A multitude of privacy-enhancing technologies (PETs) has been presented recently to solve the privacy problems of contemporary services utilizing cloud computing. Many of them are based on additively homomorphic encryption (AHE) that allows the computation of additions on encrypted data. The main technical obstacles for adaptation of PETs in practical systems are related to performance overheads compared with current privacy-violating alternatives. In this article, we present a hardware/software (HW/SW) codesign for programmable systems-on-chip (SoCs) that is designed for accelerating applications based on the Paillier encryption. Our implementation is a microcode-based multicore architecture that is suitable for accelerating various PETs using AHE with large integer modular arithmetic. We instantiate the implementation in a Xilinx Zynq-7000 programmable SoC and provide performance evaluations in real hardware. We also investigate its efficiency in a high-end Xilinx UltraScale+ programmable SoC. We evaluate the implementation with two target use cases that have relevance in PETs: privacy-preserving computation of squared Euclidean distances over encrypted data and multi-input functional encryption (FE) for inner products. Both of them represent the first hardware acceleration results for such operations, and in particular, the latter one is among the very first published implementation results of FE on any platform.

中文翻译：

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用于隐私增强技术和功能加密的基于可编程 SoC 的加速器

最近提出了多种隐私增强技术 (PET)，以解决利用云计算的当代服务的隐私问题。其中许多基于加法同态加密 (AHE)，允许对加密数据进行加法计算。与当前侵犯隐私的替代方案相比，在实际系统中适应 PET 的主要技术障碍与性能开销有关。在本文中，我们介绍了一种用于可编程片上系统 (SoC) 的硬件/软件 (HW/SW) 协同设计，旨在加速基于 Paillier 加密的应用程序。我们的实现是基于微码的多核架构，适用于使用具有大整数模运算的 AHE 加速各种 PET。我们在 Xilinx Zynq-7000 可编程 SoC 中实例化实现，并在真实硬件中提供性能评估。我们还研究了它在高端 Xilinx UltraScale+ 可编程 SoC 中的效率。我们使用与 PET 相关的两个目标用例来评估实现：加密数据上平方欧几里得距离的隐私保护计算和内积的多输入函数加密 (FE)。它们都代表了此类操作的第一个硬件加速结果，尤其是后者是 FE 在任何平台上最早发布的实现结果之一。我们使用与 PET 相关的两个目标用例来评估实现：加密数据上平方欧几里得距离的隐私保护计算和内积的多输入函数加密 (FE)。它们都代表了此类操作的第一个硬件加速结果，尤其是后者是 FE 在任何平台上最早发布的实现结果之一。我们使用与 PET 相关的两个目标用例来评估实现：加密数据上平方欧几里得距离的隐私保护计算和内积的多输入函数加密 (FE)。它们都代表了此类操作的第一个硬件加速结果，尤其是后者是 FE 在任何平台上最早发布的实现结果之一。