# set of integers modulo an integer q in lattice

Some literature about lattices set $$\mathbb{Z}_{q}$$ in $$[-\frac{q}{2}, \frac{q}{2})\cap \mathbb{Z}$$ but not $$[0,q-1]$$ such as "lattice signatures without trapdoors" and "lattice based blind signatures", I don't know the reason. Thank you for explain the reason for me if you know it.

Well, these lattice schemes need to define a 'small vector' $$\epsilon$$ in such a way that the multiplication of two small vector elements $$\epsilon_0 \times \epsilon_1$$ is still small; that is, for a random element $$A$$, we still have $$A \approx A + \epsilon_0 \times \epsilon_1$$

One way to do this is to define a 'small vector' as one whose elements all have small absolute values; if you define 'small absolute value' tightly enough, then the elements of $$\epsilon_0 \times \epsilon_1$$ (ignoring the modulus operation) will still probably  have terms with absolute values less that $$q/k$$ (which is 'close enough' - the original schemes had $$k=4$$).

Of course, you might ask "why don't they define small vectors to have elements with small nonnegative terms?". Well, if they defined the ring over $$x^n+1$$ (for example), then something strange happens. Element $$i$$ of $$A \times B$$ can be expressed as

$$A_iB_0 + A_{i-1}B_1 + … + A_0B_i - A_{n-1}B_{i+1} - A_{n-2} B{i+2} - … - A_{i+1} B_{n-1}$$

Specifically, there are $$n-i$$ terms which are added to the final product, and $$i$$ terms which are subtracted from the final product.

For small $$i$$, there are a large number of added terms; if $$A, B$$ consists of small nonnegative elements, then the result will be strongly weighted toward the positive. For large $$i$$ (just a bit smaller than $$n$$), the result will be strongly weighted toward the negative. Hence, the result will have a distinct slant, and that's harder to reason with.

In contrast, if with generate $$A, B$$ with small absolute values (with no bias between positive and negative), no such bias exists; the distribution of the elements of the result is independent of the position within the vector.

: This is where the probability of decryption failure comes in; most systems don't define the error terms small enough that decryption failure can't happen

A slightly different perspective on the matter is that often a lattice $$\Lambda\subseteq\mathbb{Z}^n$$ is used for its error-correction properties. Lattices are infinite objects, but for efficiency we often assume they're periodic mod some integer (not necessarily prime, despite the choice of variable) $$q$$, which is known as being $$q$$-ary. This gets us a finite size representation of the lattice, but it's know that any lattice is $$\det(\Lambda)^2$$-ary, so such a representation always exists (although generally $$q$$-ary lattices assume $$q$$ to be smaller than $$\det(\Lambda)^2$$). The condition of being $$q$$-ary can be algebraically stated as $$q\mathbb{Z}^n\subseteq\Lambda$$. We can consider the operation $$\Lambda\bmod q\mathbb{Z}^n$$, which has codomain $$[-q/2,\dots q/2]^n\cap \mathbb{Z}^n$$ (and for the lattice $$q\mathbb{Z}^n$$, corresponds to reducing each coordinate of $$\Lambda$$ mod $$q$$ independently).

This is a geometric answer for why $$[-q/2,\dots, q/2]$$ is used rather than $$[0, q-1]$$. While they can both be seen as ways to represent $$\mathbb{Z}/q\mathbb{Z}$$ within $$\mathbb{Z}$$, the former is the fundamental Voronoi cell of the lattice $$q\mathbb{Z}^n$$ (alternatively, the set of elements closer to $$0$$ than any other point in $$q\mathbb{Z}^n$$).