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.

Aiming at the problem of confidential determination of the positional relationship between automatic guided vehicle (AGV) and elliptical forbidden area in intelligent warehousing, this paper transforms the problem into the problem of confidential determination of the positional relationship between a point and an ellipse and proposes a determination scheme based on a secure multi-party computation, which utilizes Paillier encryption algorithm to substitute the point coordinates into the elliptical general-form equations and realizes the confidential determination of the positional relationship by the sign of the computation results. Firstly, the confidential determination protocol of the positional relationship between point and ellipse under the semi-honest model is designed, based on which the potential malicious attack behaviors are analyzed in depth, including malicious behaviors such as the malicious adversary forging the calculation results or terminating the protocol. In response to these malicious behaviors, a confidentiality determination procedure was created using a malicious model and encryption techniques including zero-knowledge proofs and the cut-and-choose. The efficiency of the strategy suggested in this study has greatly increased after an analysis of its correctness, likelihood of successful attacks by malevolent adversaries, and efficiency.

Data aggregation can integrate scattered and isolated vehicle data into useful information for real-time traffic analysis, road condition prediction, or intelligent decision-making. In the Internet of Vehicles (IoV) scenario, secure data aggregation ensures the privacy and security of original vehicle data (such as location, speed, and driving status), which is of great significance for complying with user privacy protection regulations and preventing sensitive information leakage. Based on the threshold NTRU encryption algorithm and a novel encoding method, this paper proposes a multi-party secure data aggregation protocol suitable for IoV. Furthermore, to address potential cheating behaviors by malicious participants, an anti-cheating protocol is designed to detect and prevent the submission of false data in critical steps. Both protocols can compute statistical metrics (total count, sum, average, maximum, minimum, median, variance, and standard deviation) for multi-party vehicle data in a single run and support partial result output on demand. Simulation-based proofs demonstrate that the protocols can resist n-1 collusion attacks. Performance analysis and experimental tests show that the protocols outperform existing schemes in computational and communication efficiency, and are particularly adaptable to the high dynamics and real-time requirements of IoV. This study provides an efficient and feasible solution to the problem of privacy-preserving data aggregation in IoV.

Smart contract is a computerized protocol for informationally disseminating, validating, or enforcing a contract when specific conditions are met. The similarity calculation of the graph is used to compare the similarity between different smart contracts to detect potential malicious contracts or plagiarism. At present, most of the secure multi-party calculations of graph structure similarity are designed under the semi-honest model, which cannot resist malicious enemies. In this paper, a new coding method based on prime number is proposed and a semi-honest computing graph similarity protocol is designed by using ElGamal Threshold encryption algorithm. Then we analyze the possible malicious attack behavior in the semi-honest model protocol and design the secret computational graph similarity protocol with the help of zero-knowledge proof cryptography tool. Finally, the real/ideal model paradigm is used to prove that the protocol is secure under the malicious model, and the efficiency of the protocol is proved by comparing with the existing schemes.

The key method and technology for resolving the privacy computation challenge is secure multi-party computation, which enables participants to derive the outcome securely without disclosing personal information. The problem of sets intersection and union is one of the important problems in the research topics of secure multi-party computation, which has essential application value and practical significance in the fields of data feature extraction, data merging or reconstruction in internet of things. In this paper, we analyze secure computation protocols of multi-party sets intersection and union under the semi-honest model, and examine possible malicious behaviors. For above malicious behaviors, based on modified NTRU-type multi-key fully homomorphic encryption scheme, we propose secure computation protocols of multi-party sets intersection and union against malicious spoofing and its security is proved using real/ideal model paradigm. Through experimental simulation tests, the results show that our anti-malicious spoofing protocol has improved efficiency and guaranteed security compared to similar encryption protocols and is applicable to internet of things scenarios dealing with small-scale datasets.

