Decision forests are classical models to efficiently make decision on complex inputs with multiple features. While the global structure of the trees or forests is public, sensitive information have to be protected during the evaluation of some client inputs with respect to some server model. Indeed, the comparison thresholds on the server side may have economical value while the client inputs might be critical personal data. In addition, soundness is also important for the receiver. In our case, we will consider the server to be interested in the outcome of the model evaluation so that the client should not be able to bias it. In this paper, we propose a new offline/online protocol between a client and a server with a constant number of rounds in the online phase, with both privacy and soundness against malicious clients. CCS Concepts: $\bullet$ Security and Privacy $\rightarrow$ Cryptography.
翻译:决策型森林是传统模式,可以有效地就具有多种特点的复杂投入作出决定。虽然树木或森林的全球结构是公开的,但在评估某些服务器模型的客户投入时,敏感信息必须受到保护。事实上,服务器方面的比较阈值可能具有经济价值,而客户投入可能是重要的个人数据。此外,稳健性对于接收者也很重要。就我们而言,我们将认为服务器对模型评估的结果感兴趣,使客户不能对其有偏向。在本文中,我们提议在客户与服务器之间订立新的离线/在线协议,在网上阶段,对恶意客户的隐私和稳健性进行连续循环。CCF概念:$\bullt$安全和隐私 $\rightrowcrotography。