We construct the first fully homomorphic encryption (FHE) scheme that encrypts matrices and supports homomorphic matrix addition and multiplication. This is a natural extension of packed FHE and thus supports more complicated homomorphic operations. We optimize the bootstrapping procedure of Alperin-Sheriff and Peikert (CRYPTO 2014) by applying our scheme. Our optimization decreases the lattice approximation factor from Õ(n3) to Õ(n2.5). By taking a lattice dimension as a larger polynomial in a security parameter, we can also obtain the same approximation factor as the best known one of standard lattice-based public-key encryption without successive dimension-modulus reduction, which was essential for achieving the best factor in prior works on bootstrapping of standard lattice-based FHE.

This paper proposes fast elliptic curve multiplication algorithms resistant against side channel attacks, based on the Montgomery-type scalar multiplication. The proposed scalar multiplications can be applied to all curves over prime fields, e.g., any standardized curves over finite fields with characteristic larger than 3. The method utilizes the addition formulas xECDBL and xECADD assembled by only x-coordinates of points, and is applicable for any types of curves over finite fields. Then, we encapsulate two addition formulas into one formula xECADDDBL, which accomplishes a faster computation because several auxiliary variables of two formulas can be shared. We also develop a novel addition chain for the new formula, with which we can compute scalar multiplications. The improvement of our scalar multiplications over previous Coron's dummy operation method is about 18% for a 160-bit scalar multiplication. Our method requires no table-up of precomputed points and it is suitable for the implementation on memory constraint computing architectures, e.g., smart cards. Moreover, we optimize the proposed algorithms for parallelized implementations with SIMD operations. Compared with the similar scheme proposed by Fischer et al., our scheme is about 16% faster.

The Single Instruction, Multiple Data (SIMD) architecture enables computation in parallel on a single processor. The SIMD operations are implemented on some processors such as Pentium 3/4, Athlon, SPARC, or even on smart cards. This paper proposes efficient algorithms for assembling an elliptic curve addition (ECADD), doubling (ECDBL), and k-iterated ECDBL (k-ECDBL) with SIMD operations. We optimize the number of auxiliary variables and the order of basic field operations used for these addition formulas. If an addition chain has k-bit zero run, we can replace k-time ECDBLs to the proposed faster k-ECDBL and the total efficiency of the scalar multiplication can be improved. Using the singed binary chain, we can compute a scalar multiplication about 10% faster than the previously fastest algorithm proposed by Aoki et al. Combined with the sliding window method or the width-w NAF window method, we also achieve about 10% faster parallelized scalar multiplication algorithms with SIMD operations. For the implementation on smart cards, we establish two fast parallelized scalar multiplication algorithms with SIMD resistant against side channel attacks.