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Aayush Jain
Researcher at University of California, Los Angeles
Publications - 51
Citations - 1051
Aayush Jain is an academic researcher from University of California, Los Angeles. The author has contributed to research in topics: Functional encryption & Encryption. The author has an hindex of 15, co-authored 46 publications receiving 643 citations.
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Threshold Cryptosystems From Threshold Fully Homomorphic Encryption.
Dan Boneh,Rosario Gennaro,Steven Goldfeder,Aayush Jain,Sam Kim,Peter M. R. Rasmussen,Amit Sahai +6 more
TL;DR: A general approach to adding a threshold functionality to a large class of (non-threshold) cryptographic schemes, and introduces a new concept, called a universal thresholdizer, from which many threshold systems are possible.
Book ChapterDOI
Threshold Cryptosystems from Threshold Fully Homomorphic Encryption
Dan Boneh,Rosario Gennaro,Steven Goldfeder,Aayush Jain,Sam Kim,Peter M. R. Rasmussen,Amit Sahai +6 more
TL;DR: In this article, a general approach to adding a threshold functionality to a large class of (non-threshold) cryptographic schemes was developed, which enables a secret key to be split into a number of shares, so that only a threshold of parties can use the key, without reconstructing the key.
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Indistinguishability Obfuscation from Well-Founded Assumptions
TL;DR: In this article, the authors show how to construct indistinguishability obfuscation from subexponential hardness of four well-founded assumptions, including the SXDH assumption on asymmetric bilinear groups of a prime order.
Book ChapterDOI
Indistinguishability Obfuscation Without Multilinear Maps: New Paradigms via Low Degree Weak Pseudorandomness and Security Amplification
TL;DR: The existence of secure indistinguishability obfuscators (\(i\mathcal {O}\)) has far-reaching implications, significantly expanding the scope of problems amenable to cryptographic study.
Proceedings ArticleDOI
Indistinguishability obfuscation from well-founded assumptions
TL;DR: Barak et al. as discussed by the authors constructed indistinguishability obfuscation from subexponential hardness of four well-founded assumptions, including the Learning Parity with Noise (LPN) assumption over general prime fields ℤp with polynomially many LPN samples and error rate 1/lδ.