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Triple DES: Privacy Preserving in Big Data Healthcare

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Abstract

Big data stand as a technique to retrieve, collect, manage and also analyze a vast quantity of structured and also unstructured data which are tough to process utilizing the traditional database that involves new technologies to examine them. With the expanding success of the big data usage, loads of challenges emerged. Timeless, scalability and privacy are the chief problems that researchers endeavor to work out. Privacy-preserving is at present a highly active domain of research. To guarantee a safe and trustworthy big data atmosphere, it is imperative to pinpoint the drawbacks of the existing solutions furthermore conceive directions for future study. In the given paper, the security and also the privacy-preserving on big data is proposed concerning the healthcare industry and to beat security issues in existing approach. Mainly anonymizations along with Triple DES techniques aimed at security purpose are incorporated. Triple DES offers a fairly simple technique of increasing the key size of DES to shield against such attacks, devoid of necessitates to design an entirely new block cipher algorithm. Data anonymization work as an information sanitizer whose target is to defend the data privacy. It encrypts or takes away the personally recognizable data as of the data sets in order that the persons about whom the data designate remain anonymous. In this work, a combination of anonymization and Triple DES are utilized that are shortly called as the A3DES algorithm. Experimental outcome reveals that the approach performed well when contrasted with all other related approaches.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Velammal Engineering College, Chennai, India

    R. Ramya Devi & V. Vijaya Chamundeeswari

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  1. R. Ramya Devi
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  2. V. Vijaya Chamundeeswari
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Correspondence to R. Ramya Devi.

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Ramya Devi, R., Vijaya Chamundeeswari, V. Triple DES: Privacy Preserving in Big Data Healthcare. Int J Parallel Prog 48, 515–533 (2020). https://doi.org/10.1007/s10766-018-0592-8

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  • DOI: https://doi.org/10.1007/s10766-018-0592-8

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