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Design of a secure sum protocol using trusted third party system for Secure Multi-Party Computations

机译:使用可信第三方系统进行安全多方计算的安全总和协议设计

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In distributed data mining, secrecy of private data input of parties with similar background, is achieved by Secure Multi Party Computation (SMC). One of the mostly used tool of SMC is secure sum protocol which has been modified by researchers using many techniques to provide utmost security. In this paper, we propose another novel secure sum protocol to provide more data security in an efficient way named Double Random Partitioned Model (DRPM) protocol for multi-party computation that uses the collaboration of data segmentation, value randomization technique and trusted third party for ensuring zero data leakage among participating parties. Proposed method have reduced computational steps noticeably than all other existing protocols. The comparative study shows that the proposed protocol performs much better than the existing protocols in terms of communication complexity and computation complexity, e.g., proposed DRPM protocol improves 85% on computational complexity over the existing best one.
机译:在分布式数据挖掘中,通过安全多方计算(SMC)实现了具有相似背景的各方的私有数据输入的保密性。 SMC最常用的工具之一是安全和协议,研究人员已使用许多技术对其进行了修改,以提供最大的安全性。在本文中,我们提出了另一种新颖的安全总和协议,以一种有效的方式提供了更高的数据安全性,称为多方计算的双随机分区模型(DRPM)协议,该协议使用数据分段,值随机化技术和受信任的第三方的协作来进行多方计算。确保与会各方之间零数据泄漏。与所有其他现有协议相比,提出的方法显着减少了计算步骤。对比研究表明,在通信复杂度和计算复杂度方面,提出的协议的性能要比现有协议好得多,例如,提出的DRPM协议在计算复杂度上比现有的最佳协议提高了85%。

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