Indistinguishability Obfuscation from Functional Encryption
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Indistinguishability Obfuscation from Functional Encryption.pdf
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460.64 KB
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Author(s)
Bitansky, Nir
Vaikuntanathan, Vinod
Date Issued
December 2015
Journal
2015 IEEE 56th Annual Symposium on Foundations of Computer Science
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Bitansky, Nir, and Vinod Vaikuntanathan. "Indistinguishability Obfuscation from Functional Encryption." 2015 IEEE 56th Annual Symposium on Foundations of Computer Science (FOCS), 17-20 October 2015, Berkeley, CA, 2015, pp. 171–90.
Version
Original manuscript
Abstract
Indistinguishability obfuscation (IO) is a tremendous notion, powerful enough to give rise to almost any known cryptographic object. So far, candidate IO constructions were based on specific assumptions on algebraic objects called multi-linear graded encodings. We present a generic construction of indistinguishability obfuscation from public-key functional encryption with succinct cipher texts and sub-exponential security. This shows the equivalence of indistinguishability obfuscation and public-key functional encryption, a primitive that has so far seemed to be much weaker, lacking the power and the staggering range of applications of indistinguishability obfuscation. As an application, we obtain a new candidate IO construction based on the functional encryption scheme of Garg, Gentry, Halevi, and Zhan dry [Eprint 14] under their assumptions on multi-linear graded encodings. We also show that, under the Learning with Errors assumptions, our techniques imply that any indistinguishability obfuscator can be converted to one where obfuscated circuits are of linear size in the size of the original circuit plus a polynomial overhead in its depth. Our reduction highlights the importance of cipher text succinctness in functional encryption schemes, which we hope will serve as a pathway to new IO constructions based on solid cryptographic foundations.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1109/FOCS.2015.20
