Fully Homomorphic Encryption (FHE) allows a third party to perform arbitrary computations on encrypted data, learning neither the inputs nor the computation results. Hence, it provides resilience in situations where computations are carried out by an untrusted or potentially compromised party. This powerful concept was first conceived by Rivest et al. in the 1970s. However, it remained unrealized until Craig Gentry presented the first feasible FHE scheme in 2009. The advent of the massive collection of sensitive data in cloud services, coupled with a plague of data breaches, moved highly regulated businesses to increasingly demand confidential and secure computing solutions. This demand, in turn, has led to a recent surge in the development of FHE tools. To understand the landscape of recent FHE tool developments, we conduct an extensive survey and experimental evaluation to explore the current state of the art and identify areas for future development. In this paper, we survey, evaluate, and systematize FHE tools and compilers. We perform experiments to evaluate these tools' performance and usability aspects on a variety of applications. We conclude with recommendations for developers intending to develop FHE-based applications and a discussion on future directions for FHE tools development.
翻译:完全基因加密(FHE)允许第三方对加密数据进行任意计算,既不学习投入,也不学习计算结果。因此,它提供了在未经信任或潜在受损害方进行计算的情况下的复原力。这个强大的概念是Rivest等人在1970年代首次构想的。然而,直到Craig Gentry在2009年提出第一个可行的FHE计划之前,它仍未实现。在云服务中大规模收集敏感数据,同时发生数据破损,使监管严密的企业越来越多地要求保密和安全的计算解决方案。这种需求又导致FHE工具开发最近出现激增。为了了解FHE工具的最新发展情况,我们进行了广泛的调查和实验性评估,以探索艺术的现状,并确定未来开发FHE工具的领域。在这份文件中,我们调查、评估和系统化FHE工具和编译者。我们进行了实验,以评价这些工具在各种应用方面的性能和可用性。我们最后建议开发者打算开发FHE应用程序,并讨论FHE工具开发的未来方向。