The growing volumes of data being collected and its analysis to provide better services are creating worries about digital privacy. To address privacy concerns and give practical solutions, the literature has relied on secure multiparty computation techniques. However, recent research over rings has mostly focused on the small-party honest-majority setting of up to four parties tolerating single corruption, noting efficiency concerns. In this work, we extend the strategies to support higher resiliency in an honest-majority setting with efficiency of the online phase at the centre stage. Our semi-honest protocol improves the online communication of the protocol of Damgård and Nielsen (CRYPTO’07) without inflating the overall communication. It also allows shutting down almost half of the parties in the online phase, thereby saving up to 50% in the system’s operational costs. Our maliciously secure protocol also enjoys similar benefits and requires only half of the parties, except for one-time verification towards the end, and provides security with fairness. To showcase the practicality of the designed protocols, we benchmark popular applications such as deep neural networks, graph neural networks, genome sequence matching, and biometric matching using prototype implementations. Our protocols, in addition to improved communication, aid in bringing up to 60–80% savings in monetary cost over prior work.

收集的数据量不断增加以及为提供更好的服务而进行的分析正在引发人们对数字隐私的担忧。为了解决隐私问题并提供实用的解决方案，文献依赖于安全的多方计算技术。然而，最近关于戒指的研究主要集中在最多四个政党的小党诚实多数设置上，以容忍单一腐败，并注意到效率问题。在这项工作中，我们扩展了策略，以在以在线阶段的效率为中心的诚实多数设置中支持更高的弹性。我们的半诚实协议改进了 Damgård 和 Nielsen (CRYPTO'07) 协议的在线通信，而不会增加整体通信。它还允许在在线阶段关闭几乎一半的各方，从而节省高达 50% 的系统运营成本。我们的恶意安全协议也享有类似的好处，只需要一半的参与方，除了最后的一次性验证，并提供公平的安全性。为了展示所设计协议的实用性，我们使用原型实现对深度神经网络、图形神经网络、基因组序列匹配和生物识别匹配等流行应用进行了基准测试。我们的协议除了改进沟通外，还有助于比之前的工作节省高达 60-80% 的货币成本。为了展示所设计协议的实用性，我们使用原型实现对深度神经网络、图形神经网络、基因组序列匹配和生物识别匹配等流行应用进行了基准测试。我们的协议除了改进沟通外，还有助于比之前的工作节省高达 60-80% 的货币成本。为了展示所设计协议的实用性，我们使用原型实现对深度神经网络、图形神经网络、基因组序列匹配和生物识别匹配等流行应用进行了基准测试。我们的协议除了改进沟通外，还有助于比之前的工作节省高达 60-80% 的货币成本。