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  4. A Custom Accelerator for Homomorphic Encryption Applications - 2017
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Category: MTech VLSI Projects
By MTech Projects
MTech Projects
11.Oct
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A Custom Accelerator for Homomorphic Encryption Applications - 2017

PROJECT TITLE :

A Custom Accelerator for Homomorphic Encryption Applications - 2017

ABSTRACT:

Once the introduction of initial absolutely homomorphic encryption theme in 2009, varied analysis work has been printed aiming at creating totally homomorphic encryption sensible for daily use. The first fully purposeful theme and a few others that are introduced has been proven difficult to be used in sensible applications, due to potency reasons. Here, we tend to propose a custom hardware accelerator, which is optimized for a class of reconfigurable logic, for Lopez-Alt, Tromer and Vaikuntanathan's somewhat homomorphic encryption based schemes. Our style is working as a co-processor that allows the operating system to offload the foremost compute-serious operations to this specialized hardware. The core of our style is an efficient hardware implementation of a polynomial multiplier as it is the foremost compute-heavy operation of our target scheme. The presented design will compute the product of very-large polynomials in underneath vi.25 ms which is 102 times faster than its software implementation. In case of accelerating homomorphic applications; we estimate the per block homomorphic AES as 442 ms that is 28.5 and 17 times faster than the CPU and GPU implementations, respectively. In analysis of Prince block cipher homomorphically, we tend to estimate the performance as 52 ms which is 66 times faster than the CPU implementation.

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Previous article: A novel method of decoding the BCH code based on norm syndrome to improve the error correction efficiency - 2017 A novel method of decoding the BCH code based on norm syndrome to improve the error correction efficiency - 2017 Next article: A Scalable Network-on-Chip Microprocessor With 2.5D Integrated Memory and Accelerator - 2017 A Scalable Network-on-Chip Microprocessor With 2.5D Integrated Memory and Accelerator - 2017
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