一种高效用数据起源过滤机制

A Data Provenance Sanitization Mechanism for High Utility

为解决现有起源过滤机制导致溯源效用低下的问题,提出一种数据起源过滤机制。扩展PROV数据模型,将其中的依赖关系泛化为不确定的依赖关系,并证明使用不确定的依赖关系进行溯源效用恢复的合理性。构建效用评估模型,定量地评估包含不确定依赖关系的过滤视图的效用。提出"删除+修复"的起源过滤新机制,删除敏感节点或边,并在保证溯源结果不增的前提下,引入不确定的依赖关系,恢复过滤视图的溯源效用。实验结果表明,与现有的典型起源过滤机制相比,采用该机制可得到具有更高效用的起源过滤视图。

To improve the low provenance utility provided by existed provenance sanitization mechanisms, a data provenance sanitization mechanism is proposed. PROV-DM model is extended to generalize the dependencies into uncertain dependencies. The rationality of recovering provenance utility by introducing uncertain dependencies is proved. An evaluation model for utility is built to quantitatively evaluate sanitized views with uncertain dependencies. A novel provenance sanitization mechanism of ＂delete and recover＂is proposed to delete sensitive nodes or edges and then recover provenance utility by introducing uncertain dependencies in sanitized views under the premise that the result of provenance tracing is not increased. Experimental results show that the proposed mechanism can produce sanitized views with higher provenance utility,in comparison with existed typical sanitization mechanisms.