Debugging a traditional network is notoriously difficult due to network devices' heterogeneity and protocols' decentralized nature, but Software-Defined Networking (SDN) is changing this predicament. Recent works have provided very nice approaches for an administrator to perform several fundamental network tests in a single-domain SDN network. However, how to perform these tests securely in multi-domain networks still remains open. In this paper, we study the highly challenging problem of inter-domain forwarding loop test in a SDN environment. We present two novel testing protocols that can be used for inter-domain loop tests. Both protocols are secure in the sense that they protect each domain's private information about its topology and configuration. The first protocol, based on random sampling, is highly efficient with a small error probability diminishing exponentially in the sample size. The second protocol, based on secure set intersection test, guarantees 100 percent accuracy of the result, although not as efficient as the first one. We provide rigorous proofs for the security and accuracy guarantees, and show our protocols have very good efficiency by testing them with real-world network data.

The recent proliferation of human-carried mobile devices has given rise to the crowd sensing systems. However, the sensory data provided by individual participants are usually not reliable. To better utilize such sensory data, the topic of truth discovery, whose goal is to estimate user quality and infer reliable aggregated results through quality-aware data aggregation, has drawn significant attention. Though able to improve aggregation accuracy, existing truth discovery approaches fail to address the privacy concerns of individual users. In this article, we propose a novel privacy-preserving truth discovery (PPTD) framework, which can protect not only users’ sensory data but also their reliability scores derived by the truth discovery approaches. The key idea of the proposed framework is to perform weighted aggregation on users’ encrypted data using a homomorphic cryptosystem, which can guarantee both high accuracy and strong privacy protection. In order to deal with large-scale data, we also propose to parallelize PPTD with MapReduce framework. Additionally, we design an incremental PPTD scheme for the scenarios where the sensory data are collected in a streaming manner. Extensive experiments based on two real-world crowd sensing systems demonstrate that the proposed framework can generate accurate aggregated results while protecting users’ private information.

Networks, which characterize object relationships, are ubiquitous in various domains. One very important problem is to detect the nodes of a specific function in these networks. For example, is a user normal or anomalous in an email network? Does a protein play a key role in a protein-protein interaction network? In many applications, the information we have about the networks usually includes both node characteristics and network structures. Both types of information can contribute to the task of learning functional nodes, and we call the collection of node and link information as linked data. However, existing methods only use a few subjectively selected topological features from network structures to detect functional nodes, thus fail to include highly discriminative and meaningful patterns hidden in linked data. To address this problem, a novel Feature Integration based Functional Node Detection (FIND) algorithm is presented. Specifically, FIND extracts the most discriminative information from both node characteristics and network structures in the form of a unified latent feature representation with the guidance of several labeled nodes. Experiments on two real world data sets validate that the proposed method significantly outperforms the baselines on the detection of three different types of functional nodes.

The recent proliferation of human-carried mobile devices has given rise to the crowd sensing systems. However, the sensory data provided by individual participants are usually not reliable. To identify truthful values from the crowd sensing data, the topic of truth discovery, whose goal is to estimate user quality and infer truths through quality-aware data aggregation, has drawn significant attention. Though able to improve aggregation accuracy, existing truth discovery approaches fail to take into consideration an important issue in their design, i.e., the protection of individual users' private information. In this paper, we propose a novel cloud-enabled privacy-preserving truth discovery (PPTD) framework for crowd sensing systems, which can achieve the protection of not only users' sensory data but also their reliability scores derived by the truth discovery approaches. The key idea of the proposed framework is to perform weighted aggregation on users' encrypted data using homomorphic cryptosystem. In order to deal with large-scale data, we also propose to parallelize PPTD with MapReduce framework. Through extensive experiments on not only synthetic data but also real world crowd sensing systems, we justify the guarantee of strong privacy and high accuracy of our proposed framework.

Informative gene selection is an important topic in the field of bioinformatics which has attracted intensive interest in recent years. It aims to identify the genes which are differentially expressed in different groups, and thus are informative for the classification between the groups. For this purpose, many micro array experiments have been conducted by various medical institutes on their own sets of patients and test subjects. For those institutes who have conducted experiments regarding the same type of disease, it would be beneficial to all of them if they learn on the union of their data to find the informative genes instead of learn just on their own datasets, since the amount of data each institute holds is very limited. However, in many cases, the institutes are not allowed to share their data with others because micro array datasets contain private information about the patients and test subjects. In this paper, we focus on this problem and propose a privacy preserving algorithm that allows multiple parties to perform the widely used informative gene selection method, the Fisher criterion, on the union of their data, without revealing each party's data to others. Basically, we utilize the homomorphic cryptographic system to protect the data during the calculations. Experimental results on real world datasets show the effectiveness of the proposed method.

Linked data consist of both node attributes, e.g., Preferences, posts and degrees, and links which describe the connections between nodes. They have been widely used to represent various network systems, such as social networks, biological networks and etc. Knowledge discovery on linked data is of great importance to many real applications. One of the major challenges of learning linked data is how to effectively and efficiently extract useful information from both node attributes and links in linked data. Current studies on this topic either use selected topological statistics to represent network structures, or linearly map node attributes and network structures to a shared latent feature space. However, while approaches based on statistics may miss critical patterns in network structure, approaches based on linear mappings may not be sufficient to capture the non-linear characteristics of nodes and links. To handle the challenge, we propose, to our knowledge, the first deep learning method to learn from linked data. A restricted Boltzmann machine model named LRBM is developed for representation learning on linked data. In LRBM, we aim to extract the latent feature representation of each node from both node attributes and network structures, non-linearly map each pair of nodes to the links, and use hidden units to control the mapping. The details of how to adapt LRBM for link prediction and node classification on linked data have also been presented. In the experiments, we test the performance of LRBM as well as other baselines on link prediction and node classification. Overall, the extensive experimental evaluations confirm the effectiveness of the proposed LRBM model in mining linked data.

Dynamic networks are structures with objects and links between the objects that vary in time. Temporal information in dynamic networks can be used to reveal many important phenomena such as bursts of activities in social networks and human communication patterns in email networks. In this area, one very important problem is to understand dynamic patterns of object roles. For instance, will a user become a peripheral node in a social network? Could a website become a hub on the Internet? Will a gene be highly expressed in gene-gene interaction networks in the later stage of a cancer? In this paper, we propose a novel approach that identifies the role of each object, tracks the changes of object roles over time, and predicts the evolving patterns of the object roles in dynamic networks. In particular, a probability model is proposed to extract latent features of object roles from dynamic networks. The extracted latent features are discriminative in learning object roles and are capable of characterizing network structures. The probability model is then extended to learn the dynamic patterns and make predictions on object roles. We assess our method on two data sets on the tasks of exploring how users' importance and political interests evolve as time progresses on dynamic networks. Overall, the extensive experimental evaluations confirm the effectiveness of our approach for identifying, analyzing and predicting object roles on dynamic networks.