Optimization and Security of Continuous Anonymizing Data Streams
S. Nasira Tabassum
S. Nasira Tabassum, M. Tech (Department of SE), Nizam Institute of Engineering and Technology, Deshmukhi, Nalgonda, (Andhra Pradesh), India.
Manuscript received on September 21, 2012. | Revised Manuscript received on October 02, 2012. | Manuscript published on October 30, 2012. | PP: 30-34 | Volume-2 Issue-1, October 2012.  | Retrieval Number: A0736092112 /2012©BEIESP

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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: 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. Continuously Anonymizing Streaming data via adaptive Clustering (CASTLE), an efficient and effective algorithm w.r.t. the quality of the data, is a cluster-based scheme that anonymizes data streams on-the-fly and, at the same time, ensures the freshness of the data. CASTLE is also extended to handle l-diversity. Finally, we study the optimization and security techniques of data streams using selective security encryption and compression to improve the efficiency of the CASTLE algorithm. 
Keywords: Privacy-preserving data mining, continuous anonymity, selective security encryption, data compression.