Security-control methods for statistical databases: a comparative study
ACM Computing Surveys (CSUR)
Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Models and issues in data stream systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Cryptographic techniques for privacy-preserving data mining
ACM SIGKDD Explorations Newsletter
Information sharing across private databases
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Mining concept-drifting data streams using ensemble classifiers
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Data Privacy through Optimal k-Anonymization
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
On k-anonymity and the curse of dimensionality
VLDB '05 Proceedings of the 31st international conference on Very large data bases
ACM SIGMOD Record
A Framework for On-Demand Classification of Evolving Data Streams
IEEE Transactions on Knowledge and Data Engineering
StatStream: statistical monitoring of thousands of data streams in real time
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
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In recent years, there is a rapid growth with amount of data collections in hardware, software and networking, data source sharing, transaction oriented data etc., This type of stream data also contains private and sensitive information. A new topic in the area of privacy preserving data mining in stream data is quite challenging, in which data grows rapidly at an unlimited rate and patterns will also be changed in timely manner like concept drifting. Many topics have been introduced to extract patterns in streaming data and provide the privacy preserving of data streams. In this paper we proposed a new approach of reliable and efficient way for extracting hidden patterns in stream data by using Heine-Boral property and α --cut property. For each time limit T, in these two proposed methods convert high dimensional stream data objects into lower dimensions using principal component analysis. Then apply these proposed methods to stream data objects for finding patterns to classify into subsets of classes, what an user is interested in intended patterns by imposing threshold values. It also consists of a novel method to provide privacy of stream data using fuzzy logic and discussed different types of advantages by utilizing fuzzy logic in data mining.