Federated Secure Computing

IF 3.4 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Informatics Pub Date : 2023-10-31 DOI:10.3390/informatics10040083
Hendrik Ballhausen, Ludwig Christian Hinske
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Abstract

Privacy-preserving computation (PPC) enables encrypted computation of private data. While advantageous in theory, the complex technology has steep barriers to entry in practice. Here, we derive design goals and principles for a middleware that encapsulates the demanding cryptography server side and provides a simple-to-use interface to client-side application developers. The resulting architecture, “Federated Secure Computing”, offloads computing-intensive tasks to the server and separates concerns of cryptography and business logic. It provides microservices through an Open API 3.0 definition and hosts multiple protocols through self-discovered plugins. It requires only minimal DevSecOps capabilities and is straightforward and secure. Finally, it is small enough to work in the internet of things (IoT) and in propaedeutic settings on consumer hardware. We provide benchmarks for calculations with a secure multiparty computation (SMPC) protocol, both for vertically and horizontally partitioned data. Runtimes are in the range of seconds on both dedicated workstations and IoT devices such as Raspberry Pi or smartphones. A reference implementation is available as free and open source software under the MIT license.
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联邦安全计算
隐私保护计算(PPC)允许对私有数据进行加密计算。这种复杂的技术虽然在理论上是有利的,但在实践中却有很高的进入门槛。在这里,我们推导了中间件的设计目标和原则,该中间件封装了要求苛刻的加密服务器端,并为客户端应用程序开发人员提供了一个简单易用的接口。由此产生的体系结构“联邦安全计算”将计算密集型任务卸载到服务器,并将加密和业务逻辑的关注点分开。它通过开放API 3.0定义提供微服务,并通过自己发现的插件承载多种协议。它只需要最少的DevSecOps功能,并且简单而安全。最后,它足够小,可以在物联网(IoT)和消费者硬件的推广环境中工作。我们为使用安全多方计算(SMPC)协议的计算提供基准测试,包括垂直和水平分区的数据。在专用工作站和物联网设备(如树莓派或智能手机)上的运行时间都在秒的范围内。参考实现是MIT许可下的免费开源软件。
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来源期刊
Informatics
Informatics Social Sciences-Communication
CiteScore
6.60
自引率
6.50%
发文量
88
审稿时长
6 weeks
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