Search

As recommendation systems continue to evolve, researchers are using trust data to improve the accuracy of recommendation prediction and help users find relevant information. However, large recommendation systems with trust data suffer from the sparse trust problem, which leads to grade inflation and severely affects the reliability of trust propagation. This paper presents a novel research on sparse trust data mining, which includes the new concept of sparse trust, a sparse trust model, and a trust mining framework. It lays a foundation for the trust-related research in large recommended systems. The new trust mining framework is based on customized normalization functions and a novel transitive gossip trust model, which discovers potential trust information between entities in a large-scale user network and applies it to a recommendation system. We conducts a comprehensive performance evaluation on both real-world and synthetic datasets. The results confirm that our framework mines new trust and effectively ameliorates sparse trust problem.

Mobile social applications have been widely used by Internet users. Users can efficiently acquire many kinds of information and share their statuses by various social platforms. However, when a user intends to share information through the user's social applications, the user can set the access permission only before the information is posted. Once the information is posted, it is completely beyond the user's control. Specifically, during the recommendation process (for friend or information) in social applications, a user cannot control who can get the recommendation and access his/her information. If one user is accessed by another malicious user, his/her privacy information can be disclosed, and even be further misused for malicious attacks. In this article, we propose Trust2Privacy, a trust-based access control mechanism to protect the personalized privacy of users after posting their information, which can effectively realize the transformation from trust to privacy. First, to represent the relations accurately among users, we define the direction of trust among users according to the user-follow status. Then we combine the similarity, correlation, and interaction among users to calculate the trust values. Considering the fuzzy relationship between the multi-dimensional features and trust levels, we propose a fuzzy comprehensive evaluation algorithm to compute the fuzzy trust. Moreover, for the sake of the high mobility of mobile social networks, we exploit so-called online-to-offline trust evidence to derive the trust value, including taking the location information (e.g., distance, semantics) into consideration. To meet the personalized privacy requirement, we design a filtering algorithm based on the trust relationships and the privacy policy of the target users or posted information, according to which the access requesters can get the list of accessible users or information. This enables achieving the goal of personalized privacy protection. The theoretical analysis and simulation experiment demonstrate that Trust2Privacy is able to achieve personalized privacy protection without bringing negative impact on the availability and usability of mobile social applications.

The convergence of AI and IoT enables data to be quickly explored and turned into vital decisions, and however, there are still some challenging issues to be further addressed. For example, lacking of enough data in AI-based decision making (so called Sparse Decision Making, SDM) will decrease the efficiency\ud dramatically, or even disable the intelligent IoT networks. Taking the intelligent IoT networks as the network infrastructure, the recommendation systems have been facing such SDM problems. A naive solution is to introduce so-called trust information. However, trust information also maybe face the difficulty of sparse trust evidence (a.k.a sparse trust problem). In our work, an accurate sparse decision making model with two-way trust recommendation in the AI enabled IoT systems is proposed by us, named TT-SVD. Our model incorporates both trust information and rating information more completely, which can efficiently alleviate the above mentioned sparse trust problem and therefore be able to solve the cold start and data sparsity problems. Specifically, we first consider the two-fold trust influences from both trustees and trustors, which can be represented by a factor called trust propensity. To this end, we propose a dual model, including the trustor model (TrustorSVD) and a trustee model (TrusteeSVD) based on an existing rating-only recommendation model called SVD++, which are integrated by the weighted average and yield the final model, TT-SVD. The experimental results show that our model outperforms the state of the art including SVD and TrustSVD in both the ”all users” and ”cold start users” cases, and the accuracy improvement can reach a maximum of 29%. Complexity analysis shows that our model is equally suitable for the case of large sparse datasets. In a word, our model can effectively solve the sparse decision problem by introducing the two-way trust recommendation, and hence improve the efficiency of the intelligent recommendation systems.

Distributed multi-controller deployment is a key issue in the innovative Software Defined Network (SDN) to scale network while improving performance and reliability. It is interesting to know how many controllers should be deployed and where to locate under a wide range of performance sensitive and completive constraints, including latency, fair load distribution as well as cost. We solve this problem by minimizing propagation latency and controller cost. The required number of controllers is determined based on requests and controller capacity. Due to the uneven distribution of network load, it is more likely to deploy controllers on nodes with high request density. A clustering algorithm NWDP (Node Weight Deployment Policy) is thus proposed based on node weight to choose location of multi-controller. To achieve effectively, autonomous and dynamic deployment in large-scale networks, we further propose a supervised graph convolution network model with fusion features(FF-GCN). The open network database Internet Topology Zoo is adopted to evaluate the effectiveness of our algorithms. Simulation results show that NWDP efficiently outperforms traditional algorithms in medium-sized topology, and the trained FF-GCN can figure out the deployment in a 702 nodes large-scale topology with an average prediction accuracy of 90%.

We study server consolidation problem in clouds under simultaneous failures of multiple servers, where consolidation means that cloud providers put tenants on shared servers to improve resource utilization and thus reduce operation and maintenance costs. With replicas of each tenant put on multiple servers, our objective is to minimize the total number of opened servers and ensure that a particular failure will not result in overload on any remaining server. In this paper, we propose Rotation algorithm. It packs comparable sizes replicas into the same type of servers and adopts a cyclic shift method to quickly reuse those already-opened servers without the need of new ones for new tenants. Through experimental evaluations, we show that the proposed algorithms can achieve a better performance than existing works and produce near-optimal replications allocation.

With the extensive application of artificial intelligence technology in 5G and Beyond Fifth Generation (B5G) networks, it has become a common trend for artificial intelligence to integrate into modern communication networks. Deep learning is a subset of machine learning and has recently led to significant improvements in many fields. In particular, many 5G-based services use deep learning technology to provide better services. Although deep learning is powerful, it is still vulnerable when faced with 5G-based deep learning services. Because of the nonlinearity of deep learning algorithms, slight perturbation input by the attacker will result in big changes in the output. Although many researchers have proposed methods against adversarial attacks, these methods are not always effective against powerful attacks such as CW. In this paper, we propose a new two-stream network which includes RGB stream and spatial rich model (SRM) noise stream to discover the difference between adversarial examples and clean examples. The RGB stream uses raw data to capture subtle differences in adversarial samples. The SRM noise stream uses the SRM filters to get noise features. We regard the noise features as additional evidence for adversarial detection. Then, we adopt bilinear pooling to fuse the RGB features and the SRM features. Finally, the final features are input into the decision network to decide whether the image is adversarial or not. Experimental results show that our proposed method can accurately detect adversarial examples. Even with powerful attacks, we can still achieve a detection rate of 91.3%. Moreover, our method has good transferability to generalize to other adversaries.