论文标题
通过桥接模块化和位级算术来加速完全同态加密
Accelerating Fully Homomorphic Encryption by Bridging Modular and Bit-Level Arithmetic
论文作者
论文摘要
现代计算平台中数据泄露的急剧增加强调,访问控制不足以保护敏感的用户数据。密码学的最新进展允许使用完全同型加密(FHE)进行端到端处理加密数据。但是,此类计算仍然比直接(未加密)计算慢的数量级。根据基本的加密方案,FHE方案可以使用布尔电路在比特级别工作,也可以使用模块化算术的整数在比特级工作。整数上的操作仅限于加法/减法和乘法。另一方面,比特级算术更全面,允许更多的操作,例如比较和分裂。尽管模块化算术可以效仿比特级计算,但性能的成本很高。在这项工作中,我们提出了一种称为“桥接”的新方法,该方法将模块化计算更快,限制性的模块化计算与较慢且全面的比特计算融合在一起,使其既可以在同一应用程序中都可以使用,并且具有相同的加密方案实例化。我们介绍和开源C ++类型,代表了两种不同的算术模式,提供了从一种转换为另一个算术模式的可能性。实验结果表明,桥接模块化和比特算术计算可以导致1-2个测试合成基准测试的数量级性能改善,以及一个现实世界中的应用程序:一种基因型插图案例研究。
The dramatic increase of data breaches in modern computing platforms has emphasized that access control is not sufficient to protect sensitive user data. Recent advances in cryptography allow end-to-end processing of encrypted data without the need for decryption using Fully Homomorphic Encryption (FHE). Such computation however, is still orders of magnitude slower than direct (unencrypted) computation. Depending on the underlying cryptographic scheme, FHE schemes can work natively either at bit-level using Boolean circuits, or over integers using modular arithmetic. Operations on integers are limited to addition/subtraction and multiplication. On the other hand, bit-level arithmetic is much more comprehensive allowing more operations, such as comparison and division. While modular arithmetic can emulate bit-level computation, there is a significant cost in performance. In this work, we propose a novel method, dubbed bridging, that blends faster and restricted modular computation with slower and comprehensive bit-level computation, making them both usable within the same application and with the same cryptographic scheme instantiation. We introduce and open source C++ types representing the two distinct arithmetic modes, offering the possibility to convert from one to the other. Experimental results show that bridging modular and bit-level arithmetic computation can lead to 1-2 orders of magnitude performance improvement for tested synthetic benchmarks, as well as one real-world FHE application: a genotype imputation case study.