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Tighter Security for Efficient Lattice Cryptography via the Renyi Divergence of Optimized Orders

机译:通过优化顺序的仁义散度,为有效的晶格密码学提供更严格的安全性

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In security proofs of lattice based cryptography, to bound the closeness of two probability distributions is an important procedure. To measure the closeness, the Renyi divergence has been used instead of the classical statistical distance. Recent results have shown that the Renyi divergence offers security reductions with better parameters, e.g. smaller deviations for discrete Gaussian distributions. However, since previous analyses used a fixed order Renyi divergence, i.e., order two, they lost tightness of reductions. To overcome the deficiency, we adap-tively optimize the orders based on the advantages of the adversary for several lattice-based schemes. The optimizations enable us to prove the security with both improved efficiency and tighter reductions. Indeed, our analysis offers security reductions with smaller parameters them the statistical distance based analysis and the reductions are tighter than that of previous Renyi divergence based analysis. As applications, we show tighter security reductions for sampling discrete Gaussian distributions with smaller precomputed tables for BLISS signatures, and variants of learning with errors (LWE) problem and small integer solution (SIS) problem called k-LWE and k-SIS.
机译:在基于格的​​密码学的安全性证明中,限制两个概率分布的紧密性是重要的过程。为了测量紧密度,已使用人散度代替了经典的统计距离。最近的结果表明,人一散度以更好的参数提供安全性降低,例如离散高斯分布的偏差较小。但是,由于先前的分析使用固定阶数的仁义散度,即阶数2,所以它们失去了减少的严格性。为了克服这一不足,我们基于对手的优势针对几种基于格的方案自适应地优化了订单。这些优化使我们能够以提高的效率和更严格的缩减来证明安全性。确实,我们的分析使用基于统计距离的分析提供了较小参数的安全性降低,并且与以前的基于Renyi散度的分析相比,这种降低更为严格。作为应用程序,我们展示了使用较小的用于BLISS签名的预计算表对离散高斯分布进行采样的安全性降低程度更严格的方法,以及称为k-LWE和k-SIS的带有错误(LWE)问题和小整数解(SIS)问题的学习变体。

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