Compliance Analysis Method of Hive Data Operation Based on Subgraph Isomorphism
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摘要: Hive现有的审计功能不能对数据操作目的进行合规判断。针对以上问题，该文提出一种基于子图同构的Hive数据操作合规分析方法。首先，提出基于图的Hive数据操作和合规规则的建模方法，形成数据溯源图和合规规则图；然后，将数据操作合规判断建模为溯源图和合规图的匹配问题，并提出基于子图同构的求解算法。最后，在数据治理平台Apache Atlas及Hive中进行了实验验证，实验结果表明，相比于基于集合、VF2以及Ullmann的合规验证，该文方法具有更高的合规验证效率。
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关键词:
- Hive数据 /
- Apache Atlas /
- 合规分析 /
- 子图同构
Abstract: Hive's existing audit function can not make compliance judgment on the purpose of data operation. To solve the above problems, a Hive data operation compliance analysis method based on subgraph isomorphism is proposed. Firstly, the modeling method of Hive data operation and compliance rules based on graph is proposed to form data traceability graph and compliance rule graph; Then, the compliance judgment of data operation is modeled as the matching problem of traceability graph and compliance graph, and a solution algorithm based on subgraph isomorphism is proposed. Finally, the experimental verification is carried out in the data governance platforms Apache Atlas and Hive. The experimental results show that the proposed method has higher compliance verification efficiency than the collection based, VF2 and Ullmann compliance verification.-
Key words:
- Hive database /
- Apache Atlas /
- Compliance analysis /
- Subgraph isomorphism
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表 1 合规要求及其功能说明
合规要求 功能 数据使用范围合规 使用的数据不超过声明的使用数据范围 处理方式合规 对数据的操作符合声明的数据操作 权限合规 使用数据的用户具有授权访问该数据的角色 目的合规 同时满足数据使用范围合规、处理方式合规、权限合规的情况，即目的合规 
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表 2 基于子图同构的合规验证算法
输入：${{G} }_{\text{R} },{{A} }_{1},{G}-{{A} }_{1}$ 输出：审计结果 参数说明：${ {p} }_{ {i} }\in { {V} }_{ { {A} }_{1} },{ {t} }_{ {i} }\in { {V} }_{ { {A} }_{1} },{ {D} }_{ { {U} }_{ {I} } }\subseteq { {V} }_{ { {A} }_{1} },{ {D} }_{ {j} }\subseteq { {V} }_{{R} },$ ${ {V} }_{ { {A} }_{1} }\subseteq \text{DU}\cup \text{OP},{ {V} }_{{R} }\subseteq { {D} }_{ { {U} }_{\text{r} } }\cup {\text{OP} }_{ {R} },$ $\text{PURPOSEMAP}\subseteq ({{A} }_{1},{\text{PP} }_{1})\cup ({{A} }_{2},{\text{PP} }_{2})$ $\cup ({ {A} }_{3},{\text{PP} }_{\text{3} })\cdots \cup \left({ {A} }_{{i} },{\text{PP} }_{ {I} }\right)$ (1) M(s)= $ \text{VF3} $(${ {G} }_{{R} },{ {A} }_{1}$) (2) if M(s) 包含${{A} }_{1}$中所有节点 then (3) 从 $ \mathrm{P}\mathrm{U}\mathrm{R}\mathrm{P}\mathrm{O}\mathrm{S}\mathrm{E}\mathrm{M}\mathrm{A}\mathrm{P} $获得关于${{A} }_{1}$操作的目的A (4) if OP $\text{∉}{{V} }_{\mathrm{G}-{{A} }_{1} }$ then (5) return ${ \text{purpose}}A$ (6) else (7) 基于OP $\in{ {V} }_{ {G}-{ {A} }_{1} }$ 获得 ${ {G} }_{ {{R} }_{1} }$ 和${{A} }_{2}$, ${{V} }_{ {{A} }_{2} }\subseteq {\mathrm{D}\mathrm{U} }_{2}\cup {\mathrm{O}\mathrm{P} }_{2}$,
${ {V} }_{ {{R} }_{1} }\subseteq { {D} }_{ { {U} }_{\mathrm{r}1} }\cup {\mathrm{O}\mathrm{P} }_{ {{R} }_{1} }$,(8) if ${{G} }_{ {{R} }_{1} }$=$\varnothing $ then (9) return ${\text{purpose}} A$ $ \mathrm{\Delta } $ 合规 (10) else (11) ${{M} }_{1}\left({S}\right)=\mathrm{V}\mathrm{F}3$(${{G} }_{ {{R} }_{1} },{{A} }_{2}$) (12) if ${{M} }_{1}\left({S}\right)$包含${{A} }_{2}$中的所有节点 then (13) 从$ \mathrm{P}\mathrm{U}\mathrm{M}\mathrm{A}\mathrm{P} $获得关于${{A} }_{2}$的purposeB (14) if ${\text{purpose}}B$ = ${\text{purpose}}A$ then (15) return ${\text{purpose}}A$ $ \mathrm{\Delta } $ 合规 (16) else (17) return “-2” $ \mathrm{\Delta }\mathrm{数}\mathrm{据}\mathrm{最}\mathrm{小}\mathrm{化}\mathrm{不}\mathrm{合}\mathrm{规} $ (18) else (19) if ${M}\left({S}\right)$ 包含${{A} }_{1}$中的一个节点 then (20) return “–3” $ \text{Δ} $操作和用户身份验证不合规 (21) else (22) return “–4” $ \text{Δ} $ 用户身份验证不满足
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表 3 合规规则信息
编号 规则 (1) root1被授权在StatisticalAnalyse下只能对表Customer和
表Customer_address执行rank, avg, sum,count, substr(2) root2被授权在SensitiveAnalyse下只能对表Customer和
表Customer_demographics执行count, sum, max, min, avg
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