The tremendous development of cloud computing with related technologies is an unexpected one. However, centralized cloud storage faces few challenges such as latency, storage, and packet drop in the network. Cloud storage gets more attention due to its huge data storage and ensures the security of secret information. Most of the developments in cloud storage have been positive except better cost model and effectiveness, but still data leakage in security are billiondollar questions to consumers. Traditional data security techniques are usually based on cryptographic methods, but these approaches may not be able to withstand an attack from the cloud server's interior. So, we suggest a model called multi-layer storage (MLS) based on security using elliptical curve cryptography (ECC). The suggested model focuses on the significance of cloud storage along with data protection and removing duplicates at the initial level. Based on divide and combine methodologies, the data are divided into three parts. Here, the first two portions of data are stored in the local system and fog nodes to secure the data using the encoding and decoding technique. The other part of the encrypted data is saved in the cloud. The viability of our model has been tested by research in terms of safety measures and test evaluation, and it is truly a powerful complement to existing methods in cloud storage.
The current research paper discusses the implementation of higher order-matched filter design using odd and even phase processes for efficient area and time delay reduction. Matched filters are widely used tools in the recognition of specified task. When higher order taps are implemented upon the transposed form of matched filters, it can enhance the image recognition application and its performance in terms of identification and accuracy. The proposed method i.e., odd and even phases' process of FIR filter can reduce the number of multipliers and adders, used in existing system. The main advantage of using higher order tap-matched filter is that it can reduce the area required, owing to its odd and even processes. Further, it also successfully reduces the time delay, especially in case of high order demands. The performance of higher order matched filter design, using odd and even phase process, was analyzed using Xilinx 9.1 ISE Simulator. The study results accomplished reduction in area, 70% increase in throughput compared to traditional implementation and reduced time delay. In addition to these, Vedic multiplier-based FIR is modified with a tree-based MAM that reduces the number of shifter and adder to replace the multiplier.
Mashup of health care data from different medical sources must be privacy preserved since the data recipient and/or the data provider may not always be a trusted party. Raw medical data contains person specific sensitive information like ailment, surgery etc. and hence it is susceptible to certain privacy attacks such as attribute linkage and record linkage. There are different privacy models to thwart the privacy attacks. This paper illustrates how to vertically integrate the data from mental health clinic and National AIDS Control Organization (NACO) and preserve privacy using the LKC privacy model.
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