Cloud computing has promoted the success of Internet of Things (IoT) with offering abundant storage and computation resources where the data from IoT sensors can be remotely outsourced to the cloud servers, whereas storing, exchanging and processing data collected through IoT sensors via centralised or decentralised cloud servers make cloud-based IoT systems prone to internal or external attacks. To protect IoT data against potential malicious users and adversaries, some cryptographic schemes have been applied to ensure confidentiality and integrity of IoT data. It is however a challenging task to perform any arithmetical computations once data items are encrypted. Fully-homomorphic encryption which is based on lattices can, in principle, provide a solution, but it is unfortunately inefficient in computation and hence cannot be applied to IoT. Fully-homomorphic encryption is feasible when we allow an involvement of semi-trusted server. However, it is challenging to provide such a system in the situation of distributed environments for shared IoT data. We solve this problem and provide a fully-homomorphic encryption scheme for cloud-based IoT applications. We introduce a new method with the aid of semi-trusted server who can help in the computation of the homomorphic multiplications without gaining any useful information of the encrypted data.

The proliferation of Internet of Things (IoT) as a promising paradigm has contributed enormously to modern technology design. The wireless body sensor network (WBSN) technology is an application of IoT in healthcare, whereas data security and privacy impediments have raised some concerns. The collected data via IoT wireless body sensors is vulnerable to a variety of internal and external attacks. One solution is to encrypt or sign the collected data to provide confidentiality and integrity, but the computational complexity hinders the application in the real IoT-based healthcare devices. Although there have been some attempts to provide secure and efficient IoT schemes, there is a lack of achieving secure data analysis in modern healthcare. The aggregated data statistics about the patient’s medical status is useful to doctors and healthcare providers. However, the dynamic data continually updating over time is challenging. In this paper, we present an efficient and provably secure scheme, which is the first step towards secure data analysis for handling the data collection and analysis for IoT wireless body sensors. The main contribution of our work is a novel cryptographic accumulator based on our novel authenticated additive homomorphic encryption which can collect and accumulate data from IoT wireless wearable devices. These encrypted data can be used for analysis in an encrypted form so that the information is not revealed. To validate security and efficiency, we present security analysis and performance evaluations of our proposed scheme for IoT wireless body sensors.

Traditional searchable symmetric encryption (SSE) schemes rarely support context-aware semantic extension, and then lead to the searched results being incomplete or deviating from the user's query intention. To conquer this problem, a new context-aware semantically extensible searchable symmetric encryption based on Word2vec model (CASE-SSE) is proposed to achieve context-aware semantic extension in this paper. The proposed scheme utilizes outsourced datasets as corpora to extract all keywords for training the Word2vec model, and the trained results is the ontology knowledge base that can be used to extend the semantics of query keywords directly. Further, to facilitate multi-keyword search using the extended query vector, we use the k-means clustering algorithm to classify outsourced datasets. We then construct an AVL-tree index and an inverted index based on the classified results, thereby achieving efficient context-aware semantically extensible SSE. The security analysis demonstrates the proposed scheme is secure and effective, and the experimental results show that the scheme is superior in both efficiency and accuracy.

As a critical parameter in evaluating the reliability of a multiprocessor system when processors malfunction, the \boldmath $h$ -extra connectivity ( $h$ -EC) of a multiprocessor system modeled by a graph $G$ , denoted by $\kappa ^{(h)}_{o}(G)$ , is an $h$ -extra vertex-cut with minimum cardinality. Both of the $h$ -extra conditional diagnosability ( $h$ -ECD) and the $t/h$ -diagnosability of the multiprocessor system are vital to tolerate and diagnose faulty processors. These two parameters rely on the resolving of $h$ -EC. For the multiprocessor system based on star graph $S_{n}$ , we show that the 5-EC $\kappa ^{(5)}_{o}(S_{n})$ of $S_{n}$ ( $n\geq 5$ ) is $6n-18$ . As a by-product, we present a novel proof of $\kappa ^{(2)}_{o}(S_{n})=3n-7$ (resp., $\kappa ^{(4)}_{o}(S_{n})=5n-14$ ) by relaxing the restriction $n\geq 10$ (resp., $n\geq 7$ ) to $n\geq 5$ (resp., $n\geq 5$ ). Furthermore, we determine that the $h$ -ECD of $S_{n}$ $(n\geq 5)$ under the preparata, metze, and chien (PMC) model is $(h+1)n-2h-1$ for $1\leq h\leq 3$ and $(h+1)n-3h+2$ for $4\leq h\leq 5$ . In addition, we show that $S_{n}$ is $[(h+1)n-4h+2]/h$ -diagnosable for $4\leq h\leq 5$ , which extends the result that $S_{n}$ is $[(h+1)n-3h-1]/h$ -diagnosable for $1\leq h\leq 3$ by [Zhou et al. “The t/k-diagnosability of star graph networks,” IEEE Trans. Comput., vol. 64, no. 2, pp. 547–555, Feb. 2015].

The concept of a metric dimension was proposed to model robot navigation where the places of navigating agents can change among nodes. The metric dimension $md\left(G\right)$ of a graph G is the smallest number k for which G contains a vertex set W, such that $\left|W\right|=k$ and every pair of vertices of G possess different distances to at least one vertex in W. In this paper, we demonstrate that $md\left(HDN1\right(n\left)\right)=4$ for $n\ge 2$ . This indicates that in these types of hex derived sensor networks, the least number of nodes needed for locating any other node is four.