Connected autonomous vehicles (CAVs) utilize multi-modal sensors, such as LiDAR and high-definition cameras, to collect diverse types of sensing data. Fusing object detection information from these two modalities facilitates more accurate environmental perception. In this context, lightweight secret sharing techniques are employed to protect information privacy, enabling further calculation while effectively alleviating the computational resource constraints of CAVs. Meanwhile, such techniques require an additional third-party to generate some necessary random numbers. Addressing the challenges of privacy disclosure of multi-modal object information and the reliability of random numbers, we propose a malicious third-party-resistant privacy-preserving multi-modal object fusion model, termed MPOF. First, we develop a series of secure computation protocols that do not rely on time-consuming cryptographic primitives, including secure multiplication, secure sharing conversion, and secure comparison. Leveraging the idea of sacrificial verification, we can effectively detect malicious behavior by the third-party during the random number generation process. Second, we construct a secure object bounding-box matching module based on arithmetic secret sharing (ASS), enabling similarity calculation and matching of bounding-boxes between point cloud and image modalities. Additionally, we design a secure object score fusion module that achieves fusion and updating through secure implementations of convolution, ReLU, and Maxout operations. Detailed theoretical analysis and experimental results demonstrate that, compared to secure computation protocols using homomorphic encryption for random number generation, the proposed protocols reduce computational overhead by five orders of magnitude. Furthermore, the MPOF model constructed by integrating these protocols is secure, accurate, and efficient.

This paper investigates data communication and access control in satellite-assisted Internet of Things. In particular, given the characteristics of an open communication environment, a multi-layer heterogeneous network, and a time-varying topology in the Space-Air-Ground-Sea Integrated Network (SAGSIN), traditional data communication and security mechanisms built upon the TCP/IP architecture may not fully leverage their potential. Current networks are primarily responsible for end-to-end transmission of binary data, lacking the capability to semantically perceive and handle dynamic and decentralized content. This leads to significant performance gaps in the network. In other words, it is better to retrieve expected information directly from the network and perform content protection on it with low dependency. We propose DARS, a Data Access scheme with Robust and Secure content communication for Satellite-assisted Internet of Things (S-IoT), leveraging the architectural benefits of content centric networks and rich attributes of IoT. DARS enables automatic data retrieval and access control without additional mechanisms, such as online certificate distribution, homogeneous network, and other presuppositions, which facilitates DARS to be applied in various environments. Additionally, DARS integrates a combination of techniques, including semantic representation, cryptographic technologies, and content caching, from a network-centric perspective. Theoretical analysis and experimental simulations show that DARS simplifies system operations and offers a viable solution for satellite-assisted IoT-based applications.

In recent years, online sentiment texts are generated by users in various domains and in different languages. Binary polarity classification (positive or negative) on business sentiment texts can help both companies and customers to evaluate products or services. Sometimes, the polarity of sentiment texts can be modified, making the polarity classification difficult. In sentiment analysis, such modification of polarity is termed as polarity shifting , which shifts the polarity of a sentiment clue (emotion, evaluation, etc.). It is well known that detection of polarity shifting can help improve sentiment analysis in texts. However, to detect polarity shifting in corpora is challenging: (1) polarity shifting is normally sparse in texts, making human annotation difficult; (2) corpora with dense polarity shifting are few; we may need polarity shifting patterns from various corpora. In this article, an approach is presented to extract polarity shifting patterns from any text corpus. For the first time, we proposed to select texts rich in polarity shifting by the idea of natural annotation , which is used to replace human annotation. With a sequence mining algorithm, the selected texts are used to generate polarity shifting pattern candidates, and then we rank them by C-value before human annotation. The approach is tested on different corpora and different languages. The results show that our approach can capture various types of polarity shifting patterns, and some patterns are unique to specific corpora. Therefore, for better performance, it is reasonable to construct polarity shifting patterns directly from the given corpus.