The core of our technique is to automatically reorganize data in registers to satisfy the alignment requirement imposed by the hardware. An important aspect of this problem, namely, how to vectorize misaligned memory references, still remains unaddressed.This paper presents a compilation scheme that systematically vectorizes loops in the presence of misaligned memory references. Prior research has focused primarily on vectorizing loops where all memory references are properly aligned. When vectorizing for SIMD architectures that are commonly employed by today's multimedia extensions, one of the new challenges that arise is the handling of memory alignment. For instance, our runtimes for key generation/circuit compilation and all online operations are more than one order of magnitude lower for a sample image processing application used for performance evaluation in our study. The HE circuits generated by RAMPARTS are significantly more efficient than the circuits compiled by Cingulata. We compare our approach with Cingulata, the only previously known system that automatically generates circuits for HE computations. Thus, RAMPARTS makes HE more widely available and usable by the the population of programmers. These three operations are not easily performed by programmers who are not HE experts. Third, RAMPARTS automatically selects the plaintext encoding for input values, and performs input and output data transformations. Second, RAMPARTS automatically selects the HE parameters for the generated circuit, which is typically done manually by an HE expert. First, we use symbolic execution to automate the construction of an optimized computation circuit where both the circuit size and multiplicative depth are chosen by the compiler. RAMPARTS makes the following three contributions. RAMPARTS provides an environment for developing HE applications in Julia, a high-level language, the same way as 'cleartext'' applications are typically written in Julia. We present RAMPARTS to address this challenge. A major challenge with homomorphic encryption is that it takes extensive expert knowledge to design meaningful and useful programs that are constructed from atomic HE operations. Homomorphic Encryption (HE) is an emerging technology that enables computing on data while the data is encrypted.

Mathematica compiler