计算机科学
对手
协议(科学)
计算机安全
功能(生物学)
计算
安全多方计算
私人信息检索
可信第三方
第三方
互联网隐私
密码学
算法
医学
替代医学
病理
进化生物学
生物
作者
Andreas Klinger,Felix Battermann,Ulrike Meyer
标识
DOI:10.1145/3577923.3583651
摘要
In secure multi-party computation (SMPC), it is considered that multiple parties that are known to each other evaluate a function over their private inputs in a secure fashion. The participating parties do not learn anything about each other's private inputs beyond what can be deduced from their own input and output. The assumption that the parties know each other, however, does not seem suitable for all potential applications of SMPC. In some applications participants may not only want to hide their private inputs and outputs, but may also want to hide the fact that they are participating in a given function evaluation in the first place. We therefore propose an anonymous system for SMPC that allows parties to anonymously evaluate a function of their private inputs in a fully distributed and secure fashion. The proposed system allows authorized parties to execute an SMPC protocol robust with penalty against a dishonest majority in the presence of a malicious adversary. During the protocol execution, the system guarantees that all participating parties stay anonymous w. r. t. each other as well as any third parties. In addition, it guarantees that in each function evaluation all participating parties are unique, i. e., no party can participate as more than one entity.
科研通智能强力驱动
Strongly Powered by AbleSci AI