عنوان انگلیسی مقاله:
Efficient privacy preservation of big data for accurate data mining
ترجمه فارسی عنوان مقاله:
حفظ حریم خصوصی کارا داده های بزرگ برای داده کاوی دقیق
Sciencedirect - Elsevier - Information Sciences, Corrected proof: doi:10:1016/j:ins:2019:05:053
M.A.P. Chamikara a , b , ∗, P. Bertok a , D. Liu b , S. Camtepe b , I. Khalil a
Computing technologies pervade physical spaces and human lives, and produce a vast amount of data that is available for analysis. However, there is a growing concern that po- tentially sensitive data may become public if the collected data are not appropriately sani- tized before being released for investigation. Although there are more than a few privacy- preserving methods available, they are not efficient, scalable, or have problems with data utility, or privacy. This paper addresses these issues by proposing an efficient and scalable nonreversible perturbation algorithm, PABIDOT, for privacy preservation of big data via op- timal geometric transformations. PABIDOT was tested for efficiency, scalability, attack re- sistance, and accuracy using nine datasets and five classification algorithms. Experiments show that PABIDOT excels in execution speed, scalability, attack resistance, and accuracy in large-scale privacy-preserving data classification when compared with two other, related privacy-preserving algorithms.
Keywords: Information privacy | Privacy-preserving data mining | Big data privacy | Data perturbation | Big data