With the rapid development of 5G/6G, most Internet of Things (IoT) devices will embrace wireless connection in the near future. A public concern is how to securely organize, store and retrieve data generated from IoT devices. Many cloud-based IoT data storage schemes have been proposed recently. However, for an untrusted or vulnerable cloud server, the stored IoT data can be easily accessed, modified and even destroyed given that the IoT data are stored in total centralization. Moreover, the servers in a cloud are generally homogeneous and thus vulnerable to attacks. For improvements, we design a novel framework for secure and efficient IoT data storage based on secret sharing and a collaborative blockchain. First, an ultra-lightweight secret sharing algorithm is designed to map original messages generated by IoT devices to a set of shorter message shares. Second, all the shares of IoT messages are separately delivered to different clouds for storage. To guarantee the security of shares, the delivery is notarized on a proposed blockchain. Specifically, both hash values of the shares and their information of location are embedded in blocks which are then chained to form a blockchain. Third, we create a balanced index structure about the shares for each cloud storage node based on the information in the blockchain, and we also propose a depth-first data search algorithm to improve IoT data retrieval efficiency. Theoretical analysis and simulation results illustrate that our scheme can store and retrieve the IoT data securely and efficiently.

With the continuous development of sensor technology, more and more users hope to monitor and collect information in a certain area safely and efficiently by deploying heterogeneous wireless sensor networks (HWSNs). However, nodes in HWSNs have limited capabilities, which leads to many security challenges. Existing data transmission schemes in HWSNs take measures to resist these security threats, which aggravate the node computation overhead and increase the network energy consumption. This paper proposes a Lightweight and Secure Data Transmission (LSDT) scheme against malicious nodes in heterogeneous wireless sensor networks. Firstly, considering node capabilities limitations in HWSNs, we design a lightweight secret sharing scheme based on XOR operation, which maps data to multiple shares and makes it convenient to transmit shares separately to the sink node via multiple paths. While guaranteeing data security, this scheme can greatly reduce the computation overhead of nodes compared with traditional secret sharing schemes. Further, during the delivery of shares, the network may be attacked by malicious nodes, causing the interruption of message transmission. Therefore, we design a malicious node detection and feedback mechanism, which can quickly respond to malicious node attacks and update the reputation degree of malicious nodes. Finally, we propose a routing selection scheme based on reference path which comprehensively considers the energy and reputation degree of heterogeneous nodes. It makes message transmission bypass malicious nodes while achieving network energy load balance, significantly extending the network lifetime. The security analysis proves that our scheme guarantees the security of data transmission. Theoretical analysis and experiments show that our scheme has significant advantages over the existing HWSNs data transmission schemes in terms of network lifetime extension and malicious node resistance.

The Internet of Medical Things (IoMT) is an important application of the Internet of Things in health care. In IoMT, efficiency and user privacy are crucial for cloud storage and retrieval of healthcare data documents. Existing schemes, however, often suffer from inefficient retrieval and increased risk of privacy disclosure when dealing with massive data. We propose here a new Efficient Encrypted Parallel Ranking (EEPR) search system, block-based and privacy-preserved, for encrypted cloud healthcare data. We design a parallel binary search tree structure in block and propose a parallel retrieval algorithm adaptable to such a structure. A quantitative analysis through the information retention index shows that our scheme demonstrates better search performance. In addition, feature vectors generated from our scheme are difficult to be reversely analyzed due to unexplainability, enhancing privacy protection for patients and researchers. A formal security analysis shows that our EEPR scheme is resistable to known background attack, and yields a lower time complexity and significantly improves search efficiency as well as accuracy over existing schemes.

With the explosive increase of document files, more and more data owners outsource their documents to the public cloud, which can decrease the costs of local data management systems. However, the problem of information privacy leakage in the cloud is a great challenge and it has been attracting more and more attention. In this article, we propose a secure and efficient document search scheme, named SES, based on both the cloud and fog systems. All the documents are symmetrically encrypted before being outsourced to the cloud, and an index vector is constructed based on the keywords for each document. Specifically, we integrate the position information of keywords into the TF-IDF model to generate document vectors, which are accurate and inherent summarizations about the documents. In query requests, a data user needs to provide a set of keywords, which are first extended by the Word2Vec tool and then mapped to a query vector. The extension process of keywords makes the provided keywords more comprehensive and accurate, and hence, it improves document search accuracy. To achieve the forward and backward security, both the document vectors and query vectors are appended with an ingenious vector. The relevance score between a document and a query is defined as the inner product of the document vector and the query vector. We return the most relevant documents as the search results to the data users. To protect the contextual information stored in the document and query vectors, we encrypt the vectors by the secure kNN algorithm. To improve the search efficiency, a searchable index structure for the document set is constructed based on the Diffie–Hellman secret key negotiation algorithm. The analysis and simulation results illustrate that the proposed scheme performs well in terms of both security and search efficiency.