Under the auspices of the Computational Complexity Foundation (CCF)

REPORTS > KEYWORD > LEARNING WITH ERRORS:
Reports tagged with learning with errors:
TR08-100 | 14th November 2008
Chris Peikert

Public-Key Cryptosystems from the Worst-Case Shortest Vector Problem

We construct public-key cryptosystems that are secure assuming the
\emph{worst-case} hardness of approximating the length of a shortest
nonzero vector in an $n$-dimensional lattice to within a small
$\poly(n)$ factor. Prior cryptosystems with worst-case connections
were based either on the shortest vector problem for a \emph{special
class} of lattices ... more >>>

TR10-066 | 14th April 2010
Sanjeev Arora, Rong Ge

Learning Parities with Structured Noise

Revisions: 1

In the {\em learning parities with noise} problem ---well-studied in learning theory and cryptography--- we
have access to an oracle that, each time we press a button,
returns a random vector $a \in \GF(2)^n$ together with a bit $b \in \GF(2)$ that was computed as
$a\cdot u +\eta$, where ... more >>>

TR11-109 | 9th August 2011
Zvika Brakerski, Vinod Vaikuntanathan

Efficient Fully Homomorphic Encryption from (Standard) LWE

We present a fully homomorphic encryption scheme that is based solely on the (standard) learning with errors (LWE) assumption. Applying known results on LWE, the security of our scheme is based on the worst-case hardness of short vector problems'' on arbitrary lattices.

Our construction improves on previous works in two ... more >>>

TR14-106 | 9th August 2014
Craig Gentry

Computing on the edge of chaos: Structure and randomness in encrypted computation

This survey, aimed mainly at mathematicians rather than practitioners, covers recent developments in homomorphic encryption (computing on encrypted data) and program obfuscation (generating encrypted but functional programs). Current schemes for encrypted computation all use essentially the same "noisy" approach: they encrypt via a noisy encoding of the message, they decrypt ... more >>>

TR19-041 | 7th March 2019
Srinivasan Arunachalam, Alex Bredariol Grilo, Aarthi Sundaram

Quantum hardness of learning shallow classical circuits

In this paper we study the quantum learnability of constant-depth classical circuits under the uniform distribution and in the distribution-independent framework of PAC learning. In order to attain our results, we establish connections between quantum learning and quantum-secure cryptosystems. We then achieve the following results.

1) Hardness of learning ... more >>>

TR20-080 | 19th May 2020
Joan Bruna, Oded Regev, Min Jae Song, Yi Tang

Continuous LWE

We introduce a continuous analogue of the Learning with Errors (LWE) problem, which we name CLWE. We give a polynomial-time quantum reduction from worst-case lattice problems to CLWE, showing that CLWE enjoys similar hardness guarantees to those of LWE. Alternatively, our result can also be seen as opening new avenues ... more >>>

ISSN 1433-8092 | Imprint