Privacy preservation in data intensive environment

• Autor(a) principal: Chatterjee,Jyotir Moy
• Data de Publicação: 2018
• Outros Autores: Kumar,Raghvendra, Pattnaik,Prasant Kumar, Solanki,Vijender Kumar, Zaman,Noor
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://scielo.pt/scielo.php?script=sci_arttext&pid=S2182-84582018000200008
• topic: Healthcare; healthcare data frameworks; unstructured restoration; fuzzy systems

Healthcare data frameworks have enormously expanded accessibility of medicinal reports and profited human services administration and research work. In many cases, there are developing worries about protection in sharing restorative files. Protection procedures for unstructured restorative content spotlight on recognition and expulsion of patient identifiers from the content, which might be lacking for safeguarding privacy and information utility. For medicinal services, maybe related exploration thinks about the therapeutic records of patients ought to be recovered from various destinations with various regulations on the divulgence of healthcare data. Considering delicate social insurance data, privacy protection is a significant concern, when patients' mediclinical services information is utilized for exploration purposes. In this article we have used feature selection for getting the best feature set to be selected for privacy preservation by using PCA (Principle Component Analysis). After that we have used two methods K-anonymity and fuzzy system for providing the privacy on medical databases in data intensive enviroments. The results affirm that the proposed method has better performance than those of the related works with respect to factors such as highly sensitive data preservation with k-anonymity.