In the specific value computation scenario of blockchain cross-chain data exchange, participants can organize raw data in the form of data pairs, making the computed specific values become key information for cross-chain collaboration. The main challenges faced in this scenario include: the lack of appropriate privacy protection mechanisms leading to easy leakage of sensitive data; the vulnerability of unencrypted or unvalidated data to tampering or malicious attacks; a crisis of trust among users. To address the above problems, this paper introduces secure multi-party computation (MPC) into the cross-chain data exchange process to ensure the fairness of the computation process and protect data privacy. To address the problem of computing the maximum and minimum values of the sums of keyword-corresponding values in cross-chain interactions, it is transformed into the secure computation scheme of computing the maximum and minimum values of the sums of corresponding elements in the intersection of sets (MMSI). A secure computation scheme for maximum and minimum values based on the fully homomorphic NTRU (FH-NTRU) encryption algorithm is proposed. First, a secure computation protocol for MMSI without a universal set under the semi-honest model is proposed. Then, to address potential malicious behaviors in the protocol, a secure computation protocol for MMSI under the malicious model is proposed, using the cut-and-choose method. The protocol under the malicious model is analyzed for correctness and the security of the protocol is proved using the real/ideal model paradigm. Finally, the efficiency analysis and experimental simulations show that the protocol is efficient and reliable, it can resist malicious adversary attacks and ensure the correctness of the computation results while effectively improving the security during cross-chain interactions.

With the advancement of blockchain technology, smart contracts are increasingly applied in finance, supply chain, healthcare, and other domains. However, the demand for multi-party data joint analysis within these contracts faces challenges of privacy leakage and malicious deception. This paper applies secure multi-party computation (MPC) to smart contracts, based on the secure computation of intersection, union, and difference of multi-party multisets (IUDMM), to protect data privacy while supporting joint analysis. Existing multiset computation protocols are primarily designed for two-party scenarios under the semi-honest model, which are unsuitable for applications involving multiple participants in smart contracts. This paper introduces a protocol for IUDMM under the semi-honest model, utilizing the multi-key NTRU encryption algorithm and a novel vector encoding method. Furthermore, to address potential malicious behaviors, an IUDMM protocol under the malicious model is designed by incorporating digital commitment method. The protocol's correctness is analyzed, and its security is proven using the real/ideal model paradigm. Additionally, it also resists collusion attacks by any party. Finally, efficiency analysis and experimental simulations demonstrate that the proposed protocols are efficient, reliable, and fair, providing a secure and practical solution for multi-party data joint analysis and privacy protection in smart contracts.

Federated learning promotes the development of cross-domain intelligent applications under the premise of protecting data privacy, but there are still problems of sensitive parameter information leakage of multi-party data temporal alignment and resource scheduling process, and traditional symmetric encryption schemes suffer from low efficiency and poor security. To this end, in this paper, based on the modified NTRU-type multi-key fully homomorphic encryption scheme, an asymmetric algorithm, a secure computation scheme of multi-party least common multiple and greatest common divisor without full set under the semi-honest model is proposed. Participants strictly follow the established process. Nevertheless, considering that malicious participants may engage in poisoning attacks such as tampering with or uploading incorrect data to disrupt the protocol process and cause incorrect results, a scheme against malicious spoofing is further proposed, which resists malicious spoofing behaviors and not all malicious attacks, to verify the correctness of input parameters or data through hash functions and zero-knowledge proof, ensuring it can run safely and stably. Experimental results show that our semi-honest model scheme improves the efficiency by 39.5% and 45.6% compared to similar schemes under different parameter conditions, and it is able to efficiently process small and medium-sized data in real time under high bandwidth; although there is an average time increase of 1.39 s, the anti-malicious spoofing scheme takes into account both security and efficiency, achieving the design expectations.

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.

With the wide applications of wireless sensor networks (WSNs) 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 article proposes a scheme that detects malicious nodes by securely computing spatially parallel straight-line distance (SPSLD) and combining it with secure multiparty 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, a 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 the 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.

With the advent of the big data era, deep learning techniques have achieved remarkable success in image classification. However, their heavy reliance on large-scale annotated data limits the generalization capability of models on unseen data types, particularly in complex scenarios where traditional methods face challenges such as low annotation efficiency and strong subjectivity. To address these issues, this paper proposes a multi-attention contrastive learning-based image classification algorithm. By improving the ResNet50 architecture in the SimCLR framework with the Convolutional Block Attention Module (CBAM), our method improves the model’s ability to extract finegrained image details. CBAM uses channel and spatial attention mechanisms to focus on the importance of feature channels and key spatial regions, respectively, thereby enhancing the model adaptability to complex scenes. Experiments conducted on the CIFAR-10 and STL-10 datasets demonstrate that our improved SimCLR model outperforms conventional SimCLRRes18 and SimCLR-Res50 models in both Top-1 and Top-5 accuracy. By incorporating CBAM into ResNet50, our approach significantly enhances the performance of the model in image classification tasks. The results indicate that this method not only reduces dependence on labeled data but also improves generalization capability and classification precision, showcasing broad application potential.

