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Secure Evaluation of Private Functions through Piecewise Linear Approximation

机译:通过分段线性近似来确保私有功能的评估

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While Secure Multy-Party Computation is a well known solution for cooperative function evaluation on private inputs, few solutions exist that also permit to protect the to-be-evaluated function. In this paper, we propose a solution, based on Garbled Circuit (GC) theory, to provide Secure Function Evaluation of semi-Private Functions through Piecewise Linear Approximation (PLA). We show how to approximate a generic function through a PLA chosen in a set of functions that can be implemented with the same Boolean circuit. The function is protected by hiding the coefficients of the chosen PLA. The class of approximating functions is defined in such a way to allow an efficient implementation by means of GC's. Together with the security provided by Garbled Circuits theory, the security of the protocol is ensured by the very large number of approximating functions belonging to the PLA's set. The paper ends with an investigation of the trade-off between approximation accuracy and protocol settings.
机译:虽然安全的多方计算是私人输入的合作功能评估的众所周知的解决方案,但存在很少的解决方案,也允许保护要评估的功能。在本文中,我们提出了一种基于乱码电路(GC)理论的解决方案,以通过分段线性近似(PLA)提供半私有功能的安全功能评估。我们展示了如何通过在可以用相同布尔电路实现的一组功能中选择的PLA近似通用函数。通过隐藏所选PLA的系数来保护该功能。近似函数的类别以这样的方式定义,以允许通过GC的方式进行有效的实现。与乱码电路理论提供的安全性一起,通过属于PLA集合的大量近似函数来确保协议的安全性。纸张以近似精度和协议设置之间的权衡进行了研究。

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