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Paper: Indistinguishability Obfuscation from LPN over F_p, DLIN, and PRGs in NC^0

Authors:
Aayush Jain , NTT and CMU
Huijia Lin , UW
Amit Sahai , UCLA
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Presentation: Slides
Conference: EUROCRYPT 2022
Abstract: In this work, we study what minimal sets of assumptions suffice for constructing indistinguishability obfuscation ($\iO$). We prove: {\bf Theorem}(Informal): {\em Assume sub-exponential security of the following assumptions: - the Learning Parity with Noise ($\mathsf{LPN}$) assumption over general prime fields $\mathbb{F}_p$ with polynomially many $\mathsf{LPN}$ samples and error rate $1/k^\delta$, where $k$ is the dimension of the $\mathsf{LPN}$ secret, and $\delta>0$ is any constant; - the existence of a Boolean Pseudo-Random Generator ($\mathsf{PRG}$) in $\mathsf{NC}^0$ with stretch $n^{1+\tau}$, where $n$ is the length of the $\mathsf{PRG}$ seed, and $\tau>0$ is any constant; - the Decision Linear ($\mathsf{DLIN}$) assumption on symmetric bilinear groups of prime order. Then, (subexponentially secure) indistinguishability obfuscation for all polynomial-size circuits exists. Further, assuming only polynomial security of the aforementioned assumptions, there exists collusion resistant public-key functional encryption for all polynomial-size circuits. This removes the reliance on the Learning With Errors (LWE) assumption from the recent work of [Jain, Lin, Sahai STOC'21]. As a consequence, we obtain the first fully homomorphic encryption scheme that does not rely on any lattice-based hardness assumption. Our techniques feature a new notion of randomized encoding called Preprocessing Randomized Encoding (PRE), that essentially can be computed in the exponent of pairing groups. When combined with other new techniques, PRE gives a much more streamlined construction of $\iO$ while still maintaining reliance only on well-studied assumptions.
Video from EUROCRYPT 2022
BibTeX
@inproceedings{eurocrypt-2022-31938,
  title={Indistinguishability Obfuscation from LPN over F_p, DLIN, and PRGs in NC^0},
  publisher={Springer-Verlag},
  author={Aayush Jain and Huijia Lin and Amit Sahai},
  year=2022
}