Indistinguishability Obfuscation from Well-Founded Assumptions
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Abstract (arXiv)
In this work, we show how to construct indistinguishability obfuscation from subexponential hardness of four well-founded assumptions. We prove: Let τ ∈ (0,∞), δ ∈ (0,1), ε ∈ (0,1) be arbitrary constants. Assume sub-exponential security of the following assumptions, where λ is a security parameter, and the parameters ℓ,k,n below are large enough polynomials in λ: - The SXDH assumption on asymmetric bilinear groups of a prime order p = O(2λ), - The LWE assumption over mathbbZp with subexponential modulus-to-noise ratio 2kε, where k is the dimension of the LWE secret, - The LPN assumption over mathbbZp with polynomially many LPN samples and error rate 1/ℓδ, where ℓ is the dimension of the LPN secret, - The existence of a Boolean PRG in mathsfNC0 with stretch n1+τ, Then, (subexponentially secure) indistinguishability obfuscation for all polynomial-size circuits exists.
Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). First posted on arXiv 21 Aug 2020.
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Citing papers per year: 2021: 2; 2022: 3; 2023: 0; 2024: 5; 2025: 0; 2026: 2.
12 papers.
- 2022Planting Undetectable Backdoors in Machine Learning Modelscited by 59
- 2024SLIP: Securing LLMs IP Using Weights Decompositioncited by 14
- 2022Minimax Optimality (Probably) Doesn't Imply Distribution Learning for GANscited by 6
- 2024Private PAC Learning May be Harder than Online Learningcited by 4
- 2024Injecting Undetectable Backdoors in Obfuscated Neural Networks and Language Modelscited by 3
- 2021Spoofing Generalization: When Can't You Trust Proprietary Models?cited by 2
- 2024Backdoor defense, learnability and obfuscationcited by 2
- 2021Mitigating Adversarial Attacks by Distributing Different Copies to Different Userscited by 1
- 2024On the Hardness of Learning One Hidden Layer Neural Networkscited by 1
- 2026Statistically Undetectable Backdoors in Deep Neural Networkscited by 1
- 2022One Picture is Worth a Thousand Words: A New Wallet Recovery Processcited by 0
- 2026Cheap to Hypothesize, Costly to Verify: The Defense Surface of Agentic Vulnerability Discoverycited by 0
- 2026Cheap to Hypothesize, Costly to Verify: The Defense Surface of Agentic Vulnerability Discovery28 Sep 2026
- 2026Statistically Undetectable Backdoors in Deep Neural Networks10 Jul 2026
- 2024On the Hardness of Learning One Hidden Layer Neural Networks4 Oct 2024
- 2024Backdoor defense, learnability and obfuscation4 Sep 2024
- 2024SLIP: Securing LLMs IP Using Weights Decomposition15 Jul 2024
- 2024Injecting Undetectable Backdoors in Obfuscated Neural Networks and Language Models9 Jun 2024
- 2024Private PAC Learning May be Harder than Online Learning16 Feb 2024
- 2022One Picture is Worth a Thousand Words: A New Wallet Recovery Process5 May 2022
- 2022Planting Undetectable Backdoors in Machine Learning Models14 Apr 2022
- 2022Minimax Optimality (Probably) Doesn't Imply Distribution Learning for GANs18 Jan 2022
- 2021Mitigating Adversarial Attacks by Distributing Different Copies to Different Users30 Nov 2021
- 2021Spoofing Generalization: When Can't You Trust Proprietary Models?15 Jun 2021