In real-world scenarios, computing the shortest path between given source and destination is widely prevalent, such as seeking the optimal route in a road network for navigation. However, in traditional non-private solutions, the user discloses its location information to the server in order to obtain the targeted shortest path, giving rise to a significant privacy leakage problem. Existing private shortest path computation schemes either provide limited privacy guarantees or require multiple interactions between the user and the server. In this paper, we introduce a fully private shortest path computation scheme, named Srchpa. This scheme ensures full privacy for both the location information provided by the user and the routing information held by the server. Furthermore, we propose a locally iterative computation method, achieving single-round interaction between the user and the server to obtain the targeted shortest path. Finally, we present the formal security analyses and comprehensive experiment evaluations. The security analyses demonstrate that our scheme achieves full privacy even if the server is malicious. The experiment evaluation results show that our scheme has lower computation, communication and storage costs on the user side, thus is practical for the lightweight user with limited resources.

Large-scale distributed communication networks have been widely recognized as the backbone for efficient data dissemination in cloud and IoT systems, serving as the foundation for dynamic routing, load balancing, and synchronization. In the specific scenario of real-time routing, traditional static approaches fail to adapt to variable communication delays, congestion, and node failures, resulting in high average end-to-end delay and suboptimal performance. In this paper, we propose the adaptive routing and fault tolerance protocol (ARFTP), a unified framework that leverages adaptive Susceptible-Infectious-Recovered (SIR) model applications and dynamic gossip algorithms to minimize the average end-to-end delay while ensuring load balancing and fault tolerance in large-scale distributed networks. Our approach employs an adaptive weight mechanism that continuously updates routing decisions based on real-time congestion and delay feedback, integrating recursive load feedback and a backup routing strategy to achieve efficient information dissemination. Experimental results demonstrate that the ARFTP reduces the average end-to-end delay to 0.15 s, increases throughput to 0.92, and significantly improves load balancing and fault tolerance compared to conventional static routing methods.

The continuous development of mobile terminal applications, online maps, and other navigation services have become widely used, simultaneously giving rise to significant security risks. To address the issues of privacy leakage and low efficiency in traditional graph shortest path retrieval schemes, an efficient privacy-preserving graph shortest path retrieval scheme is proposed, called EP-GSPR. Specifically, this scheme addresses the privacy security problems in the existing graph shortest path retrieval solutions by ensuring the bilateral privacy protection of the user's query location and the database privacy of the cloud server. Throughout the retrieval process, the cloud server cannot obtain the user's location information, and the user cannot access any database information other than the retrieval results. To overcome the performance bottlenecks in existing schemes, a progressive iterative retrieval framework is designed as the fundamental modular, called Pirf, achieving sub-linear retrieval costs and low storage overhead on the cloud server side. Finally, the security analyses demonstrate the EP-GSPR scheme achieves the bilateral privacy-preserving in terms of user and server sides. The comprehensive experiment evaluations also state the efficiency and practicality of the proposed scheme

The graph-based embedding is an effective and useful method in reducing the dimension and extracting relevant data. This paper introduces a framework for classifying high dimensional data via a joint graph-based embedding and weighting method which could be used in semi-supervised or supervised learning. We design on effective optimization algorithm to solve the objective function. Experiments on image classification show that our proposed method can have a performance that is better than that of many state-of-the-art methods including linear and nonlinear methods.

This paper introduces a robust and flexible graph-based semi-supervised embedding method for generic classification and recognition tasks. It combines the merits of sparsity preserving projections, margin maximization, and robust loss function. The latter reduces the effect of outliers on the regression model needed for mapping unseen examples. Furthermore, unlike label propagation semi-supervised schemes, our proposed method is a data embedding into a space whose dimension is not limited to the number of classes. The used robust norm combines the merits of matrix l1,2 and l2 norms. It is suited for the Laplacian distribution of outliers and the Gaussian distribution of samples with small losses. We provide experiments on four benchmark image datasets in order to study the performance of the proposed method. These experiments show that the proposed methods can be more discriminative than other state-of-the-art methods.

In the past years, various graph-based data embedding algorithms were proposed and used in machine learning and pattern recognition fields. This paper introduces a graph-based non-linear embedding learning algorithm for image classification and recognition. The proposed embedding method can be used for supervised and semi-supervised learning settings. The proposed criterion allows the simultaneous estimation of a linear and a non-linear embedding. It integrates manifold smoothness, Sparse Regression and Margin Discriminant Embedding. The deployed sparse regression implicitly performs feature selection on the original features of the data matrix and of the linear transform. The proposed method is applied to four image datasets: 8 Sports Event Categories dataset, Scene 15 dataset, ORL Face dataset and COIL-20 Object dataset. The experiments demonstrate the effectiveness of the proposed embedding method.