But WHY is the Lattices Bounded Distance Decoding Problem difficult?

Introduction to lattices and the Bounded Distance Decoding Problem

A lattice is a discrete subgroup \mathcal{L} \subset \mathbb{R}^n, where the word discrete means that each x \in \mathcal{L} has a neighborhood in \mathbb{R}^n that, when intersected with \mathcal{L} results in x itself only. One can think of lattices as being grids, although the coordinates of the points need not be integer. Indeed, all lattices are isomorphic to \mathbb{Z}^n, but it may be a grid of points with non-integer coordinates.

Another very nice way to define a lattice is: given n independent vectors b_i \in \mathbb{R}^n, the lattice \mathcal{L} generated by that base is the set of all linear combinations of them with integer coefficients:

    \[\mathcal{L} = \{\sum\limits_{i=0}^{n} z_i b_i, \ b_i \in \mathbb{R}^n, z_i \in \mathbb{Z} \}\]

Then, we can go on to define the Bounded Distance Decoding problem (BDD), which is used in lattice-based cryptography (more specifically, for example in trapdoor homomorphic encryption) and believed to be hard in general.

Given an arbitrary basis of a lattice \mathcal{L}, and a point x \in \mathbb{R}^n not necessarily belonging to \mathcal{L}, find the point of \mathcal{L} that is closest to x. We are also guaranteed that x is very close to one of the lattice points. Notice how we are relying on an arbitrary basis – if we claim to be able to solve the problem, we should be able to do so with any basis.

Bounded Distance Problem example

Now, as the literature goes, this is a problem that is hard in general, but easy if the basis is nice enough. So, for example for encryption, the idea is that we can encode our secret message as a lattice point, and then add to it some small noise (i.e. a small element v \in \mathbb{R}^n). This basically generates an instance of the BDD problem, and then the decoding can only be done by someone who holds the good basis for the lattice, while those having a bad basis are going to have a hard time decrypting the ciphertext.

However, albeit of course there is no proof of this (it is a problem believed to be hard), I wanted to get at least some clue on why it should be easy with a nice basis and hard with a bad one (GGH is an example schema that employs techniques based on this).

So now to our real question. But WHY is the Bounded Distance Decoding problem hard (or easy)?

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Relationship between reduced rings, radical ideals and nilpotent elements

This post aims at providing some intuition and meaning for the following algebra relationship:

Reduced ring – Radical ideal – Nilpotent

Reduced ring – Radical ideal – Nilpotent

A basic fact of ring theory is that if you take a ring A and quotient it for a (double-sided) radical ideal I you get a reduced ring. Let us suppose A is a commutative ring and understand why this fact is true.

Nilpotent element
Def. a \in A is nilpotent \Leftrightarrow \exists n \in \mathbb{N} : a^n = 0

Informally, a nilpotent element is like a road ending in the middle of nowhere, collapsing in the depth of an abyss. You are driving on it, following the powers of a, and then all of a sudden, with no explanation, your road ends in a big black hole. Indeed, the zero really acts as some kind of black hole, attracting nilpotent-made roads at some point or another: we can think of nilpotent roads as spiraling into the zero.

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