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A. Rahmani, A. Amine, M. R. Hamou.  2015.  "De-identification of Textual Data Using Immune System for Privacy Preserving in Big Data". 2015 IEEE International Conference on Computational Intelligence Communication Technology. :112-116.

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.