encryption enabled
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This zero-knowledge content-addressable storage system represents a significant advancement in privacy-preserving storage technology. By combining URN-based encryption, cryptographic address transformation, and deterministic decoy data generation, the system achieves true zero-knowledge properties where storage providers cannot determine content existence, cannot decrypt stored data, and cannot correlate publisher activities.
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The implementation demonstrates that zero-knowledge storage is practical and efficient, with minimal computational overhead and strong security guarantees. This technology enables new applications in distributed storage, censorship resistance, and privacy-preserving content distribution.
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## References
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1. Goldwasser, S., Micali, S., & Rackoff, C. (1985). The knowledge complexity of interactive proof-systems. SIAM Journal on Computing, 18(1), 186-208.
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2. Bellare, M., & Rogaway, P. (2000). Authenticated encryption: Relations among notions and analysis of the generic composition paradigm. In International Conference on the Theory and Application of Cryptology and Information Security (pp. 531-545).
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3. Rabin, M. O. (1981). Fingerprinting by random polynomials. Center for Research in Computing Technology, Harvard University.
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4. Merkle, R. C. (1987). A digital signature based on a conventional encryption function. In Conference on the Theory and Application of Cryptographic Techniques (pp. 369-378).
*This whitepaper describes the theoretical foundations and practical implementation of the zero-knowledge content-addressable storage system implemented in Digstore Min. The system provides strong privacy guarantees while maintaining practical performance characteristics suitable for real-world deployment.*
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The implementation demonstrates that zero-knowledge storage is practical and efficient, with minimal computational overhead and strong security guarantees. This technology enables new applications in distributed storage, censorship resistance, and privacy-preserving content distribution.
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