Deep-Learning-as-a-service (DLaaS) has received increasing attention due to its novelty as a diagram for deploying deep learning techniques. However, DLaaS faces performance and security issues that urgently need to be addressed. Given the limited computation resources and concern of benefits, Quality-of-Service (QoS) metrics should be revised to optimize the performance and reliability of distributed DLaaS systems. New users and services dynamically and continuously join and leave such a system, resulting in cold start issues, and additionally, the increasing demand for robust network connections requires the model to evaluate the uncertainty. To address such performance problems, we propose in this article a deep learning-based model called embedding enhanced probability neural network, in which information is extracted from inside the graph structure and then estimated the mean and variance values for the prediction distribution. The adversarial attack is a severe threat to model security under DLaaS. Due to such, the service recommender system's vulnerability is tackled, and adversarial training with uncertainty-aware loss to protect the model in noisy and adversarial environments is investigated and proposed. Extensive experiments on a large-scale real-world QoS dataset are conducted, and comprehensive analysis verifies the robustness and effectiveness of the proposed model.

To select appropriate web services for users, the Quality-of-Service (QoS) based collaborative prediction models are widely used. Despite the success of collaborative prediction models in selecting appropriate web services for users, existing models do not take into account the users' authority to manage their own generated data as stipulated by many privacy-preserving regulations, such as the General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA). Moreover, unlearning is urgently needed due to the security concerns such as data poisoning attacks. Existing QoS prediction methods are not optimized for unlearning, suffering from low model availability when handling unlearning requests by full re-training. To address this problem, we propose QoSEraser: a novel efficient machine unlearning framework for QoS prediction tasks. The central concepts of the QoSEraser involve: (1) dividing the training data into multiple shards to train submodels according to the cluster results on graph embeddings induced by random walk on contextual information graph. Such division ensures the preservation of collaborative signals in collected QoS records; (2) a concatenate aggregation method and a stacking & attention-based aggregation method are used to condense information in fragmented embeddings to a uniform one adaptively. Experiments on large-scale datasets show that QoSEraser achieves efficient forgetting learning and outperforms state-of-the-art unlearning approaches in terms of performance. Source codes are available at https://github.com/ZengYuXiang7/QoSEraser.

Quality-of-Service (QoS) based collaborative prediction models are emerging to select appropriate edge cloud services for users. Nevertheless, there are still challenges in the realworld QoS prediction task. First, existing QoS prediction models are mostly transductive, failing to generalize to unseen users and services. Secondly, an accurate prediction model remains unexplored under the extreme sparse data scenario, where only a few interactions are available for collaborative filtering. To address these problems, we propose -Inductive -Subgraph -Pattern -Aware Graph Neural Network (ISPA-GNN), which leverages a novel graph-based collaborative filtering method with a subgraph sampling strategy. We further optimize the embeddings components, replacing the user/service embeddings with compositional context information to enable better generalization to unseen nodes while reducing memory usage. Extensive experiments on a large-scale real-world service QoS dataset demonstrate some decent properties of our model, including high prediction accuracy, memory efficiency, and slight performance degradation even if 25% of users/services are never seen.

Selecting suitable web services based on the quality-of-service (QoS) is essential for developing high-quality service-oriented applications. A critical step in this direction is acquiring accurate, personalised QoS values of web services. As the number of web services is enormous and the QoS data are highly sparse, improving the accuracy of QoS prediction has become a challenging issue recently. In this study, we propose a novel QoS prediction model, called neural fusion matrix factorisation, wherein we combine neural networks and matrix factorisation to perform non-linear collaborative filtering for latent feature vectors of users and services. Moreover, we consider context bias and employ multi-task learning to reduce prediction error and improve the predicted performance. Furthermore, we conducted extensive experiments in a large-scale real-world QoS dataset, and the experimental results verify the effectiveness of our proposed method.

The previous blockchain data transmission techniques in industrial Internet of Things (IoT) have low security, high management cost of the trading center, and big difficulty in supervision. To address these issues, this paper proposes a secure FaBric blockchain-based data transmission technique for industrial IoT. This technique uses the blockchain-based dynamic secret sharing mechanism. A reliable trading center is realized using the power blockchain sharing model, which can also share power trading books. The power data consensus mechanism and dynamic linked storage are designed to realize the secure matching of the power data transmission. Experiments show that the optimized FaBric power data storage and transmission has high security and reliability. The proposed technique can improve the transmission rate and packet receiving rate by 12% and 13%, respectively. Moreover, the proposed technique has good superiority in sharing management and decentralization.

With the fast development of Web services in service-oriented systems, the requirement of efficient Quality of Service (QoS) evaluation methods becomes strong. However, many QoS values are unknown in reality. Therefore, it is necessary to predict the unknown QoS values of Web services based on the obtainable QoS values. Generally, the QoS values of similar users are employed to make predictions for the current user. However, the QoS values may be contributed from unreliable users, leading to inaccuracy of the prediction results. To address this problem, we present a highly credible approach, called reputation-based Matrix Factorization (RMF), for predicting the unknown Web service QoS values. RMF first calculates the reputation of each user based on their contributed QoS values to quantify the credibility of users, and then takes the users' reputation into consideration for achieving more accurate QoS prediction. Reputation-based matrix factorization is applicable to the prediction of QoS data in the presence of unreliable user-provided QoS values. Extensive experiments are conducted with real-world Web service QoS data sets, and the experimental results show that our proposed approach outperforms other existing approaches.

Web service recommendation is of great importance when users face a large number of functionally-equivalent candidate services. To recommend Web services that best fit a user's need, QoS values which characterize the non-functional properties of those candidate services are in demand. But in reality, the QoS information of Web service is not easy to obtain, because only limited historical invocation records exist. To tackle this challenge, in recent literature, a number of QoS prediction methods are proposed, but they still demonstrate disadvantages on prediction accuracy. In this paper, we design a location-based hierarchical matrix factorization (HMF) method to perform personalized QoS prediction, whereby effective service recommendation can be made. We cluster users and services into several user-service groups based on their location information, each of which contains a small set of users and services. To better characterize the QoS data, our HMF model is trained in a hierarchical way by using the global QoS matrix as well as several location-based local QoS matrices generated from user-service clusters. Then the missing QoS values can be predicted by compactly combining the results from local matrix factorization and global matrix factorization. Comprehensive experiments are conducted on a real-world Web service QoS dataset with 1,974,675 real Web service invocation records. The experimental results show that our HMF method achieves higher prediction accuracy than the state-of-the-art methods.