Visible to the public "De-identification of Textual Data Using Immune System for Privacy Preserving in Big Data"Conflict Detection Enabled

Title"De-identification of Textual Data Using Immune System for Privacy Preserving in Big Data"
Publication TypeConference Paper
Year of Publication2015
AuthorsA. Rahmani, A. Amine, M. R. Hamou
Conference Name2015 IEEE International Conference on Computational Intelligence Communication Technology
Date PublishedFeb
ISBN Number978-1-4799-6023-1
Accession Number15034947
KeywordsBig 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

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.

Citation Key7078678