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 language | English |
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Title of host publication | Networks of the Future: Architectures, Technologies, and Implementations |
Editors | Mahmoud Elkhodr, Qusay F. Hassan, Seyed A. Shahrestani |
Place of Publication | U.S. |
Publisher | CRC Press |
Pages | 415-430 |
Number of pages | 16 |
ISBN (Print) | 9781498783972 |
Publication status | Published - 2018 |
Keywords
- big data
- data protection