The demand of cloud computing and 5G networks has increased in the current scenario due to their attractive features and also the security related to the data over the cloud. In the context of cloud security, there is a number of computationally hard methods available. One of the most popular methods used to secure data over the cloud is the identity-based encryption (IBE). It is an access policy that allows only authorized users to access legible data in order to avoid a malicious attack. IBE comprises of four stages, namely, setup, key generation or extract, encryption, and decryption. Key generation is one of the important and time-consuming phases in which a security key is generated. It is a computational and decisional hard problem for generating unbreakable and nonderivable secure keys. This paper proposes an enhanced identity-based encryption approach where a secure key is generated using part of an identity bit string in order to avoid leakage of users’ identity even if an adversary or attacker decodes the key or encrypted data. Experiment results show that the prosed algorithm takes less time in the encryption and decryption as compared to the competitive approach named efficient selective-ID secure identity-based encryption approach. One of the most important features of the proposed approach is that it hides the user’s identity by using the Lagrange coefficient, which consists of a polynomial interpolation function. The security of the system depends on the hardness of computing the bilinear Diffie-Hellman problem.
Node localization is commonly employed in wireless networks. For example, it is used to improve routing and enhance security. Localization algorithms can be classified as range-free or range-based. Range-based algorithms use location metrics such as ToA, TDoA, RSS, and AoA to estimate the distance between two nodes. Proximity sensing between nodes is typically the basis for range-free algorithms. A tradeoff exists since range-based algorithms are more accurate but also more complex. However, in applications such as target tracking, localization accuracy is very important. In this paper, we propose a new range-based algorithm which is based on the density-based outlier detection algorithm (DBOD) from data mining. It requires selection of the K-nearest neighbours (KNN). DBOD assigns density values to each point used in the location estimation. The mean of these densities is calculated and those points having a density larger than the mean are kept as candidate points. Different performance measures are used to compare our approach with the linear least squares (LLS) and weighted linear least squares based on singular value decomposition (WLS-SVD) algorithms. It is shown that the proposed algorithm performs better than these algorithms even when the anchor geometry about an unlocalized node is poor
A large amount of patient information has been gathered in Electronic Health Records (EHRs) concerning their conditions. An EHR, as an unstructured text document, serves to maintain health by identifying, treating, and curing illnesses. In this research, the technical complexities in extracting the clinical text data are removed by using machine learning and natural language processing techniques, in which an unstructured clinical text data with low data quality is recognized by Halve Progression, which uses Medical-Fissure Algorithm which provides better data quality and makes diagnosis easier by using a cross-validation approach. Moreover, to enhance the accuracy in extracting and mapping clinical text data, Clinical Data Progression uses Neg-Seq Algorithm in which the redundancy in clinical text data is removed. Finally, the extracted clinical text data is stored in the cloud with a secret key to enhance security. The proposed technique improves the data quality and provides an efficient data extraction with high accuracy of 99.6%.
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