A new fundamental and secure group key management approach with a group controller GC using the theory of polynomial functions over a vector space over finite field is developed, where each member in the group corresponds to a vector in the vector space and the GC computes a central vector, whose inner product with every member's ID vector are identical. The central vector is published and each member can compute a common group key via inner product. The security relies on the fact that any illegitimate user cannot calculate this value without the legitimate vector, therefore cannot derive the group key. This approach is secure and its backward and forward secrecy can be guaranteed. The performance of our approach is analyzed to demonstrate its advantages in comparison with others, which include: 1) it requires both small memory and little computations for each group member; 2) it can handle massive membership change efficiently with only two re-keying messages, i.e., the central vector and a random number; 3) it is very efficient and very scalable for large size groups. Our experiments confirm these advantages and the implementation of our prototype presents very satisfactory performance for large size groups.

In this paper, we prove that the degree of regularity of the family of Square systems, an HFE type of systems, over a prime finite field of odd characteristics q is exactly q, and therefore prove that \begin{itemize} \item inverting Square systems algebraically is exponential, when q=O(n), where n is the number of variables of the system. \end{itemize}

In this paper, we prove a closed formula for the degree of regularity of the family of HFE- (HFE Minus) multivariate public key cryptosystems over a finite field of size q. The degree of regularity of the polynomial system derived from an HFE- system is less than or equal to \begin{eqnarray*} \frac{(q-1)(\lfloor \log_q(D-1)\rfloor +a)}2 +2 & & \text{if is even and is odd,} \\ \frac{(q-1)(\lfloor \log_q(D-1)\rfloor+a+1)}2 +2 & & \text{otherwise.} \end{eqnarray*} Here q is the base field size, D the degree of the HFE polynomial, r=\lfloor \log_q(D-1)\rfloor +1 and a is the number of removed equations (Minus number). This allows us to present an estimate of the complexity of breaking the HFE Challenge 2: \begin{itemize} \item the complexity to break the HFE Challenge 2 directly using algebraic solvers is about 2^{96}. \end{itemize}

In this paper, we present a new algorithm, such that, for the learning with errors (LWE) problems, if the errors are bounded -- the errors do not span the whole prime finite field F_q but a fixed known subset of size D (D<q), which we call the learning with bounded errors (LWBE) problems, we can solve it with complexity O(n^D).

In this paper, we present a new algorithm, such that, for the small integer solution (SIS) problem, if the solution is bounded ( by an integer \beta in l_\infty norm, which we call a bounded SIS (BSIS) problem, {\it and if the difference between the row dimension n and the column dimension m of the corresponding matrix is relatively small with respect the row dimension m}, we can solve it easily with a complexity of polynomial in m.