Browsing by Author "Preethi, P."

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    A novel key exchange algorithm for security in internet of things
    (Institute of Advanced Engineering and Science info@iaesjournal.com, 2019) Koduru, K.; Reddy, P.V.G.D.P.; Preethi, P.
    Today Internet of things (IoT) interconnects any object possessing sensing and computing capabilities to the internet. In this era, increasing number of electronic devices and applications in Internet of Things (IoT) requires secured communication with low power consumption capabilities. As security is a major challenge in internet of things, it is important to design a key management solution that considers resource constrained nodes and hence key management in public key cryptography is a crucial issue. In this paper, a novel key exchange algorithm was developed and implemented on a low powered “Raspberry pi machine” to realize the overall impact it creates on the device. The performance of the proposed algorithm had shown a great improvement over the popular Diffie Hellman key exchange algorithm and a two-level security for data exchange between the parties is implemented. © © 2019 Institute of Advanced Engineering and Science. All rights reserved.
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    Privacy preserving data clustering using a heterogeneous data distortion
    (2019) Preethi, P.; Kumar, K.P.; Ullhaq, M.R.; Naveen, A.; Janapana, H.
    Modern age computation leads to huge amount of data. The whole data is analysed using data mining. In return, it made its path to disruption of the privacy of data owners. In order to achieve privacy on data we use Privacy Preserving Data Mining (PPDM). But when the privacy is maintained the data utility is decreased and vice versa. So, in order to maintain a balance in both privacy and data utility, Privacy Preserving Data Clustering (PPDC) using a Heterogeneous data distortion is introduced. In this article both original and perturbed data are analysed using K-means and density based clustering techniques and the results are compared to show the balance between privacy and utility of the data. � Springer Nature Singapore Pte Ltd. 2019.
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    Privacy preserving data clustering using a heterogeneous data distortion
    (Springer Science and Business Media Deutschland GmbH info@springer-sbm.com, 2019) Preethi, P.; Kumar, K.P.; Ullhaq, M.R.; Naveen, A.; Hyma, H.
    Modern age computation leads to huge amount of data. The whole data is analysed using data mining. In return, it made its path to disruption of the privacy of data owners. In order to achieve privacy on data we use Privacy Preserving Data Mining (PPDM). But when the privacy is maintained the data utility is decreased and vice versa. So, in order to maintain a balance in both privacy and data utility, Privacy Preserving Data Clustering (PPDC) using a Heterogeneous data distortion is introduced. In this article both original and perturbed data are analysed using K-means and density based clustering techniques and the results are compared to show the balance between privacy and utility of the data. © Springer Nature Singapore Pte Ltd. 2019.