De-identification of Textual Data Using Immune System for Privacy Preserving in Big Data

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 deidentification 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.

Büyük Veride Kişi Mahremiyetinin Korunması

“…[14] 2015 Özel bir bağıĢıklık sistemi kullanarak metin verilerini kimliksizleĢtiren yeni bir model ortaya konulmuĢtur.…”

“…Ġlki seçilen ilk antikor ile ikincisi ise ikinci ile yer değiĢtirilir. Sonuç olarak orijinal metinde yer değiĢtirme antikorları kimlik saptayıcılarını değiĢtirmek için çözülür[14].Tekrardan kimlik saptama kimliksizleĢtirilmiĢ belgelerin her bir kelimenin kendi haline yeniden sokulmasıyla orijinal yapısına döndürülmesidir. Bunu yapmanın yolu depolama aĢamasında kullanılan matematiksel fonksiyon kadar kolaydır.…”

“…Hafızadaki her bir antikor (antibody) her bir kelimenin konumunu belirleyen bir anahtardır. Antikorların konumları belirlendikten sonra sistem; metin dosyalarına eklenecek olan hakiki kelimeleri oluĢturmak için bu antikorları çözer (decode)[14].Büyük verinin geliĢimini önleyen en büyük engellerden biri kullanıcıların mahremiyetidir. Bu konu bağlamında birçok geliĢmiĢ araĢtırma yapılmıĢtır.…”

A Survey on Security Issues and the Existing Solutions in Big Data

“…This paper going to represent some related work which already done by some author, so, in this current paper some authors are Amine Rahmani1, Abdelmalek Amine2 and Mohamed Reda Hamou3 represent privacy preserving in big data through De-Identification technique [1]. This technique which provide, deleting and masking the data of identifiable information.…”

Supervised Machine Learning for Plants Identification Based on Images of Their Leaves

“…The present paper proposes a comparison of the classification of different representation of plant leaves based on its margin, shape and textures; we used for each representation different classical supervised data mining algorithms. The organization of this paper is given as follows: Section 2 provides a stat of the art in which we gave a summary of machine learning and some recent works on application of machine learning in plants identification; Section 3 gives details about dataset used in our experiment, Section 4 presents used machine learning approaches, discussion of the results got by Rahmani et al (2015) compared to the obtained results of classification of plants using multilayer neural network is shown in Section 5, and finally Section 6 gives the overall conclusion and the scope for future research.…”