Privacy Preserving Data Mining With 3-D Rotation Transformation
| المصدر: | مجلة جامعة الملك سعود - علوم الحاسب والمعلومات |
|---|---|
| الناشر: | جامعة الملك سعود |
| المؤلف الرئيسي: | Upadhyay, Somya (Author) |
| مؤلفين آخرين: | Sharma, Chetana (Co-Author) , Sharma, Pravishti (Co-Author) , Bharadwaj, Prachi (Co-Author) , Seeja, K. R. (Co-Author) |
| المجلد/العدد: | مج30, ع4 |
| محكمة: | نعم |
| الدولة: |
السعودية |
| التاريخ الميلادي: |
2018
|
| الصفحات: | 524 - 530 |
| DOI: |
10.33948/0584-030-004-008 |
| ISSN: |
1319-1578 |
| رقم MD: | 974503 |
| نوع المحتوى: | بحوث ومقالات |
| اللغة: | الإنجليزية |
| قواعد المعلومات: | science |
| مواضيع: | |
| كلمات المؤلف المفتاحية: |
Data Perturbation | Variance | Three Dimensional Rotation | Privacy Preserving | Data Mining
|
| رابط المحتوى: |
| المستخلص: |
Data perturbation is one of the popular data mining techniques for privacy preserving. A major issue in data perturbation is that how to balance the two conflicting factors – protection of privacy and data utility. This paper proposes a Geometric Data Perturbation (GDP) method using data partitioning and three-dimensional rotations. In this method, attributes are divided into groups of three and each group of attributes is rotated about different pair of axes. The rotation angle is selected such that the variance-based privacy metric is high which makes the original data reconstruction difficult. As many data mining algorithms like classification and clustering are invariant to geometric perturbation, the data utility is preserved in the proposed method. The experimental evaluation shows that the proposed method provides good privacy preservation results and data utility compared to the state of the art techniques. |
|---|---|
| ISSN: |
1319-1578 |
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