论文标题
BISCHASIC PRISACY
Bistochastic privacy
论文作者
论文摘要
我们介绍了一个依赖于BISCHASIC矩阵的新隐私模型，即，其组件是非负的矩阵，并将其汇总到1个行和列的1个矩阵。这类矩阵既用于定义隐私保证，又是在数据集上应用保护的工具。 BISCHASITIOS假设恰好连接了隐私文献的几个领域，包括两个最受欢迎的模型，K-匿名性和差异性隐私。此外，它建立了一个带有信息理论的桥梁，这简化了评估受保护数据集的效用的棘手问题。 BISCOCHASIC PRISACY还通过使用位来阐明保护和公用事业之间的权衡，可以将其视为一种本币来理解和运营这种权衡，就像信息理论中使用的位置相同，以捕获不确定性。还提供了有关Binechastic矩阵的合适参数化以实现此新模型的隐私保证的讨论。
We introduce a new privacy model relying on bistochastic matrices, that is, matrices whose components are nonnegative and sum to 1 both row-wise and column-wise. This class of matrices is used to both define privacy guarantees and a tool to apply protection on a data set. The bistochasticity assumption happens to connect several fields of the privacy literature, including the two most popular models, k-anonymity and differential privacy. Moreover, it establishes a bridge with information theory, which simplifies the thorny issue of evaluating the utility of a protected data set. Bistochastic privacy also clarifies the trade-off between protection and utility by using bits, which can be viewed as a natural currency to comprehend and operationalize this trade-off, in the same way than bits are used in information theory to capture uncertainty. A discussion on the suitable parameterization of bistochastic matrices to achieve the privacy guarantees of this new model is also provided.
