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REPORTS > KEYWORD > LEARNING PARITY WITH NOISE:
Reports tagged with Learning Parity with Noise:
TR14-033 | 10th March 2014
Adi Akavia, Andrej Bogdanov, Siyao Guo, Akshay Kamath, Alon Rosen

#### Candidate Weak Pseudorandom Functions in $\mathrm{AC}0 \circ \mathrm{MOD}2$

Revisions: 1

Pseudorandom functions (PRFs) play a fundamental role in symmetric-key cryptography. However, they are inherently complex and cannot be implemented in the class $\mathrm{AC}^0( \mathrm{MOD}_2)$. Weak pseudorandom functions (weak PRFs) do not suffer from this complexity limitation, yet they suffice for many cryptographic applications. We study the minimal complexity requirements for ... more >>>

TR15-094 | 10th June 2015
Eli Ben-Sasson, iddo Ben-Tov, Ivan Bjerre Damgard, Yuval Ishai, Noga Ron-Zewi

#### On Public Key Encryption from Noisy Codewords

Several well-known public key encryption schemes, including those of Alekhnovich (FOCS 2003), Regev (STOC 2005), and Gentry, Peikert and Vaikuntanathan (STOC 2008), rely on the conjectured intractability of inverting noisy linear encodings. These schemes are limited in that they either require the underlying field to grow with the security parameter, ... more >>>

TR18-056 | 20th March 2018
Zvika Brakerski, Vadim Lyubashevsky, Vinod Vaikuntanathan, Daniel Wichs

#### Worst-Case Hardness for LPN and Cryptographic Hashing via Code Smoothing

We present a worst case decoding problem whose hardness reduces to that of solving the Learning Parity with Noise (LPN) problem, in some parameter regime. Prior to this work, no worst case hardness result was known for LPN (as opposed to syntactically similar problems such as Learning with Errors). The ... more >>>

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