The searchable encryption (SE) is a particular case of structured encryption, which has been intensively researched in the secure cloud storage system. By constructing a structured secure index, such as encrypted multimaps (EMMs), encrypted inverted index (EII), etc., SE can achieve efficient keyword search over the encrypted data set. However, existing SE constructions do not take search permissions into consideration, resulting in the lack of a mechanism of the data access control, which may not be suitable for Industrial Internet of Things (IIoT) applications, since an integrated industrial system contains all kinds of data with rigorous access permissions. In this article, we construct an attribute-based SE (ABSE) construction for a cloud-assisted IIoT application scenario. By designing the novel access policy-based structured secure index and the attribute-based search token, our construction achieves fine-grained keyword search privilege control over encrypted IIoT data as well as the same search complexity as the traditional SE. To the best of our knowledge, this is the first ABSE construction. We provide the correctness and security proofs for our construction. Experimental evaluation results in a real-world data set show the correctness and the practical search efficiency of the proposed ABSE.

Existing indoor semantic recognition schemes are capable of discovering patterns through smartphone sensing; however, there is a lack of a device-free indoor semantic recognition system. In this paper, we propose WiFiMap+, which is a first-ever automatical inference system using WiFi signals to recognize highlevel indoor semantics from human activities and environments, where the high-level indoor semantics consist of indoor facilities and environments. To characterize the static indoor environments and dynamical human activities separately with channel state information (CSI), we propose a novel two-stream architecture to generate the spatial streams and the movement streams independently. Compared to the recent research on activity recognition, this two-stream architecture can make the content area of CSI samples extend from human activities to indoor environments. For obtaining accurate indoor environment detection, we propose a CSI-environment model with a spatial stream generation algorithm, which can reduce the effect of human activities on environment detection. For stable activity recognition, we also propose an environment-based testing sample representation method, which can utilize the environment knowledge to overcome the diversity of CSI caused by the environment changes. Finally, we implement WiFiMap+ using commercial WiFi devices and evaluate its performance for seven common semantic detection cases in sixroom scenarios. The experimental results show that our proposed WiFiMap+ is robust to the multi-room scenario and can achieve the average accuracy of 92.8% and the lowest accuracy of about 82%.

Existing WiFi recognition schemes are capable of discovering patterns of indoor semantics such as human activity, identity, indoor environment, and so on. We note that, channel state information (CSI) presents an opportunity for hackers to learn the indoor privacy, however, currently there is a lack of security research on CSI. In this paper, we are the first to discuss and define the security problem of CSI signals, which is further extended to the problems of non-targeted protection and targeted protection. To solve them, we present two types of adversarial autoencoder networks (AAENs). Through replacing the original signals with the generated adversarial ones, the protected semantic features are modified, and the significant features of the other semantics required to be recognized are reserved. Intensive evaluations demonstrate that, with the proposed AAENs, the recognition accuracy of the protected semantic can be significantly decreased, while still maintaining the other semantics to be identified correctly.

In mobile crowdsensing (MCS) system, a variety of sensors are required to operate together to glean and upload sensory data to clouds for processing. In real practice, truth discovery has been widely explored to find reliable information from various mobile devices. Under the requirement of MCS security, privacy-preserving truth discovery (PPTD) causes wide concern, which refers to discovering truthful information from these unreliable uploaded data while protecting users' private information. Although many PPTD mechanisms have been proposed, they can either not guarantee low communication from users to the cloud, or fail to realize fully strong privacy protect including sensing data privacy, weight privacy, intermediate privacy, and estimated truth privacy. This study designs a collaborative cloud encryption architecture. In this framework, we propose a new system architecture that adapts a two-cloud peer model while leveraging garbled circuit (GC). Users only need to transfer data to two clouds once, which realizes low users workloads while supporting dynamic users. The two cloud terminals cooperate to complete the weight update through the GC but execute the truth discovery algorithm, respectively. In this way, the noninteractive comes true and the users' workloads are transferred to the cloud server side. The weight update finish and weight privacy are fulfilled through GC, meanwhile the other intermediate values maintain strong privacy. At the same time, because the collaborative cloud architecture of the two clouds ensures the confidentiality of the truth value in the cloud and the homomorphism at the inquiry side, it ensures the strong privacy of the whole process from users to inquiries. The performance of this proposed method is verified through extensive evaluations.

The attribute-based keyword search (ABKS), which simultaneously achieves searching and fine-grained access control over encrypted data, is frequently applied in cloud computing environments characterized by data storage and sharing. Recently, inspired by attribute-based encryption (ABE) and searchable encryption (SE) primitives, several ABKS schemes have been presented. However, almost all existing ABKS schemes actually only provide an attribute-based keyword equality match function and do not have a structural index to support practical search efficiency and dynamic data updates in real-world applications. To the best of our knowledge, this study is the first to realize an attribute-based keyword search construction supporting numerical comparison expressions with the practical search efficient and dynamic data update capacity (ABKS-NICEST), based on our proposed attribute-based keyword secure search scheme supporting numerical comparison expressions (ABKS-NICE) and an exclusive OR-chain -based inverted index structure. To the best of our knowledge, ABKS-NICEST is the first attributed-based keyword search scheme with practical search efficiency and dynamic data update capacity. In addition, numerical values are an important and common attribute, so providing comparison expressions among numerical values can greatly enhance the expressivity of access policy. Therefore, we use the prefix membership verification technique to design a method to support any numeric comparison expression in a flexible and uniform manner. Through theoretical and experimental evaluations, we determine that ABKS-NICEST is the most efficient ABKS scheme.

Distributed data storage can assist compressive crowdsensing to solve the challenge of temporary data storage in the network. Block compressive sensing effectively addresses distributed data storage of large spatiotemporal data from crowdsensing, allowing efficient reconstruction at low storage and computational costs. However, when the data is unevenly distributed over the sensing area, existing algorithms ignore the variability of information between blocks and still require the central server to uniformly collect samples from each block stored on the mobile device, leading to a reduction in overall reconstruction accuracy. To address this, we propose an adaptive sampling allocation strategy that deeply analyzes the statistical information of each block which can help the central server to collect the number of measurements for each block adaptively to improve the sampling quality. Additionally, we consider the correlation between blocks and use a global denoising strategy to further improve the reconstruction accuracy. Experimental results demonstrate that, compared to the state-of-the-art DDS-CCS algorithm, our proposed adaptive sampling allocation with a joint-denoising mechanism significantly improves the accuracy of the information-rich blocks that most affect the global accuracy, and hence the global reconstruction accuracy. which also remains robust to different block sizes and exhibits improved stability.