Deriving Private Information from Perturbed Data using IQR Based Approach
Several randomized techniques have been proposed for privacy preserving data mining of continuous data. These approaches generally attempt to hide the sensitive data by randomly modifying the data values using some additive noise and aim to reconstruct the original distribution closely at an aggrega...
محفوظ في:
المؤلفون الرئيسيون: | GUO, Songtao, WU, Xintao, LI, Yingjiu |
---|---|
التنسيق: | text |
اللغة: | English |
منشور في: |
Institutional Knowledge at Singapore Management University
2006
|
الموضوعات: | |
الوصول للمادة أونلاين: | https://ink.library.smu.edu.sg/sis_research/321 http://dx.doi.org/10.1109/ICDEW.2006.47 |
الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
المؤسسة: | Singapore Management University |
اللغة: | English |
مواد مشابهة
-
On the Lower Bound of Reconstruction Error for Spectral Filtering Based Privacy Preserving Data Mining
بواسطة: GUO, Songtao, وآخرون
منشور في: (2006) -
Determining Error Bounds for Spectral Filtering Based Reconstruction Methods in Privacy Preserving Data Mining
بواسطة: GUO, Songtao, وآخرون
منشور في: (2008) -
Preventing Interval-based Inference by Random Data Perturbation
بواسطة: LI, Yingjiu, وآخرون
منشور في: (2002) -
An Approach to Outsourcing Data Mining Tasks While Protecting Business Intelligence and Customer Privacy
بواسطة: QU, Ling, وآخرون
منشور في: (2006) -
An Efficient Online Auditing Approach to Limit Private Data Disclosure
بواسطة: LU, Haibing, وآخرون
منشور في: (2009)