Correlated secret randomness is a useful resource for many cryptographic applications. We
initiate the study of pseudorandom correlation functions (PCFs) that offer the ability to …

Learning parity with noise (LPN) has been widely studied and used in cryptography. It was
recently brought to new prosperity since Boyle et al.(CCS'18), putting LPN to a central role in …

Collapsing is a post-quantum strengthening of collision resistance, needed to lift many
classical results to the quantum setting. Unfortunately, the only existing standard-model …

It is a long standing open problem to find search to decision reductions for structured
versions of the decoding problem of linear codes. Such results in the lattice-based setting …

In this work we introduce a new (circuit-dependent) homomorphic secret sharing (HSS)
scheme for all log/log log-local circuits, with communication proportional only to the width of …

Learning parity with noise (LPN) is a notorious (average-case) hard problem that has been
well studied in learning theory, coding theory and cryptography since the early 90's. It further …

Homomorphic encryption enables public computation over encrypted data. In the past few
decades, homomorphic encryption has become a staple of both the theory and practice of …

Hard learning problems are important building blocks for the design of various cryptographic
functionalities such as authentication protocols and post-quantum public key encryption. The …

Over the past few decades, we have seen a proliferation of advanced cryptographic
primitives with lossy or homomorphic properties built from various assumptions such as …

We give a quantum reduction from finding short codewords in a random linear code to
decoding for the Hamming metric. This is the first time such a reduction (classical or …