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. However, recent research has mostly focused on the small-party honest-majority setting of up to four parties, noting efficiency concerns. In this work, we extend the strategies to support a larger number of participants in an honest-majority setting with efficiency at the center stage. Cast in the preprocessing paradigm, our semi-honest protocol improves the online complexity of the decade-old state-of-the-art protocol of Damg\aa rd and Nielson (CRYPTO'07). In addition to having an improved online communication cost, we can shut 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. 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 improved protocols aid in bringing up to 60-80% savings in monetary cost over prior work.
翻译:为了解决隐私问题和提供实际的解决办法,文献依靠的是安全的多党计算。然而,最近的研究主要侧重于小党诚实多数的四方,注意到效率问题。在这项工作中,我们扩展了战略,以支持更多的参与者进入一个诚实多数且效率高的中央阶段。在预处理范式中,我们的半诚实协议提高了十年来Damghaa Rd和Nielson(CRYPTO'07)最先进的协议的在线复杂性。除了改进在线通信成本外,我们还可以关闭近一半在网上阶段的政党,从而节省到系统运作成本的50%。我们恶意安全的协议也享有类似的好处,并且只要求半数缔约方最终完成,但一次性核查除外。为了展示设计协议的实用性,我们为大众应用设定了基准,例如深层神经网络、神经网络、基因组序列匹配,以及利用我们之前的原型协议将60 %的援助升级到之前的货币储蓄。