With the rapid development of the Internet of Vehicles (IoV), achieving trustworthy vehicle position verification while preserving location privacy has become a key requirement in intelligent traffic supervision scenarios such as defense control zones and urban restricted-access areas. Existing privacy-preserving schemes have difficulty simultaneously supporting accurate determination of complex-shaped prohibited areas and efficient computation, and still face malicious attacks such as interference with verification procedures, tampering with communication processes, and privacy inference when determining the positional relationship between vehicles and prohibited areas. To address these issues, this paper proposes an efficient privacy-preserving position verification (PPPV) scheme based on secure multi-party computation (MPC). The scheme supports arbitrary polygonal prohibited areas, including convex, concave, and self-intersecting polygons, thereby improving its applicability in complex IoV supervision scenarios. Based on an improved cross-product determination method, this paper constructs an efficient PPPV protocol under the semi-honest model, achieving near-plaintext computational efficiency while protecting the privacy of both vehicle locations and area boundaries. To resist malicious attacks, this paper further combines Paillier homomorphic encryption, the cut-and-choose method, and zero-knowledge proof to construct a secure PPPV protocol under the malicious model, which can effectively prevent protocol deviations, result tampering, and inference attacks. This paper also conducts formal security proof based on the real/ideal model paradigm, and evaluates the performance of the scheme through benchmark experiments and attack experiments. Experimental results show that the scheme achieves a good balance among efficiency, applicability, and security, providing a deployable trustworthy position verification mechanism for next-generation IoV intelligent supervision applications.

In computer vision, the intersection determination of polygonal areas is utilized to segment different regions in an image and assist in detecting the boundaries of the regions. Moreover, the secure computation of the intersection area of polygons can solve the private calculation of geometric problems in machine learning. A security protocol under the semi-honest model was designed for the problem of secure computation of the intersection area of two polygons. This protocol adopts a new coding method and the Paillier homomorphic encryption algorithm. Aiming at the malicious behaviors that malicious participants may carry out in the semi-honest protocol, a secure computation protocol for the intersection area of polygons under the malicious model was designed by using methods such as hash function, cut-and-choose and zero-knowledge proof. The security of this protocol was proved, and its computational complexity and communication complexity were analyzed. Compared with the existing schemes, it is more efficient.

ABSTRACT Malignant lung nodules can significantly affect patients' normal lives and, in severe cases, threaten their survival. Owing to the heterogeneity of computed tomography scans and the varying sizes of nodules, physicians often face challenges in diagnosing this condition. Therefore, a novel adaptive multi‐channel fusion network (AMCF‐Net) is proposed for computer‐aided diagnosis of lung nodules. First, a Multi‐Channel Fusion Model module is designed, which divides the channels into two parts in specific proportions, effectively extracting multi‐scale channel information while reducing network parameters. After the feature maps output at each layer of the AMCF‐Net, a novel adaptive depth‐wise separable convolution with a squeeze‐and‐excitation module is designed to adaptively integrate the feature maps of various stages of the AMCF‐Net, ensuring that the key lesions of lung nodules are not lost during classification. Finally, a hybrid loss scheme based on an adaptive mixing ratio is proposed to solve the problem of an imbalanced number of positive and negative nodule samples in the dataset. The model achieved the following test results: an accuracy of 90.22%, a specificity of 98.19%, an F1‐score of 86.57%, a sensitivity of 86.49%, and a G‐mean of 87.72%. Compared with other advanced networks, AMCF‐net delivers high‐precision lung nodule classification with minimal inference cost. Related codes have been released at: https://github.com/GuYuIMUST/AMCF‐net .

With the wide applications of wireless sensor networks (WSN) in the fields of smart transportation and industrial internet of things (IIoT), there is an increasing demand for their security and trustworthiness. To solve the problem of WSN’s malicious nodes such as identity forgery attacks, node spoofing, and man-in-the-middle attacks, this paper proposes a scheme that detects malicious nodes by securely computing spatially parallel straight-line distance (SPSLD) and combining it with secure multi-party computation (MPC). This scheme uses the NTRU encryption algorithm with the additive homomorphism to design the SPSLD secure computation protocol under the semi-honest model, and for the malicious attack behaviors present in it, the secure protocol under the malicious model is proposed with the cut-and-choose method. The correctness of the protocol under different models is analyzed, and the security is proved by real/ideal model paradigm. Performance comparison and experimental simulation results indicate that, while ensuring security: The computational complexity of the semi-honest model protocol is reduced by at least 85% compared to Paillier-based schemes, with execution time shortened by 32-46%. The malicious model protocol is 12% faster than similar attack-resistant schemes, effectively defending against malicious adversary attacks, although additional overhead is introduced, its execution efficiency remains within an acceptable range for WSN environments, providing an efficient solution for enhancing the security and reliability of WSN.

