Privacy-preserving distributed service recommendation based on locality-sensitive hashing

With the advent of IoT (Internet of Things) age, considerable web services are emerging rapidly in service communities, which places a heavy burden on the target users’ service selection decisions. In this situation, various techniques, e.g., collaborative filtering (i.e., CF) is introduced in service recommendation to alleviate the service selection burden. However, traditional CF-based service recommendation approaches often assume that the historical user-service quality data is centralized, while neglect the distributed recommendation situation. Generally, distributed service recommendation involves inevitable message communication among different parties and hence, brings challenging efficiency and privacy concerns. In view of this challenge, a novel privacy-preserving distributed service recommendation approach based on Locality-Sensitive Hashing (LSH), i.e., DistSRLSH is put forward in this paper. Through LSH, DistSRLSH can achieve a good tradeoff among service recommendation accuracy, privacy-preservation and efficiency in distributed environment. Finally, through a set of experiments deployed on WS-DREAM dataset, we validate the feasibility of our proposal in handling distributed service recommendation problems

OptIForest: Optimal Isolation Forest for Anomaly Detection

Anomaly detection plays an increasingly important role in various fields for critical tasks such as intrusion detection in cybersecurity, financial risk detection, and human health monitoring. A variety of anomaly detection methods have been proposed, and a category based on the isolation forest mechanism stands out due to its simplicity, effectiveness, and efficiency, e.g., iForest is often employed as a state-of-the-art detector for real deployment. While the majority of isolation forests use the binary structure, a framework LSHiForest has demonstrated that the multi-fork isolation tree structure can lead to better detection performance. However, there is no theoretical work answering the fundamentally and practically important question on the optimal tree structure for an isolation forest with respect to the branching factor. In this paper, we establish a theory on isolation efficiency to answer the question and determine the optimal branching factor for an isolation tree. Based on the theoretical underpinning, we design a practical optimal isolation forest OptIForest incorporating clustering based learning to hash which enables more information to be learned from data for better isolation quality. The rationale of our approach relies on a better bias-variance trade-off achieved by bias reduction in OptIForest. Extensive experiments on a series of benchmarking datasets for comparative and ablation studies demonstrate that our approach can efficiently and robustly achieve better detection performance in general than the state-of-the-arts including the deep learning based methods.Comment: This paper has been accepted by International Joint Conference on Artificial Intelligence (IJCAI-23

An IoT-Oriented Offloading Method with Privacy Preservation for Cloudlet-Enabled Wireless Metropolitan Area Networks

With the development of the Internet of Things (IoT) technology, a vast amount of the IoT data is generated by mobile applications from mobile devices. Cloudlets provide a paradigm that allows the mobile applications and the generated IoT data to be offloaded from the mobile devices to the cloudlets for processing and storage through the access points (APs) in the Wireless Metropolitan Area Networks (WMANs). Since most of the IoT data is relevant to personal privacy, it is necessary to pay attention to data transmission security. However, it is still a challenge to realize the goal of optimizing the data transmission time, energy consumption and resource utilization with the privacy preservation considered for the cloudlet-enabled WMAN. In this paper, an IoT-oriented offloading method, named IOM, with privacy preservation is proposed to solve this problem. The task-offloading strategy with privacy preservation in WMANs is analyzed and modeled as a constrained multi-objective optimization problem. Then, the Dijkstra algorithm is employed to evaluate the shortest path between APs in WMANs, and the nondominated sorting differential evolution algorithm (NSDE) is adopted to optimize the proposed multi-objective problem. Finally, the experimental results demonstrate that the proposed method is both effective and efficient

The Design of 3D-Printed Lattice-Reinforced Thickness-Varying Shell Molds for Castings

3D printing technologies have been used gradually for the fabrication of sand molds and cores for castings, even though these molds and cores are dense structures. In this paper, a generation method for lattice-reinforced thickness-varying shell molds is proposed and presented. The first step is the discretization of the STL (Stereo Lithography) model of a casting into finite difference meshes. After this, a shell is formed by surrounding the casting with varying thickness, which is roughly proportional to the surface temperature distribution of the casting that is acquired by virtually cooling it in the environment. A regular lattice is subsequently constructed to support the shell. The outside surface of the shell and lattice in the cubic mesh format is then converted to STL format to serve as the external surface of the new shell mold. The internal surface of the new mold is the casting’s surface with the normals of all of the triangles in STL format reversed. Experimental verification was performed on an Al alloy wheel hub casting. Its lattice-reinforced thickness-varying shell mold was generated by the proposed method and fabricated by the binder jetting 3D printing. The poured wheel hub casting was sound and of good surface smoothness. The cooling rate of the wheel hub casting was greatly increased due to the shell mold structure. This lattice-reinforced thickness-varying shell mold generation method is of great significance for mold design for castings to achieve cooling control