Xehe An Intel Gpu Accelerated Fully Homomorphic Encryption Library

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Xehe An Intel Gpu Accelerated Fully Homomorphic Encryption Library A Sycl Sparkler Making The Most Of C And Sycl

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Author : Alexander Lyashevsky
language : en
Publisher: James Reinders
Release Date : 2023-04-02

Xehe An Intel Gpu Accelerated Fully Homomorphic Encryption Library A Sycl Sparkler Making The Most Of C And Sycl written by Alexander Lyashevsky and has been published by James Reinders this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-04-02 with Computers categories.

This installment of a "SYCL Sparkler" explores in depth a way to implement a reasonably efficient implementation for Homomorphic Encryption using modern C++ with SYCL. As a result of their work, the authors learned some valuable optimization techniques and insights that the they have taken time to share in this very interesting and detailed piece. A key value of using C++ with SYCL, is the ability to be portable while supporting the ability to optimize at a lower level when it is deemed worth the effort. This work helps illustrate how the authors isolated that optimization work, and their thought process on how to pick what to optimize. The code for this implementation is available open source online. None of the performance numbers shown are intended to provide guidance on hardware selection. The authors offer their results and observations to illustrate the magnitude of changes that may correspond to the optimizations being discussed. Readers will find the information valuable to motivate their own optimization work on their applications using some of the techniques highlighted by these authors. Key Insights shared include: pros/cons of a hand-tuned vISA, memory allocation overheads, multi-tile scaling, event-based profiling, algorithm tuning, measuring of device throughput, developing with 'dualities' to increase portability and performance portability.

Xehe An Intel Gpu Accelerated Fully Homomorphic Encryption Library

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Author : Alexander Lyashevsky
language : en
Publisher: Codeplay Software Printing
Release Date : 2023-04-02

Xehe An Intel Gpu Accelerated Fully Homomorphic Encryption Library written by Alexander Lyashevsky and has been published by Codeplay Software Printing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-04-02 with categories.

Accelerating Secure Computations Under Fully Homomorphic Encryption

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Author : Alhassan Khedr
language : en
Publisher:
Release Date : 2017

Accelerating Secure Computations Under Fully Homomorphic Encryption written by Alhassan Khedr and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with categories.

Fully homomorphic encryption (FHE) systems enable computations on encrypted data without decrypting and without knowledge of the secret key. In this thesis, we describe an optimized RLWE-based and NTRU-based variants of the FHE system recently proposed by Gentry, Sahai and Waters (GSW). Although the GSW system was widely believed to be less efficient than its contemporaries due to the dimensionality of its ciphertext, we demonstrate quite the opposite behavior. We first highlight and carefully exploit the algebraic features of the system to achieve significant speedup over the state-of-the-art FHE implementations, namely the IBM homomorphic encryption library (HElib) and DARPA's SIPHER implementation. We introduce several optimizations on top of our HE implementation, and use the resulting scheme to construct numerous secure applications. We introduce the first high performance Homomorphic Processing Unit (HPU) hardware accelerator. A carefully crafted parallel GPU implementation of our RLWE scheme running on an NVIDIA GeForce GTX980 achieved a speedup factor of 89,700x compared to DARPA's SIPHER v01 baseline implementation. Our single-staged homomorphic processing unit (HPU) hardware accelerator achieved a speedup factor of 57x compared to our GPU implementation. Our NTRU scheme is mathematically 4x more efficient than our RLWE scheme. In total, our NTRU scheme running on one single-staged HPU unit managed to achieve a combined speedup factor of 2x10^7 compared to DARPA's SIPHER v01 baseline implementation, which is twice the performance target originally set by DARPA's PROCEED program to accelerate fully homomorphic encryption. An additional 4.47x speedup can be achieved by implementing a log(n)-staged HPU unit at the cost of 3x the die area. Finally, by exploiting the computational independence in our FHE schemes and applications, a speedup factor of 10^9 can be achieved by distributing independent computations on 50 single-staged HPU units.

Cpu And Gpu Accelerated Fully Homomorphic Encryption

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Author : Md Toufique Morshed Tamal
language : en
Publisher:
Release Date : 2019

Cpu And Gpu Accelerated Fully Homomorphic Encryption written by Md Toufique Morshed Tamal and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.

Fully Homomorphic Encryption (FHE) is one of the most promising technologies for privacy protection as it allows an arbitrary number of function computations over encrypted data. However, the computational cost of these FHE systems limits their widespread applications. In this thesis, our objective is to improve the performance of FHE schemes by designing efficient parallel frameworks. In particular, we choose Torus Fully Homomorphic Encryption (TFHE) as it offers exact results for an infinite number of boolean gate (e.g., AND, XOR) evaluations. We first extend the gate operations to algebraic circuits such as addition, multiplication, and their vector and matrix equivalents. Secondly, we consider the multi-core CPUs to improve the efficiency of both the gate and the arithmetic operations. Finally, we port the TFHE to the Graphics Processing Units (GPU) and device novel optimizations for boolean and arithmetic circuits employing the multitude of cores. We also experimentally analyze both the CPU and GPU parallel frameworks for different numeric representations (16 to 32-bit). Our GPU implementation outperforms the existing techniques, and it achieves a speedup of 20x for any 32-bit boolean operation and 14.5x for multiplications.

On Architecting Fully Homomorphic Encryption Based Computing Systems

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Author : Rashmi Agrawal
language : en
Publisher: Springer Nature
Release Date : 2023-07-24

On Architecting Fully Homomorphic Encryption Based Computing Systems written by Rashmi Agrawal and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-24 with Technology & Engineering categories.