In recent years, with the development of blockchain, electronic bidding auction has received more and more attention. Aiming at the possible problems of privacy leakage in the current electronic bidding and auction, this paper proposes an electronic bidding auction system based on blockchain against malicious adversaries, which uses the secure multi-party computation to realize secure bidding auction protocol without any trusted third party. The protocol proposed in this paper is an electronic bidding auction scheme based on the threshold elliptic curve cryptography. It can be implemented without any third party to complete the bidding auction for some malicious behaviors of the participants, which can solve the problem of resisting malicious adversary attacks. The security of the protocol is proved by the real/ideal model paradigm, and the efficiency of the protocol is analyzed. The efficiency of the protocol is verified by simulating experiments, and the protocol has practical value.

Data fusion in the Internet of Things (IoT) is based on the intersection and union problems of sets without complete set restrictions to achieve accurate and reliable location services. However, protecting users’ privacy in IoT is challenging, for which a solution is proposed herein in conjunction with secure multi-party computation (MPC). Using the proposed solutions, users’ privacy can be ensured while achieving accurate and reliable location positioning. Current privacy-preserving schemes used for data fusion require a set’s elements belonging to an appropriate complete set, and the range of elements used by such schemes is usually integers. Moreover, a unified range of all elements in a set cannot be determined in real-time for practical scenarios. Limiting the range of elements to integers is also impossible, because the type of data collected by IoT devices is uncertain. To address these issues, new encoding and transformation methods are proposed herein to map a set of rational numbers to the Cartesian coordinate system. The NTRU encryption scheme is used to design the intersection and union MPC protocols of rational numbers’ sets without any complete set restriction under the semi-honest model. Then, to prevent possible malicious behaviors in the semi-honest model protocols, MPC protocols for the intersection and union of rational numbers’ sets are designed without any complete set. The security of the protocols is demonstrated by applying the real/ideal model paradigm. The comparison of the proposed protocols with other schemes shows that our protocols have higher computational efficiency.

The millionaires’ problem is the basis of secure multiparty computation and has many applications. Using a vectorization method and the Paillier encryption scheme, we first propose a secure two-party solution to the millionaires’ problem, which can determine x=y, x<y , or x>y in one execution. Subsequently, using the vectorization and secret splitting methods, we propose an information-theoretically secure protocol to solve the multiparty millionaires’ problem (a.k.a. secure sorting problem), and this protocol can resist collusion attacks. We analyze the accuracy and security of our protocols in the semihonest model and compare the computational and communication complexities between the proposed protocols and the existing ones.

Secure subset problem is important in secure multiparty computation, which is a vital field in cryptography. Most of the existing protocols for this problem can only keep the elements of one set private, while leaking the elements of the other set. In other words, they cannot solve the secure subset problem perfectly. While a few studies have addressed actual secure subsets, these protocols were mainly based on the oblivious polynomial evaluations with inefficient computation. In this study, we first design an efficient secure subset protocol for sets whose elements are drawn from a known set based on a new encoding method and homomorphic encryption scheme. If the elements of the sets are taken from a large domain, the existing protocol is inefficient. Using the Bloom filter and homomorphic encryption scheme, we further present an efficient protocol with linear computational complexity in the cardinality of the large set, and this is considered to be practical for inputs consisting of a large number of data. However, the second protocol that we design may yield a false positive. This probability can be rapidly decreased by reexecuting the protocol with different hash functions. Furthermore, we present the experimental performance analyses of these protocols.

Vehicular Ad Hoc Networks (VANETs) enable car-to-car communication without the support of network infrastructure, which introduce diverse application possibilities and have drawn much attention from academy and industry in the past years. Unlike other ad hoc networks, nodes in VANETs are restricted to move in streets and have limited communication ranges. Intuitively, vehicle-to-vehicle communication somehow has similarity to human-to-human interaction, which lead to an interesting question of exploring the social properties of VANET nodes. To address the question, we consider encounters of vehicles as their social relationships and model VANETs as social graphs. Based on the social graph model, we use two traces of mobile vehicles from San Francisco and Shanghai to explore their social properties. Our analysis show that several universal laws of social network are hold for VANETs. The social graphs forming by vehicles are scale-free networks with power-law like distribution of node degrees. Small world phenomenon is also observed in our experiments: the nodes in VANETs have high cluster coefficient and there exist short paths between node pairs less than 3 hops on average. The implication of our analytical results is of benefit to develop large scale software system for mobile applications such as VANETs, as well as helps to facilitate inter-device wireless communications in pervasive environment.