Reviewing Privacy Preserving Distributed Data Mining

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Distributed Data Mining</title> <meta name="description" content="Reviewing Privacy Preserving Distributed Data Mining"> <meta name="keywords" content="data mining, distributed data mining, privacy protection, privacy preserving"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" 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Distributed Data Mining</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sajjad%20Baghernezhad">Sajjad Baghernezhad</a>, <a href="https://publications.waset.org/abstracts/search?q=Saeideh%20Baghernezhad"> Saeideh Baghernezhad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Nowadays considering human involved in increasing data development some methods such as data mining to extract science are unavoidable. One of the discussions of data mining is inherent distribution of the data usually the bases creating or receiving such data belong to corporate or non-corporate persons and do not give their information freely to others. Yet there is no guarantee to enable someone to mine special data without entering in the owner鈥檚 privacy. Sending data and then gathering them by each vertical or horizontal software depends on the type of their preserving type and also executed to improve data privacy. In this study it was attempted to compare comprehensively preserving data methods; also general methods such as random data, coding and strong and weak points of each one are examined. <iframe src="https://publications.waset.org/abstracts/28876.pdf" style="width:100%; height:400px;" frameborder="0"></iframe> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title="data mining">data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=distributed%20data%20mining" title=" distributed data mining"> distributed data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=privacy%20protection" title=" privacy protection"> privacy protection</a>, <a href="https://publications.waset.org/abstracts/search?q=privacy%20preserving" title=" privacy preserving"> privacy preserving</a> </p> <a 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Reviewing Privacy Preserving Distributed Data Mining