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Abstract

Sensing devices are being wide accustomed construct and install self classifying WSN networks for a spread of applications. Stream Manager (D-SM) is collects data streams (often referred to as massive data) to perform decision-making and real time analysis for these decisive functions. An inclined oppose will access or tamper with the info in transmission. One in all the difficult tasks in such applications is to confirm the trait of collected information so any call is formed on the process of correct information. High information peculiarity assurance needs that the theme ought to satisfy two key security properties: integrity and confidentiality. To form certain the discretion of collected information, it's needed to forestall perceptive info from reaching the incorrect folks and to form certain that right folks have gotten it. Detected information area unit invariably associated with varied sensitivity levels supported rising applications sensitivity, the detected information sorts and/or the sensing devices. For large information streams, providing construction information confidentiality beside information integrity within the context of close to real time analytics is that the main downside. This thesis proposes an Enhanced Classification Model is (ECM) technique for securing massive sensing information streams that meets multiple levels of confidentiality and integrity. This ECM technique includes two vital concepts: common shared keys that area unit initialized and updated by D-SM while not requiring retransmission and a seam-less key stimulant method while not break off the data-stream encryption/decryption. Moreover, a replacement theme is planned to secure a multi-hop schedule protocol through the employment of multiple unidirectional hash chains. The theme is shown to be lower in machine, power utilization. Also, communication prices area unit nevertheless still ready to secure multi-hop dissemination.

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How to Cite
C.Mani, & S.Arun. (2019). A selective encryption method to ensure confidentiality for big sensing data streams . International Journal of Intellectual Advancements and Research in Engineering Computations, 7(1), 828–834. Retrieved from https://ijiarec.com/ijiarec/article/view/1013