Virtualized Network Function (VNF) and Service Function Chain (SFC) are the fundamental components in Network Functions Virtualization (NFV) infrastructure, which supports the evolution of modern 5G networks. For online Internet of Things (IoT) applications, characterized by dynamic and diverse requirements, achieving optimal quality of service hinges on a resource-efficient yet performant SFC caching strategy, which is a critical challenge. Besides, despite the performance boost and flexibility brought by modern cloud-native technology, it brings the cold-start problem due to the requirement for runtime image transmission and booting-up, resulting in a non-negligible launch latency. To tackle these challenges, this paper proposes CPSC (Cloud-Native Parallel SFC Caching framework), a novel approach to address the cloud-native parallel SFC caching problem in edge-cloud networks leveraging Deep Reinforcement Learning (DRL), seeking an efficient resource utilization of the edge-cloud network with consideration of SFC processing performance and cold-start suppressing. Graph Convolutional Network (GCN) -based embeddings are adopted for topology-aware feature extraction of the substrate edge-cloud network as well as the incoming SFC caching requests. Then, a Pointer Network (PN) is utilized for contextual information-aware caching decision-making. Benefiting from the online capability of DRL, CPSC makes caching decisions in an online manner with no prior knowledge requirement on future incoming requests. Extensive simulations show that CPSC manages to outperform the state-of-the-art approaches in edge network acceptance ratio and launch latency, with minimal overhead on the SFC processing performance and decision-making duration.

With the wide use of Location-Based Social Networks (LBSNs), predicting user friendship from online social relations and offline trajectory data is of great value to improve the platform service quality and user satisfaction. Existing methods mainly focus on some hand-crafted features or graph embedding models based on the user-location bipartite graph, which cannot precisely capture the latent mobility similarity for the majority of users who have no explicit co-visit behaviors and also fail to balance the tradeoff between social features and mobility features for friendship prediction. In this regard, we propose a dual subgraph-based pairwise graph neural network (DSGNN) for friendship prediction in LBSNs, which extracts a pairwise social subgraph and a trajectory subgraph to model the social proximity and mobility similarity, respectively. Specifically, to overcome the co-visit data sparsity, we design an entropy-based random walk to construct a location graph that captures the high-level correlation between locations. Based on this, we characterize the pairwise mobility similarity from trajectory level instead of location level, which is modeled by a graph neural network (GNN) on a labeled trajectory subgraph composed of the two trajectories of the target user pair. Besides, we also utilize another GNN to extract social proximity based on social subgraph of the target user pair. Finally, we propose a gate layer to adaptively balance the fusion of the social and mobility features for friendship prediction. We conduct extensive experiments on the real-world datasets and demonstrate the superiority of our approach, which outperforms other state-of-the-art methods. In particular, the comparative experiments on the trajectory level mobility similarity further validate the effectiveness of the designed trajectory subgraph-based method, which can extract predictive mobility features.

Pervasive smartphones boost the prosperity of location-based service (LBS) and the increasing data prompt LBS providers to outsource their LBS datasets to the cloud side. The privacy issues of LBS in the outsourced cloud scenario have attracted considerable interest recently. However, current schemes cannot provide sufficient privacy preservation against practical challenges and are little concerned about the data retrieval efficiency of the cloud side. Therefore, we present an efficient Privacy-Preserving LBS Query Scheme (i.e., PPQS ). In our scheme, two cloud entities are employed to store the sensitive information of the outsourced data and provide the query service, which enhances the ability of privacy preservation for sensitive information. Besides, by using the techniques of homomorphic encryption and searchable symmetric encryption, the proposed scheme supports both the type query and the range query, which can significantly improve the data retrieval efficiency of the cloud side and reduce the computation burden on the cloud side and the user side. Through detailed analysis on security and computation cost, we show the enhanced ability of privacy preservation and the lower computation cost compared to previous schemes. Based on a real dataset, extensive simulations are performed to validate the effectiveness and performance of our scheme.

As training high-performance object detectors requires expensive bounding box annotations, recent methods resort to free-available image captions. However, detectors trained on caption supervision perform poorly because captions are usually noisy and cannot provide precise location information. To tackle this issue, we present a visual analysis method, which tightly integrates caption supervision with object detection to mutually enhance each other. In particular, object labels are first extracted from captions, which are utilized to train the detectors. Then, the objects detected from images are fed into caption supervision for further improvement. To effectively loop users into the object detection process, a node-link-based set visualization supported by a multi-type relational co-clustering algorithm is developed to explain the relationships between the extracted labels and the images with detected objects. The co-clustering algorithm clusters labels and images simultaneously by utilizing both their representations and their relationships. Quantitative evaluations and a case study are conducted to demonstrate the efficiency and effectiveness of the developed method in improving the performance of object detectors.

As a review system, the Crowd-Sourced Local Businesses Service System (CSLBSS) allows users to publicly publish reviews for businesses that include display name, avatar, and review content. While these reviews can maintain the business reputation and provide valuable references for others, the adversary also can legitimately obtain the user’s display name and a large number of historical reviews. For this problem, we show that the adversary can launch connecting user identities attack (CUIA) and statistical inference attack (SIA) to obtain user privacy by exploiting the acquired display names and historical reviews. However, the existing methods based on anonymity and suppressing reviews cannot resist these two attacks. Also, suppressing reviews may result in some reiews with the higher usefulness not being published. To solve these problems, we propose a cross-platform strong privacy protection mechanism (CSPPM) based on the partial publication and the complete anonymity mechanism. In CSPPM, based on the consistency between the user score and the business score, we propose a partial publication mechanism to publish reviews with the higher usefulness of review and filter false or untrue reviews. It ensures that our mechanism does not suppress reviews with the higher usefulness of reviews and improves system utility. We also propose a complete anonymity mechanism to anonymize the display name and avatars of reviews that are publicly published. It ensures that the adversary cannot obtain user privacy through CUIA and SIA. Finally, we evaluate CSPPM from both theoretical and experimental aspects. The results show that it can resist CUIA and SIA and improve system utility.

Known as a distributed ledger, blockchain is becoming prevalent due to its decentralization, traceability and tamper resistance. Particularly, permissioned blockchain such as Hyperledger Fabric shows great application prospects as the infrastructure of IoT security, credit management, etc. Many cloud platforms like AWS, Azure, Oracle and IBM cloud currently provide blockchain as a service, in which tenants can quickly build permissioned blockchain and run smart contract based applications. However, the transactions throughput and scalability in the permissioned blockchain are not ideal, despite many optimization efforts in consensus protocol and parallel chain. Existing solutions still reveals some limitations like excessive CPU scheduling, inefficient block broadcast and high latency of initial blocks synchronization when new nodes join blockchain network. Inspired by the emerging RDMA (Remote Direct Memory Access) network, we propose BoR, an RDMA-based permissioned blockchain framework. By offloading the block transfer transaction into RDMA NICs, it can increase block broadcast speed and reduce block sync delay. We exploit the RDMA primitives to redesign the block synchronization protocol and accelerate DPoS (Delegated Proof of Stake) consensus process for higher throughput and lower latency in kernel-bypass manner. As demonstrated in our evaluation with different workloads, BoR with lower CPU utilization significantly outperforms the state-of-the-art EoS blockchain.