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Optimization and Seurity of Continuous Anonymizing Data Stream


S. Nasira Tabassum


Vol. 13  No. 9  pp. 120-124


The characteristic of data stream is that it has a huge size and its data change continually, which needs to be responded quickly, since the times of query is limited. The continuous query and data stream approximate query model are introduced in this paper. Then, the query optimization of data stream and traditional database are compared such as k-anonymity methods, are designed for static data sets. As such, they cannot be applied to streaming data which are continuous, transient, and usually unbounded. Moreover, in streaming applications, there is a need to offer strong guarantees on the maximum allowed delay between incoming data and the corresponding anonymized output. Finally, technology of continuous query optimization over data streams was investigated. Monitoring aggregates on network traf?c streams is a compelling application of data stream management systems. Continuously Anonymizing Streaming data via adaptive cLustEring (CASTLE), a cluster-based scheme that anonymizes data streams on-the-fly and, at the same time, the basis of the optimization is a powerful but decidable theory in which constraints over data streams can be formulated. CASTLE is extended to handle ‘l-diversity’. There is a need to secure the data when transmitting it over the network. This can be done using selective encryption algorithm to compress and encrypt the data streams before transmission.


privacy-preserving data mining, continuous anonymity, selective security encryption