Cloud computing as a promising computing paradigm is increasingly utilized as potential hosts for users’ massive dataset. Since the cloud service provider (CSP) is outside the users’ trusted domain, existing research suggests encrypting sensitive data before outsourcing and adopting Searchable Symmetric Encryption (SSE) to facilitate keyword-based searches over the ciphertexts. However, it remains a challenging task to design an effective SSE scheme that simultaneously supports sublinear search time , efficient update and verification , and on-demand information retrieval . To address this, we propose a Verifiable Dynamic Encryption with Ranked Search (VDERS) scheme that allows a user to perform top- $K$ searches on a dynamic document collection and verify the correctness of the search results in a secure and efficient way. Specifically, we first provide a basic construction, $\mathsf {VDERS}^0$ , where a ranked inverted index and a verifiable matrix are constructed to enable verifiable document insertion in top- $K$ searches. Then, an advanced construction, $\mathsf {VDERS}^{\star }$ , is devised to further support document deletion with a reduced communication cost. Extensive experiments on real datasets demonstrate the efficiency and effectiveness of our VDERS scheme.

Vehicular ad-hoc networks (VANETs) are the specific sort of ad-hoc networks that are utilized in intelligent transportation systems (ITS). VANETs have become one of the most reassuring, promising, and quickest developing subsets of the mobile ad-hoc networks (MANETs). They include smart vehicles, roadside units (RSUs), and on-board units (OBUs) which correspond through inconsistent wireless network. The current research in the vehicles industry and media transmission innovations alongside the remarkable multimodal portability administrations expedited center-wise ITS, of which VANETs increase considerably more attention. The particular characteristics of the software defined networks (SDNs) use the vehicular systems by its condition of the centralized art having a complete understanding of the network. Security is an important issue in the SDN-based VANETs, as a result of the effect the threats and vulnerabilities can have on driver’s conduct and personal satisfaction. This paper opens a discourse on the security attacks that future SDN-based VANETs should confront and examines how SDNs could be advantageous in building new countermeasures. SDN-based VANETs encourage us to dispose of the confinement and difficulties that are available in the traditional VANETs. It helps us to diminish the general burden on the system by dealing with the general system through a single wireless controller. While SDN-based VANETs provide us some benefits in terms of applications and services, they also have some important challenges which need to be solved. In this study we discuss and elaborate the challenges, along with the applications, and the future directions of SDN-based VANETs. At the end we provide the conclusion of the whole study.

Vehicle-based mobile crowdsensing has gained widespread attention due to its low cost and efficient data collection mode. One common method to improve the accuracy of sensing data in this context is truth discovery. However, the emergence of privacy leakage and data misuse has reduced users’ motivation to participate in sensing tasks. Meanwhile, existing solutions for privacy-preserving truth discovery generally suffer from low computational efficiency and frequent interactions between users and servers. Hence, this paper proposes a novel privacy-preserving truth discovery scheme based on secure multi-party computation. For the purpose of high efficiency and strong privacy protection, we utilize the Secret Sharing method to securely decompose data and construct a Secure Multi-party Computation protocol to compute the ground truth. In addition, the weight value generated by truth discovery is employed as a quantitative data quality indicator that dynamically adjusts the user’s rewards and constructs a data quality-driven incentive mechanism. Finally, we demonstrate the high performance of our method through a detailed analysis, showing its effectiveness even in scenarios with numerous users.

Task matching is widely used for participant selection in mobile crowdsensing (MCS). However, accurate task matching relies on collecting a large amount of user information, which has the risk of privacy leakage. Existing privacy-preserving task matching methods have the disadvantages of low matching efficiency and coarse matching granularity, and are difficult to apply to MCS because of higher real-time requirements. In this article, we propose a spatiotemporal-aware privacy-preserving task matching scheme, achieving efficient and fine-grained matching while protecting privacy between users and task publishers. Specifically, the time matching score (TMS) and location matching score (LMS) between users and tasks are defined for the spatiotemporal requirement of MCS. In addition, a lightweight protocol called SCP (secure computing protocol) is constructed based on Shamir secret sharing and Carmichael theorem for securely calculating TMS and LMS and matching attribute values by size and range. The correctness and security of our scheme are proved by detailed theoretical analysis, and the experimental result shows that the computational overhead of our proposed scheme is only 10% of that in the scheme we compared with, while the difference in communication overhead is less than 200 KB.

The huge values created by Big Data and the recent advances in cloud computing have been driving data from different sources into cloud repositories for comprehensive query services. However, cloud-based data fusion makes it challenging to verify if an untrusted server faithfully integrates data and executes queries or not. This is even harder for range-aggregate queries that apply aggregate operations on data within given ranges. In this paper, we propose a query authentication scheme, named ${\sf MARS}$ , enabling a user to efficiently authenticate range-aggregate queries on multi-source data. Specifically, ${\sf MARS}$ creates a VG-tree by subtly integrating Expressive Set Accumulator into a multi-dimensional G-tree while signing the root digest with a multi-source aggregate signature scheme. Compared with previous solutions, ${\sf MARS}$ has the following merits: (1) Practicality. Instead of treating range and aggregate queries separately, the user can directly verify the statistical result of selected data. (2) Scalability. Instead of authenticating the individual result from each source, the user can perform an aggregative validation on the integrated result from multiple sources. The experimental results demonstrate the effectiveness of MARS. For large-scale data fusion, the user-side verification time increases by only 103 ms as the amount of data sources increases by five times.