A multidimensional sensitivity-based anonymization method of big data

Mohammed Al-Zobbi, Seyed Shahrestani, Chun Ruan

Research output: Chapter in Book / Conference PaperChapter

Abstract

Big data analytics is where advanced analytic techniques operate on big data sets (Russom, 2011). Hence, analytics is the main concern in big data, and it may be exploited by data miners to breach privacy (Samarati, 2001). In the past few years, several methods that address the data leakage concerns have been proposed for conventional data (Ninghui Li et al., 2007; Qing Zhang et al., 2007). The proposed methods provide remedies for variant types of attacks against data analytics process. Side attack is considered to be one of the most critical attacks (Dwork et al., 2010). This attack is prevalent in medical data, where the attacker owns partial information about the patient. The attacker aims to find the hidden sensitive information by logically linking between his/her data and the targeted data. A side attack can be conducted by either manipulating the query, a state attack, or running malicious code that can transfer the output from other users through the network, a privacy attack. However, a variety of attacks can be triggered by the adversary to interrupt the analytics process by mounting the malicious code, which may cause infinite loop operations or may eavesdrop on other user’s operations (Shin et al., 2012).
Original languageEnglish
Title of host publicationNetworks of the Future: Architectures, Technologies, and Implementations
EditorsMahmoud Elkhodr, Qusay F. Hassan, Seyed A. Shahrestani
Place of PublicationU.S.
PublisherCRC Press
Pages415-430
Number of pages16
ISBN (Print)9781498783972
Publication statusPublished - 2018

Keywords

  • big data
  • data protection

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