Pseudorandom Functions in Almost Constant Depth from Low-Noise LPN

Pseudorandom functions (PRFs) play a central role in symmetric cryptography. While in principle they can be built from any one-way functions by going through the generic HILL (SICOMP 1999) and GGM (JACM 1986) transforms, some of these steps are inherently sequential and far from practical. Naor, Reingold (FOCS 1997) and Rosen (SICOMP 2002) gave parallelizable constructions of PRFs in NC2 and TC0 based on concrete number-theoretic assumptions such as DDH, RSA, and factoring. Banerjee, Peikert, and Rosen (Eurocrypt 2012) constructed relatively more efficient PRFs in NC1 and TC0 based on “learning with errors” (LWE) for certain range of parameters. It remains an open problem whether parallelizable PRFs can be based on the “learning parity with noise” (LPN) problem for both theoretical interests and efficiency reasons (as the many modular multiplications and additions in LWE would then be simplified to AND and XOR operations under LPN). In this paper, we give more efficient and p...

• Type: Journal
• Year: 2016
• Where: IACR
• Authors: Yu Yu, John P. Steinberger
• Keywords: Biometrics, IACR 2016