In the context of rapid advancements in big data and artificial intelligence, similarity measurement methods between samples have been widely applied in data mining, pattern recognition, medical diagnosis, financial risk control, and other fields. The Mahalanobis distance, due to its effectiveness in capturing correlations within high-dimensional data, has become a crucial tool in many practical scenarios. However, sample data often contains sensitive privacy information, making it essential to achieve secure and privacy-preserving computation of Mahalanobis distance. This paper proposes a secure Mahalanobis distance calculation scheme tailored for sample vectors that effectively resists malicious cheating behaviors. The designed multi-party computation algorithms ensure privacy protection while maintaining computational efficiency and minimizing communication overhead. The experimental results compare three algorithms in terms of execution time and communication delay across varying sample sizes and vector dimensions. The results demonstrate that our proposed scheme achieves a favorable balance between security and performance. This research provides a practical and robust solution for similarity measurement under privacy constraints and lays a theoretical and practical foundation for secure data collaboration in multi-party computing environments, offering significant application potential.

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 swift evolution of information technology is catalyzing a transformative fusion between cloud-integrated Internet of Things (IoT) and smart healthcare, poised to usher in revolutionary transformations within the medical sector. Through iot technology, various medical devices, sensors, and monitoring systems can connect and exchange data in real time, enabling remote monitoring, intelligent analysis, and personalized medicine. The confluence of the expanding Internet of Things (IoT) and the blossoming smart healthcare sector is set to streamline medical resource allocation, uplift healthcare service quality, and markedly ameliorate patients' experiences in healthcare settings. At the same time, it also brings some challenges, such as data privacy protection, standardization and security issues, which need continuous scientific and technological innovation and policy support to solve. In this paper, the row index obfuscation Bloom filter is designed in the scenario of smart healthcare to protect the privacy of data. Compared with other schemes, the proposed scheme has better privacy protection and better performance, and the proposed scheme is secure. In a word, the combination of iot and smart healthcare is promoting the digital transformation and innovative development of the medical industry, and providing more intelligent and convenient medical services for people's health.

Secure multi -party computation is a hotspot in the cryptography field, and it is also a significant means to realize privacy computation. The Millionaires' problem is the most fundamental problem among them, which is the basic module of secure multi -party computation protocols. Although there are many solutions to this problem, there are few anti-malicious adversarial protocols besides protocols based on Yao's garbled circuit. Only a few solutions have low efficiency, and there is no protocol for rational numbers comparison under the malicious model, which restricts the solution of many secure multi -party computation problems. In this paper, the possible malicious behaviors are analyzed in the existing Millionaires' problem protocols. These behaviors are discovered and taken precautions against through the triangle area formula, zero-knowledge proof, and cut-andchoose method, so the protocol of comparing confidentially rational numbers is proposed under the malicious model. And this paper adopts the real/ideal model paradigm to prove the security of the malicious model protocol. Efficiency analysis indicates that the proposed protocol is more effective than existing protocols. The protocol of rational numbers comparison under the malicious model is more suitable for the practical applications of secure multi -party computation, which has important theoretical and practical significance.

The grading of products is important in many ways. One of the important activities after harvesting agricultural products is product grading based on shape and color dimensions. This activity in the agricultural transformation industries, Bas Controller, improves various processes on fruits and vegetables with the same dimensions, which improves the storage conditions of the product, creates added value for the farmer, and gives the consumer the power to choose. The main focus of this study is the application of image processing in the field of identification and classification of fruits. It is an application that has received much less attention than other applications of image processing. The proposed systems presented in this article, are software solutions based on image processing techniques, including histogram matching techniques, for detection, Sable edge detection algorithms, Private edge and Kenny edge, Otsu threshold limit, and clustering method It is an automatic mean and classification of different degrees of fruit. In addition, it has been mentioned more about the examination and description of product grading and clustering methods, that by using the proposed application hardware and its connection with the software, a big step can be taken in product quality grading. This method can be used in product classification and packaging. The accuracy rate for peaches, lemons, apples, and tomatoes is 94.58%, 88.23%, 70%, and 93.33%, respectively. The best accuracy for all 20 sample levels is for peach fruit.

