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首页> 外文期刊>Knowledge and Data Engineering, IEEE Transactions on >Precision-Bounded Access Control Using Sliding-Window Query Views for Privacy-Preserving Data Streams
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Precision-Bounded Access Control Using Sliding-Window Query Views for Privacy-Preserving Data Streams

机译：使用滑动窗口查询视图的精确范围访问控制，用于保护隐私的数据流

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摘要

Access control mechanisms and Privacy Protection Mechanisms (PPM) have been proposed for data streams. The access control for a data stream allows roles access to tuples satisfying an authorized predicate sliding-window query. Sharing the sensitive stream data without PPM can compromise the privacy. The PPM meets privacy requirements, e.g., -anonymity or -diversity by generalization of stream data. Imprecision introduced by generalization can be reduced by delaying the publishing of stream data. However, the delay in sharing the stream tuples to achieve better accuracy can lead to false-negatives if the tuples are held by PPM while the query predicate is evaluated. Administrator of an access control policy defines the imprecision bound for each query. The challenge for PPM is to optimize the delay in publishing of stream data so that the imprecision bound for the maximum number of queries is satisfied. We formulate the precision-bounded access control for privacy-preserving data streams problem, present the hardness results, provide an anonymization algorithm, and conduct experimental evaluation of the proposed algorithm. Experiments demonstrate that the proposed heuristic provides better precision for a given data stream access control policy as compared to the minimum or maximum delay heuristics proposed in existing literature.
机译：已经提出了针对数据流的访问控制机制和隐私保护机制（PPM）。数据流的访问控制允许角色访问满足授权谓词滑动窗口查询的元组。在没有PPM的情况下共享敏感的流数据可能会损害隐私。通过流数据的通用化，PPM满足隐私要求，例如-匿名或-多样性。可以通过延迟流数据的发布来减少泛化引入的不精确性。但是，如果在评估查询谓词时，元组由PPM保留，则共享流元组以获得更好的准确性时的延迟会导致假阴性。访问控制策略的管理员定义每个查询的不精确范围。 PPM的挑战是优化流数据发布的延迟，以便满足最大查询数量的不精确约束。我们针对隐私保护数据流问题制定了精确边界访问控制，给出了硬度结果，提供了匿名算法，并对所提出的算法进行了实验评估。实验表明，与现有文献中提出的最小或最大延迟启发式算法相比，该启发式算法可为给定的数据流访问控制策略提供更好的精度。

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