In this paper, we consider how the security of a disclosure control mechanism based on randomised, but uncontrolled, rounding can be assessed by Bayesian methods. We develop a methodology, based on Markov chain Monte Carlo, for estimating the conditional (posterior) probability distribution for the original cell counts given the released rounded values. An effective rounding-based disclosure control will result in high posterior uncertainty about the true value. Conversely, a posterior distribution concentrated on a single value provides evidence of ineffective disclosure control.
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机译：在本文中，我们考虑了基于随机但不受控制的泄露但不受控制的舍入的公开控制机制的安全性如何评估贝叶斯方法。我们基于Markov Chain Monte Carlo开发一种方法，用于估计原始细胞计数的条件（后部）概率分布给出释放的圆形值。有效的基于圆形的披露控制将导致对真实值的高度不确定性。相反，在单个值上集中的后部分布提供了无效的公开控制的证据。
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