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Indistinguishability Obfuscation from Well-Founded Assumptions

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Constructs iO by reducing it to a structured seed PRG built from two assumptions: sparse LPN and an NC0 Boolean PRG with stretch n^{1+τ} (τ arbitrarily small, e.g. 0.001). Public seed is LPN samples as public seed that hide the input; a short private seed stores precomputed low-degree monomials and compressed correction data via low-rank matrix bucketing to fix about M·λ/L^δ errors. Evaluation uses bilinear (degree‑2) operations; security loses a 1/λ factor but admits amplification.

AI-written summary · about

  1. 0:00Introduction
  2. 0:53Learning Parity with Noise
  3. 3:58Boolean PRGs
  4. 6:50Structured PRGs
  5. 10:23Implausibility Results
  6. 13:30Constructing the Model
  7. 27:49Finding a Short Seed
  8. 28:29Preprocessing
  9. 31:29Bad output bits
  10. 35:24One naive approach
  11. 54:18Questions
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