Membership inference attacks on machine learning models have drawn significant attention. While current research primarily utilizes shadow modeling techniques, which require knowledge of the target model and training data, practical scenarios involve black-box access to the target model with no available information. Limited training data further complicate the implementation of these attacks. In this paper, we experimentally compare common data enhancement schemes and propose a data synthesis framework based on the variational autoencoder generative adversarial network (VAE-GAN) to extend the training data for shadow models. Meanwhile, this paper proposes a shadow model training algorithm based on adversarial training to improve the shadow model's ability to mimic the predicted behavior of the target model when the target model's information is unknown. By conducting attack experiments on different models under the black-box access setting, this paper verifies the effectiveness of the VAE-GAN-based data synthesis framework for improving the accuracy of membership inference attack. Furthermore, we verify that the shadow model, trained by using the adversarial training approach, effectively improves the degree of mimicking the predicted behavior of the target model. Compared with existing research methods, the method proposed in this paper achieves a 2% improvement in attack accuracy and delivers better attack performance.

To solve the privacy leakage problem of truck trajectories in intelligent logistics, this paper proposes a Quadtree-based Personalized Joint location Perturbation (QPJLP) algorithm using location generalization and local differential privacy techniques. Firstly, a flexible position encoding mechanism based on the spatial quadtree indexing is designed, and the length of the encoding can be adjusted freely according to data availability. Secondly, to meet the privacy needs of different locations of users, location categories are introduced to classify locations as sensitive and ordinary locations. Finally, the truck invokes the corresponding mechanism in the QPJLP algorithm to locally perturb the code according to the location category, allowing the protection of non-sensitive locations to be reduced without weakening the protection of sensitive locations, thereby improving data availability. Simulation experiments demonstrate that the proposed algorithm effectively meets the personalized trajectory privacy requirements while also exhibiting good performance in trajectory proportion estimation and Top-K classification.

Learning archives management in traditional systems faces challenges such as inadequate security, weak tamper resistance, and limited sharing capabilities. To address these issues, this paper proposes LRChain, a method based on consortium blockchain, for lifelong learning archives data protection and sharing. LRChain employs a combination of on-chain and off-chain cooperative storage using a consortium chain and InterPlanetary File System (IPFS) to enhance data security and availability. It also enables fine-grained verification of learning archives through selective disclosure principles, ensuring privacy protection of sensitive data. Furthermore, an attribute-based encryption algorithm is utilized to establish authorized access control for learning archives, facilitating safe and trusted sharing. Experimental evaluations and security analyses demonstrate that this method exhibits decentralization, strong security, tamper resistance, and performs well, effectively meeting the requirements for secure sharing of learning archive data.

As a fact, most constructed knowledge graphs are far from complete no matter its size. This incompleteness will cause negative influence on the applications based on knowledge graphs. As an important method for knowledge graph complementation, link prediction has become a hot research topic in recent years. In this paper, a performance enhancement scheme for link prediction models based on the idea of semi-supervised learning and model soup is proposed, which effectively improves the model performance on several mainstream link prediction models with small changes to their architecture. This novel scheme consists of two main parts: (1) predicting potential fact triples in the graph with semi-supervised learning strategies, (2) creativily combining semi-supervised learning and model soup to further improve the final model performance without adding significant computational overhead. We experimentally validate the effectiveness of the scheme for a variety of link prediction models, especially on the dataset with dense relationships. In terms of CompGCN, the model with the best overall performance among the tested models improves its Hits@1 metric by 14.7% on the FB15K-237 dataset and 7.8% on the WN18RR dataset after using the enhancement scheme. Meanwhile, we observe that the semi-supervised learning strategy in the augmentation scheme has significant improvement for multi-class link prediction models, and the performance improvement brought by the introduction of the model soup is related to the specific tested models, because performance of some models are improved while others remained largely unaffected.

An artificial rabbit optimization algorithm based on chaotic mapping and Levy flight improvement is proposed, which has the advantages of good initial population quality and fast convergence compared with the traditional artificial rabbit optimization algorithm, called CLARO. CLARO’s improvement method starts from three aspects: to optimize the quality of the initial population of the algorithm a chaotic mapping is brought in to initialize the population; to avoid the algorithm from falling into local optimum Levy flight is added in the exploration phase and the threshold of energy factor A is optimized to better balance exploration and exploitation. The efficiency of CLARO is tested on a set of 23 benchmark function sets by comparing it with ARO and different meta-heuristics algorithms. At last, the comparison experiments conclude that all three improvement strategies enhance the performance of ARO to some extent, with Levy flight providing the most significant improvement in ARO performance. The experimental results showed that CLARO has better results and faster convergence compared to other algorithms, while successfully addressing the drawbacks of ARO and being able to face more challenging problems.

In this paper, the online power control and rate adaptation for a wireless communication system with energy harvesting are investigated, in which soft decision decoding is adopted by the receiver. To efficiently utilize the harvested energy and maximize the average actual information transmission rate, transmit power, modulation order and code rate are jointly optimized. The Lyapunov framework is utilized to transform the long-term optimization problem into an optimization problem per time slot. Since there is no theoretical formula for the error rate of soft decision decoding, the optimization problem cannot be analytically solved. A table to find the optimal modulation order and code rate under the different values of signal-to-noise ratio is built first, and then a numeric algorithm to find the solution to the optimization problem is given. The feasibility and performance of the proposed algorithm are demonstrated by simulation. The simulation results show that compared with the algorithms to maximize the theoretic channel capacity, the proposed algorithm can achieve a higher actual transmission rate.

A new procedure of distributed self-control coverage for monitoring the dynamic targets with mobile sensor network is proposed. A special model is given for maintaining the nodes bi-connectivity and optimizing the coverage of the moving targets. The model consists of two parts, the virtual force model is proposed for motion control and the whale optimization algorithm is improved to further optimize the node positions and to reach the steady state quickly. The virtual resultant force stretches the network toward the uncovered targets by its multi-target attractive force, and maintains the network connectivity by its attractive force while network stretching, and avoids node collisions by its repulsive force while nodes moving. The operating mechanism of multi-target attractive force and other forces is also profoundly anatomized. The adjustment criteria for the model in different application scenarios are also given. Finally, the comparisons are shown to be significant advantages over other similar kinds.