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<h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: January 1–6:</h2> | <h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: January 7–13:</h2> | ||
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Rao JofBigData2018 5.png|240px]]</div> | |||
'''"[[Journal:Privacy preservation techniques in big data analytics: A survey|Privacy preservation techniques in big data analytics: A survey]]"''' | |||
Incredible amounts of data are being generated by various organizations like [[hospital]]s, banks, e-commerce, retail and supply chain, etc. by virtue of digital technology. Not only humans but also machines contribute to data streams in the form of closed circuit television (CCTV) streaming, web site logs, etc. Tons of data is generated every minute by social media and smart phones. The voluminous data generated from the various sources can be processed and analyzed to support decision making. However [[Data analysis|data analytics]] is prone to privacy violations. One of the applications of data analytics is recommendation systems, which are widely used by e-commerce sites like Amazon and Flipkart for suggesting products to customers based on their buying habits, leading to inference attacks. Although data analytics is useful in decision making, it will lead to serious privacy concerns. Hence privacy preserving data analytics became very important. This paper examines various privacy threats, privacy preservation techniques, and models with their limitations. The authors then propose a data lake-based modernistic privacy preservation technique to handle privacy preservation in unstructured data. ('''[[Journal:Privacy preservation techniques in big data analytics: A survey|Full article...]]''')<br /> | |||
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|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: January 1–6:</h2> | |||
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Tab1 Mulder PLOSCompBio2018 14-2.png|240px]]</div> | <div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Tab1 Mulder PLOSCompBio2018 14-2.png|240px]]</div> | ||
'''"[[Journal:The development and application of bioinformatics core competencies to improve bioinformatics training and education|The development and application of bioinformatics core competencies to improve bioinformatics training and education]]"''' | '''"[[Journal:The development and application of bioinformatics core competencies to improve bioinformatics training and education|The development and application of bioinformatics core competencies to improve bioinformatics training and education]]"''' |
Revision as of 14:58, 14 January 2019
If you're looking for other "Article of the Week" archives: 2014 - 2015 - 2016 - 2017 - 2018 - 2019 |
Featured article of the week archive - 2019
Welcome to the LIMSwiki 2019 archive for the Featured Article of the Week.
Featured article of the week: January 7–13:"Privacy preservation techniques in big data analytics: A survey" Incredible amounts of data are being generated by various organizations like hospitals, banks, e-commerce, retail and supply chain, etc. by virtue of digital technology. Not only humans but also machines contribute to data streams in the form of closed circuit television (CCTV) streaming, web site logs, etc. Tons of data is generated every minute by social media and smart phones. The voluminous data generated from the various sources can be processed and analyzed to support decision making. However data analytics is prone to privacy violations. One of the applications of data analytics is recommendation systems, which are widely used by e-commerce sites like Amazon and Flipkart for suggesting products to customers based on their buying habits, leading to inference attacks. Although data analytics is useful in decision making, it will lead to serious privacy concerns. Hence privacy preserving data analytics became very important. This paper examines various privacy threats, privacy preservation techniques, and models with their limitations. The authors then propose a data lake-based modernistic privacy preservation technique to handle privacy preservation in unstructured data. (Full article...)
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