The advance of Internet of Things (IoT) techniques has promoted an increasing number of organizations to explore more mission-critical solutions. However, the response latency, bandwidth usage, and reliability are still challenging issues in traditional IoT. To tackle these challenges, fog-based IoT has become popular and range query is one of the most frequently used operations in fog-based IoT, where, given a range query, a fog node will return the aggregated data from IoT devices to the query user. Because the fog nodes are not fully trusted, there is a desire to design a privacy-preserving range query scheme in fog-based IoT. However, most of existing privacy-preserving range query schemes are not efficient in terms of communication overhead, especially for a large-size range. Therefore, it is still a challenging issue to design a communication-efficient range query in fog-based IoT. Aiming at this challenge, in this paper, we propose a new privacy-preserving range query scheme in fog-based IoT. Specifically, we first devise an efficient homomorphic encryption scheme for maintaining the data privacy and security in a range query. Then, we present a novel range decomposition technique to compile the range query, which can transform a given range query L,U, where 0≤L≤U≤n-1, into a semi-triangular structure, and enable our proposed scheme to achieve O(log3n) communication efficiency. Detailed security analysis shows that our proposed scheme is really privacy-preserving, and the extensive performance evaluation demonstrates that our proposed scheme is efficient in terms of low communication overhead and computational cost.

Similarity query over time series data plays a significant role in various applications, such as signal processing, speech recognition, and disease diagnosis. Meanwhile, driven by the reliable and flexible cloud services, encrypted time series data are often outsourced to the cloud, and as a result, the similarity query over encrypted time series data has recently attracted considerable attention. Nevertheless, existing solutions still have issues in supporting similarity queries over time series data with different lengths, query accuracy and query efficiency. To address these issues, in this article, we propose a new efficient and privacy-preserving similarity range query scheme, where the time warp edit distance (TWED) is used as the similarity metric. Specifically, we first organize time series data into a $k$ d-tree by leveraging TWED’s triangle inequality, and design an efficient similarity range query algorithm for the $k$ d-tree. Second, based on a symmetric homomorphic encryption technique, we carefully devise a suite of privacy-preserving protocols to provide a security guarantee for $k$ d-tree based similarity range queries. After that, by using the similarity range query algorithm and these protocols, we propose our privacy-preserving similarity range query scheme, in which we elaborate on two strategies to make our scheme resist against the cloud inference attack. Finally, we analyze the security of our scheme and conduct extensive experiments to evaluate its performance, and the results indicate that our proposed scheme is indeed privacy-preserving and efficient.

In order to protect the privacy of individual data, meantime guaranteeing the utility of big data, the privacy preserving data aggregation is widely researched, which is a feasible solution since it not only preserves the statistical feature of the original data, but also masks single user’s data. With smart meter owning the capability of connecting to Internet, the aggregation area extends to the virtual area rather than a traditional physical area. However, in a virtual aggregation area, the users’ membership maybe frequently changes, if while executing the aggregation protocol for the traditional area, the overhead is not ignorable. In this paper, the homomorphic encryption and ID-based signature are employed to design a dynamic membership data aggregation (DMDA) scheme, which reduces the complexity on a new user’s joining and an old user’s quitting. In addition, the operation center obtains the sum of the data in the virtual aggregation area, meantime knows nothing about single user’s data. Comparing with traditional privacy-preserving data aggregation scheme, DMDA is more suitable for next-generation smart grid and other Internet of Things environments.

Cloud storage as one of the most important services of cloud computing facilitates cloud users to outsource their data to the cloud for storage and share them with authorized users. In cloud storage, secure deduplication has been investigated as it can eliminate the redundancy over the encrypted data to reduce storage space and communication overhead. Regarding the security and privacy, many existing secure deduplication schemes generally focus on achieving the following properties: data confidentiality, tag consistency, access control and resistance to brute force attacks. However, as far as we know, none of them can achieve these four requirements at the same time. To overcome this shortcoming, in this paper, we propose an efficient secure deduplication scheme that supports user-defined access control. Specifically, by allowing only the cloud service provider to authorize data access on behalf of data owners, our scheme can maximally eliminate duplicates without violating the security and privacy of cloud users. Detailed security analysis shows that our authorized secure deduplication scheme achieves data confidentiality and tag consistency while resisting brute-force attacks. Furthermore, extensive simulations demonstrate that our scheme outperforms the existing competing schemes, in terms of computational, communication and storage overheads as well as the effectiveness of deduplication.

Due to its capabilities of searches and updates over the encrypted database, the dynamic searchable symmetric encryption (DSSE) has received considerable attention recently. To resist leakage abuse attacks, a secure DSSE scheme usually requires forward and backward privacy. However, the existing forward and backward private DSSE schemes either only support single keyword queries or require more interactions between the client and the server. In this paper, we first give a new leakage function for range queries, which is more complicated than the one for single keyword queries. Furthermore, we propose a concrete forward and backward private DSSE scheme by using a refined binary tree data structure. Finally, the detailed security analysis and extensive experiments demonstrate that our proposal is secure and efficient, respectively.

The sixth-generation (6G) communication technology has been attracting great interests from both industry and academia, as it is regarded as a promising approach to achieve more stable and low-latency communication. These promising features of 6G make it an enabler for cybertwin, a technique to create digital representations for physical objects to implement various functionalities. In this article, we consider a cybertwin-based spatiotemporal keyword query service over a dynamic message data set in intelligent transportation system (ITS) scenarios. Particularly, in the considered service, publishers upload messages to the cloud, and each cybertwin predictively launches queries to retrieve messages on behalf of the corresponding vehicle, such that each vehicle can timely receive messages that are of its interest whenever it arrives at a location. Nevertheless, as the cloud is not fully trustable, there exist privacy concerns related to the messages and queries. Up to now, although many schemes have been proposed to handle privacy-preserving spatial, temporal, or keyword queries, none of them can simultaneously support queries containing both spatial, temporal, and keyword criteria on dynamic data sets. Aiming at the issue, we design a layered index based on segment trees to dynamically organize messages containing both spatial, temporal, and keyword information. Moreover, based on a symmetric homomorphic encryption scheme, we encrypt the messages and queries and present a two-server privacy-preserving spatiotemporal keyword query scheme. We analyze the security of the proposed scheme and also conduct extensive experiments to evaluate its performance. The results show that our proposed scheme is indeed privacy preserving and computationally efficient.