"De-identification of Textual Data Using Immune System for Privacy Preserving in Big Data"
| Title | "De-identification of Textual Data Using Immune System for Privacy Preserving in Big Data" |
| Publication Type | Conference Paper |
| Year of Publication | 2015 |
| Authors | A. Rahmani, A. Amine, M. R. Hamou |
| Conference Name | 2015 IEEE International Conference on Computational Intelligence Communication Technology |
| Date Published | Feb |
| Publisher | IEEE |
| ISBN Number | 978-1-4799-6023-1 |
| Accession Number | 15034947 |
| Keywords | Big Data, CLONALG, cryptography, data mining, Data models, data privacy, de-identification, Immune system, immune systems, Informatics, privacy, privacy preserving, pubcrawl170105, security, specific immune system algorithm, text analysis, textual data de-identification |
| Abstract | With the growing observed success of big data use, many challenges appeared. Timeless, scalability and privacy are the main problems that researchers attempt to figure out. Privacy preserving is now a highly active domain of research, many works and concepts had seen the light within this theme. One of these concepts is the de-identification techniques. De-identification is a specific area that consists of finding and removing sensitive information either by replacing it, encrypting it or adding a noise to it using several techniques such as cryptography and data mining. In this report, we present a new model of de-identification of textual data using a specific Immune System algorithm known as CLONALG. |
| URL | http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7078678&isnumber=7078645 |
| DOI | 10.1109/CICT.2015.146 |
| Citation Key | 7078678 |