Is this problem based on a known hard problem? Suppose I generated an $n$-dimensional vector $a_{(1)} = [a_1, \dotsc, a_n]$ with integer component (actually I can generate as many $a_{(i)}$ as possible). Now I need to get an vector $b = [b_1, \dotsc, b_n]$ such that $\langle a_{(i)}, b \rangle = c_{(i)}$, for all $i$. I don't know the value of $c_{(i)}$ but I can verify whether $c_{(i)}$ is correct (by an decryption algorithm which have $c_{(i)}$ as the key, like AES).
I want to know, is this problem hard? If true, what hard problem is it based on? I've read materials about the knapsack problem, subset sum problem even integer programming. But I don't think that they match.
Edited on Dec 3, 2014: Allow me to modify the question described above because it may not be meaningful. Now I have a group of equations $Ba = c$, where $B$ is an unknown matrix, $a$ and $c$ are known vectors. They are all $n$-dimensional (the previous question is one equation of this group, and I remove the restriction to $c$ because if I want to realize better security, I need to assume that $c$ is known). So in group $1$, we have
$$
Ba^{(1)} = \left[
\begin{array}{cccc}
b_{11} &amp; b_{21} &amp; \cdots &amp; b_{1n}\\
b_{21} &amp; b_{22} &amp; \cdots &amp; b_{2n}\\
\vdots &amp; \vdots &amp; \ddots &amp; \vdots\\
b_{n1} &amp; b_{n2} &amp; \cdots &amp; b_{nn}
\end{array}
\right] \cdot \left[
\begin{array}{c}
a_1\\
a_2\\
\vdots\\
a_n
\end{array}
\right] = \left[
\begin{array}{c}
c_1\\
c_2\\
\vdots\\
c_n
\end{array}
\right] = c^{(1)}
$$
Now my question is:

As far as I know, if we have $n$ groups of $a^{(i)}$ and $c^{(i)}$ such that $Ba^{(i)} = c^{(i)}$, we can calculate $B$ easily. But what if we can get at most $n-1$ groups? I think there are infinite solutions, but can we recover part of $B$? Or all of the $\{b_{ij}\}$ are indeterminate? (Suppose $\{a^{(i)}\}$ are independent vector set.)
What if $B$ is an orthogonal matrix? How many groups are enough to recover $B$? And is it hard to calculate $B$ if $n$ is big?
This problem hugely depends on the distributions of the $c_{(i)}$, if they are independant or not, also on the size of the space in which each coordinate of the vectors are, and on the information you gain with each check if $c_{(i)}$ is correct.

Assuming the vector space is $Z_q^n$ and $n$ is polynomial in $\log q$, that $c_{(i)}$ are independant and uniformly distributed, and that your verification algorithm only tells you if $c_{(i)}$ is correct or not, let us look at the problem for one instance of $a$, $b$ and $c$. 

If you take $b$ uniformly at random, you have probability $\frac{1}{q}$ to have $⟨a,b⟩=c$, which is exponentially small. 

Moreover, there is no way to improve brute force on the value of $⟨a,b⟩$ because you don't learn anything beside the correctness of your $b$, so this is a problem you would call hard is cryptography. 
Even if you have a polynomial number of instances, this won't help you that much because finding a $b$ such that even only one $⟨a_i,b_i⟩=c_i$ is already exponentially hard.

Of course, this is an information theoretical approach to the complexity of this problem, and I think the real assumption you want to reduce to is one that would allow you to use my results, i.e. an assumption which makes you learn nothing when you verify $⟨a,b⟩=c$.

I know this is not really a reduction, but that's all I've got to help you, I hope it is enough.

Let me edit to reply to yours.

First, say you have $q$ groups of $a^{(i)}$ and $c^(i)$.

Now if you know $c$, you are just asking to solve a system of $qn$ linear equations with $n^2$ unknown. Now I think the subject is quite known and you can find a lot of documentation on it easily.

Remark that $B$ being an orthogonal matrix corresponds to adding $n$ equations to the system. Even though they are no more linear, I think the subject has already been studied.

As for the complexity questions, if I'm not wrong you can solve the problem with an algorithm running in time polynomial in $n$, so one would consider it easy.