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Laconic Function Evaluation and ABE for RAMs from (Ring-)LWE

Authors:
Fangqi Dong , IIIS, Tsinghua University
Zihan Hao , IIIS, Tsinghua University
Ethan Mook , Northeastern University
Hoeteck Wee , NTT Research and ENS, Paris
Daniel Wichs , Northeastern University and NTT Research
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DOI: 10.1007/978-3-031-68382-4_4 (login may be required)
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Presentation: Slides
Conference: CRYPTO 2024
Abstract: Laconic function evaluation (LFE) allows us to compress a circuit $f$ into a short digest. Anybody can use this digest as a public-key to efficiently encrypt some input $x$. Decrypting the resulting ciphertext reveals the output $f(x)$, while hiding everything else about $x$. In this work we consider LFE for \emph{Random-Access Machines} (RAM-LFE) where, instead of a circuit $f$, we have a RAM program $f_{\DB}$ that potentially contains some large hard-coded data $\DB$. The decryption run-time to recover $f_{\DB}(x)$ from the ciphertext should be roughly the same as a plain evaluation of $f_{\DB}(x)$ in the RAM model, which can be sublinear in the size of $\DB$. Prior works constructed LFE for circuits under LWE, and RAM-LFE under indisitinguishability obfuscation (iO) and Ring-LWE. In this work, we construct RAM-LFE with essentially optimal encryption and decryption run-times from just Ring-LWE and a standard circular security assumption, without iO. RAM-LFE directly yields 1-key succinct functional encryption and reusable garbling for RAMs with similar parameters. If we only want an \emph{attribute-based} LFE for RAMs (RAM-AB-LFE), then we can replace Ring-LWE with plain LWE in the above. Orthogonally, if we only want \emph{leveled} schemes, where the encryption/decryption efficiency can scale with the depth of the RAM computation, then we can remove the need for a circular-security. Lastly, we also get a leveled many-key \emph{attribute-based encryption for RAMs (RAM-ABE)}, from LWE.
BibTeX
@inproceedings{crypto-2024-34199,
  title={Laconic Function Evaluation and ABE for RAMs from (Ring-)LWE},
  publisher={Springer-Verlag},
  doi={10.1007/978-3-031-68382-4_4},
  author={Fangqi Dong and Zihan Hao and Ethan Mook and Hoeteck Wee and Daniel Wichs},
  year=2024
}