Learning with errors

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In cryptography, learning with errors (LWE) is a mathematical problem that is widely used to create secure encryption algorithms. It is based on the idea of representing secret information as a set of equations with errors. In other words, LWE is a way to hide the value of a secret by introducing noise to it. In more technical terms, it refers to the computational problem of inferring a linear n {\displaystyle n} -ary function f {\displaystyle f} over a finite ring from given samples y i = f ( x i ) {\displaystyle y_{i}=f(\mathbf {x} _{i})} some of which may be erroneous. The LWE problem is conjectured to be hard to solve, and thus to be useful in cryptography. More precisely, the LWE problem is defined as follows. Let Z q {\displaystyle \mathbb {Z} _{q}} denote the ring of integers modulo q {\displaystyle q} and let Z q n {\displaystyle \mathbb {Z} _{q}^{n}} denote the set of n {\displaystyle n} -vectors over Z q {\displaystyle \mathbb {Z} _{q}} . There exists a certain unknown linear function f : Z q n → Z q {\displaystyle f:\mathbb {Z} _{q}^{n}\rightarrow \mathbb {Z} _{q}} , and the input to the LWE problem is a sample of pairs ( x , y ) {\displaystyle (\mathbf {x} ,y)} , where x ∈ Z q n {\displaystyle \mathbf {x} \in \mathbb {Z} _{q}^{n}} and y ∈ Z q {\displaystyle y\in \mathbb {Z} _{q}} , so that with high probability y = f ( x ) {\displaystyle y=f(\mathbf {x} )} . Furthermore, the deviation from the equality is according to some known noise model. The problem calls for finding the function f {\displaystyle f} , or some close approximation thereof, with high probability. The LWE problem was introduced by Oded Regev in 2005 (who won the 2018...