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International Journal of Sensor Networks and Data Communications

International Journal of Sensor Networks and Data Communications

ISSN: 2090-4886

Open Access
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Data Mining Article

Today, the design of a differentially private data mining algorithm shows more interest, since the extraction of elements is the most confronted with the problem of data mining. During this study, the possibility of designing a private frequent object data mining algorithm makes it possible to obtain a high degree of confidentiality, usefulness of the data and high time efficiency. To achieve confidentiality, usefulness and efficiency, a frequent object game exploration algorithm is proposed, based on the frequent model growth algorithm. The private frequent model growth algorithm is divided into two phases, namely the preprocessing phase and the exploitation phase. The preprocessing phase consists of improving the utility, the confidentiality and the new intelligent splitting method to transform the database; the pretreatment phase is carried out only once. The exploration phase consists of compensating for the information lost during the splitting of transactions and calculating a method for estimating the execution time to find the real support of the set of elements in a given database. Another dynamic reduction method is used dynamically to reduce the added noise in order to guarantee confidentiality during the process of exploring a set of elements.

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Citations: 343

International Journal of Sensor Networks and Data Communications received 343 citations as per Google Scholar report

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