This book provides an introduction to the key concepts of Fully Homomorphic Encryption (FHE)-based computing, and discusses the challenges associated with architecting FHE-based computing systems. Readers will see that due to FHE’s ability to compute on encrypted data, it is a promising solution to address privacy concerns arising from cloud-based services commonly used for a variety of applications including healthcare, financial, transportation, and weather forecasting. This book explains the fundamentals of the FHE operations and then presents an architectural analysis of the FHE-based computing. The authors also highlight challenges associated with accelerating FHE on various commodity platforms and argue that the FPGA platform provides a sweet spot in making privacy-preserving computing plausible.

Making Computation On Encrypted Data Practical Through Hardware Acceleration Of Fully Homomorphic Encryption

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Author : Nikola Samardzic (Researcher in electrical engineering and computer science)
language : en
Publisher:
Release Date : 2022

Making Computation On Encrypted Data Practical Through Hardware Acceleration Of Fully Homomorphic Encryption written by Nikola Samardzic (Researcher in electrical engineering and computer science) and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.

Fully Homomorphic Encryption (FHE) enables offloading computation to untrusted servers with cryptographic privacy. Despite its attractive security, FHE is not yet widely adopted due to its prohibitive overheads, about 10,000x over unencrypted computation.

Accelerating Homomorphic Encryption In The Cloud Environment Through High Level Synthesis And Reconfigurable Resources

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Author : Michael J. Foster
language : en
Publisher:
Release Date : 2017

Accelerating Homomorphic Encryption In The Cloud Environment Through High Level Synthesis And Reconfigurable Resources written by Michael J. Foster and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with Cloud computing categories.

"The recent surge in cloud services is revolutionizing the way that data is stored and processed. Everyone with an internet connection, from large corporations to small companies and private individuals, now have access to cutting-edge processing power and vast amounts of data storage. This rise in cloud computing and storage, however, has brought with it a need for a new type of security. In order to have access to cloud services, users must allow the service provider to have full access to their private, unencrypted data. Users are required to trust the integrity of the service provider and the security of its data centers. The recent development of fully homomorphic encryption schemes can offer a solution to this dilemma. These algorithms allow encrypted data to be used in computations without ever stripping the data of the protection of encryption. Unfortunately, the demanding memory requirements and computational complexity of the proposed schemes has hindered their wide-scale use. Custom hardware accelerators for homomorphic encryption could be implemented on the increasing number of reconfigurable hardware resources in the cloud, but the long development time required for these processors would lead to high production costs. This research seeks to develop a strategy for faster development of homomorphic encryption hardware accelerators using the process of High-Level Synthesis. Insights from existing number theory software libraries and custom hardware accelerators are used to develop a scalable, proof-of-concept software implementation of Karatsuba modular polynomial multiplication. This implementation was designed to be used with High-Level Synthesis to accelerate the large modular polynomial multiplication operations required by homomorphic encryption. The accelerator generated from this implementation by the High-Level Synthesis tool Vivado HLS achieved significant speedup over the implementations available in the highly-optimized FLINT software library."--Abstract.

Pyfhe A Python Library For Fully Homomorphic Encryption

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Author : Saroja Erabelli
language : en
Publisher:
Release Date : 2020

Pyfhe A Python Library For Fully Homomorphic Encryption written by Saroja Erabelli and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.

Fully homomorphic encryption (FHE) schemes often entail complex lattice operations and error associated with addition and multiplication, making them a challenge to implement. While a few lattice cryptography libraries exist in C++, there is no such library in Python, a language which allows simplicity and readability, making it ideal for prototyping. Many such libraries also do not include bootstrapping, the most complicated operation of FHE schemes. We present a new Python library pyFHE for fully homomorphic encryption schemes, which currently includes the Brakerski-Fan-Vercauteren (BFV) scheme, the Cheon-Kim-Kim-Song (CKKS) scheme, and bootstrapping for CKKS.

Hardware Acceleration For Homomorphic Encryption

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Author : Joël Cathebras
language : en
Publisher:
Release Date : 2018

Hardware Acceleration For Homomorphic Encryption written by Joël Cathebras and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.

In this thesis, we propose to contribute to the definition of encrypted-computing systems for the secure handling of private data. The particular objective of this work is to improve the performance of homomorphic encryption. The main problem lies in the definition of an acceleration approach that remains adaptable to the different application cases of these encryptions, and which is therefore consistent with the wide variety of parameters. It is for that objective that this thesis presents the exploration of a hybrid computing architecture for accelerating Fan and Vercauteren's encryption scheme (FV).This proposal is the result of an analysis of the memory and computational complexity of crypto-calculation with FV. Some of the contributions make the adequacy of a non-positional number representation system (RNS) with polynomial multiplication Fourier transform over finite-fields (NTT) more effective. RNS-specific operations, inherently embedding parallelism, are accelerated on a SIMD computing unit such as GPU. NTT-based polynomial multiplications are implemented on dedicated hardware such as FPGA. Specific contributions support this proposal by reducing the storage and the communication costs for handling the NTTs' twiddle factors.This thesis opens up perspectives for the definition of micro-servers for the manipulation of private data based on homomorphic encryption.

Gpuhelib And Distributedhelib

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Author : Ethan Andrew Frame
language : en
Publisher:
Release Date : 2015

Gpuhelib And Distributedhelib written by Ethan Andrew Frame and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with Data encryption (Computer science) categories.

Homomorphic Encryption, an encryption scheme only developed in the last five years, allows for arbitrary operations to be performed on encrypted data. Using this scheme, a user can encrypt data, and send it to an online service. The online service can then perform an operation on the data and generate an encrypted result. This encrypted result is then sent back to the user, who decrypts it. This decryption produces the same data as if the operation performed by the online service had been performed on the unencrypted data. This is revolutionary because it allows for users to rely on online services, even untrusted online services, to perform operations on their data, without the online service gaining any knowledge from their data.