跨异构通信模型的流数据访问控制

Atul Anand Gopalakrishnan, Ashish Christopher Victor, Deepika Karanji, Umashankar Sivakumar, Seema Nambiar, Subramaniam Kalambur
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引用次数: 0

摘要

流式大数据管道经常使用多个相互连接的平台,以不同的通信模型执行分析。诸如访问控制列表(acl)或基于角色的访问控制(RBAC)等现有技术无法在单个元组的粒度上解决访问控制问题。此外,acl和RBAC无法对异构流平台施加统一的访问控制。在本文中,我们提出了一种统一的机制,将访问控制策略插入到数据流的摄取点,并在使用不同通信模型(如发布-订阅和点对点)的多个平台上强制执行。我们通过Apache Kafka和Apache Storm实现了我们的解决方案。我们进一步说明在涉及具有不同访问控制规则的流的连接查询中实施访问控制。
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HACS: Access Control for Streaming Data Across Heterogeneous Communication Models
Streaming Big Data pipelines frequently use multiple platforms connected to each other for performing analytics with different communication models. Existing techniques like Access Control Lists (ACLs) or Role-Based Access Control (RBAC) are unable to address access control at the granularity of an individual tuple. Moreover, ACLs and RBAC fail to impose uniform access control over heterogeneous streaming platforms. In this paper, we present a unified mechanism to insert access control policies into data streams at the point of ingestion and enforce it across multiple platforms that use different communication models like publish-subscribe and point to point. We exemplify our solution through implementation with Apache Kafka and Apache Storm. We further illustrate the enforcement of access control in join queries involving streams with different access control rules.
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