Masoud Rahimi

Work place: Sheikhbahaee University, Isfahan, Iran



Research Interests: Computational Science and Engineering, Computational Engineering, Computer systems and computational processes, Computer Architecture and Organization


Masoud Rahimi, received the B.Sc. degree in computer engineering in 2009 from University of PNU, Farsan, Iran, and the M.Sc. degree in computer engineering from University of Sheikhbahaee (SHBU), Isfahan, Iran, in 2015.

Author Articles
Extended K-Anonymity Model for Privacy Preserving on Micro Data

By Masoud Rahimi Mehdi Bateni Hosein Mohammadinejad

DOI:, Pub. Date: 8 Nov. 2015

Today, information collectors, particularly statistical organizations, are faced with two conflicting issues. On one hand, according to their natural responsibilities and the increasing demand for the collected data, they are committed to propagate the information more extensively and with higher quality and on the other hand, due to the public concern about the privacy of personal information and the legal responsibility of these organizations in protecting the private information of their users, they should guarantee that while providing all the information to the population, the privacy is reasonably preserved. This issue becomes more crucial when the datasets published by data mining methods are at risk of attribute and identity disclosure attacks. In order to overcome this problem, several approaches, called p-sensitive k-anonymity, p+-sensitive k-anonymity, and (p, α)-sensitive k-anonymity, were proposed. The drawbacks of these methods include the inability to protect micro datasets against attribute disclosure and the high value of the distortion ratio. In order to eliminate these drawbacks, this paper proposes an algorithm that fully protects the propagated micro data against identity and attribute disclosure and significantly reduces the distortion ratio during the anonymity process.

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