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A Privacy-Preserving Feature Extraction Method for Big Data Analytics Based on Data-Independent Reusable Projection
| Content Provider | Semantic Scholar |
|---|---|
| Author | Ravindran, Siddharth Aghila, Gnanasekaran |
| Copyright Year | 2019 |
| Abstract | Big data analytics is one of the key research areas ever since the advancement of internet technologies, social media, mobile networks, and internet of things (IoT). The volume of big data creates a major challenge to the data scientist while interpreting the information from raw data. The privacy of user data is an important issue faced by the users who utilize the computing resources from third party (i.e., cloud environment). This chapter proposed a data independent reusable projection (DIRP) technique for reducing the dimension of the original high dimensional data and also preserves the privacy of the data in analysis phase. The proposed method projects the high dimensional input data into the random low dimensional space. The data independent and distance preserving property helps the proposed method to reduce the computational complexity of the machine learning algorithm. The randomness of data masks the original input data which helps to solve the privacy issue during data analysis. The proposed algorithm has been tested with the MNIST hand written digit recognition dataset. A Privacy-Preserving Feature Extraction Method for Big Data Analytics Based on Data-Independent Reusable Projection |
| Starting Page | 151 |
| Ending Page | 169 |
| Page Count | 19 |
| File Format | PDF HTM / HTML |
| DOI | 10.4018/978-1-5225-8407-0.ch008 |
| Alternate Webpage(s) | https://www.igi-global.com/viewtitlesample.aspx?id=225415&ptid=217685&t=a+privacy-preserving+feature+extraction+method+for+big+data+analytics+based+on+data-independent+reusable+projection |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