Data aggregation is the process of combining multiple data points or data sets into a larger data set. Through calculation, statistics or other means, it can provide higher level data information, which is widely used in different data types and fields such as database query, data analysis, data mining and data processing. In data processing scenarios, data aggregation functions play a key role. They are used to perform aggregate calculations on data, such as finding the maximum, minimum, and maximum difference of multiple data. However, it is important to pay special attention to protecting sensitive information and privacy when performing data aggregation. Aiming at this problem, based on the threshold NTRU encryption algorithm with additive homomorphism, combining the vector encoding method and the ciphertext re-randomization method, this paper proposes a secure calculation protocol for the maximum value, minimum value and maximum difference in multi-party data sets under the semi-honest model. To verify the security of the proposed protocol, we use the simulation paradigm approach for security proof and show that the protocol can resist the attack of the maximum number of colluders. Through theoretical analysis and simulation experiments, we show the high efficiency of the proposed scheme, and prove that the protocol has practical value.

Secure multi-party computation of Chebyshev distance represents a crucial method for confidential distance measurement, holding significant theoretical and practical implications. Especially within electronic archival management systems, secure computation of Chebyshev distance is employed for similarity measurement, classification, and clustering of sensitive archival information, thereby enhancing the security of sensitive archival queries and sharing. This paper proposes a secure protocol for computing Chebyshev distance under a semi-honest model, leveraging the additive homomorphic properties of the NTRU cryptosystem and a vector encoding method. This protocol transforms the confidential computation of Chebyshev distance into the inner product of confidential computation vectors, as demonstrated through the model paradigm validating its security under the semi-honest model. Addressing potential malicious participant scenarios, a secure protocol for computing Chebyshev distance under a malicious model is introduced, utilizing cryptographic tools such as digital commitments and mutual decryption methods. The security of this protocol under the malicious model is affirmed using the real/ideal model paradigm. Theoretical analysis and experimental simulations demonstrate the efficiency and practical applicability of the proposed schemes.

In recent years, with the development of information security, secure multi-party computation has gradually become a research hotspot in the field of privacy protection. The intersection and union computation of graphs is an important branch of secure computing geometry. At present, the intersection and union of graphs are almost designed under the semi-honest model, and few solutions are proposed under the malicious model. However, the solution under the malicious model is more secure and has important theoretical and practical significance. In this paper, the possible malicious behaviors of computing the intersection and union of graphs are analyzed. Using the Lifted-ElGamal threshold cryptosystem and zero-knowledge proof method, the secure multi-party computation algorithm of graphs’ intersection and union under the malicious model is designed. The real/ideal model paradigm is used to prove the security of the algorithm, the efficiency of the algorithm is analyzed in detail, and the feasibility is verified through experiment.

With the development of deep learning, the demand for similarity matching between texts in text classification is becoming increasingly high. How to match texts quickly under the premise of keeping private information secure has become a research hotspot. However, most existing protocols currently have full set limitations, and the applicability of these methods is limited when the data size is large and scattered. Therefore, this paper applies the secure vector calculation method for text similarity matching in the case of data without any complete set constraints, and it designs a secure computation protocol of text similarity (SCTS) based on the semi-honest model. At the same time, elliptic-curve cryptography technology is used to greatly improve the execution efficiency of the protocol. In addition, we also analyzed the possibility of the malicious behavior of participants in the semi-honest-model protocol, and further designed an SCTS protocol suitable for the malicious model using the cut-and-choose and zero-knowledge-proof methods. By proposing a security mechanism, this protocol aims to provide a reliable and secure computing solution that can effectively prevent malicious attacks and interference. Finally, through the analysis of the efficiencies of the existing protocols, the efficiencies of the protocols under the malicious model are further verified, and the practical value for text classification in deep learning is demonstrated.