Eric Ke Wang

Security Issues and Challenges for Cyber Physical System

In this paper, we investigate the security challenges and issues of cyber-physical systems. (1)We abstract the general workflow of cyber physical systems, (2)identify the possible vulnerabilities, attack issues, adversaries characteristics and a set of challenges that need to be addressed, (3)then we also propose a context-aware security framework for general cyber-physical systems and suggest some potential research areas and problems.

Location-Based Distributed Group Key Agreement Scheme for Vehicular Ad Hoc Network

With the rapid development of vehicular ad hoc networks (VANETs), security issues are concerned. Among various security measures, secure group key agreement for VANETs attracts more and more attention. Researchers have studied generic group key agreement schemes for a long while; however, most of them are not designed for VANETs. Considering energy-constrained and fast moving features in vehicular ad hoc network, we propose a new location-based distributed group key agreement LDGKA scheme for VANETs. We adopt a hybrid approach in which members in the vehicular ad hoc network form various logic groups in the same region. Within each group, virtual key tree model is employed so that the rekeying operation can be efficiently carried out when members leave or join. It only requires to rebuild O ( log ⁡ ( n ) ) keys when one node leaves or joins. Moreover, we adopt one-way key derivation to rebuild keys in order to reduce communication overhead of distributing new keys. We also design a protocol for establishing the secure temporary channel dynamically for the nodes that are located in different regions.

An Efficient and Secure Key Establishment Scheme for Wireless Sensor Network

Traditional key management techniques, such as public key cryptography or key distribution center (e.g., Kerberos), are often not effective for wireless sensor networks for the serious limitations in terms of computational power, energy supply, network bandwidth and defection of centra authority. In order to balance the security and efficiency, we propose a new scheme by employing LU Composition techniques for mutualauthenticated pairwise key establishment and integrating LU Matrix with Elliptic Curve Diffie-Hellman for anonymous path key establishment. At the meantime, it can achieve efficient group key agreement and management by tree-based extension of LU Matrix Composition key exchange protocol(TGLU). Theoretical analysis shows that the new scheme has better performance and provides authenticity and anonymity for sensor to establish multiple kinds of keys, compared with previous related works.

A New Privacy-Preserving Scheme for Continuous Query in Location-Based Social Networking Services

With continuous queries that are used widely in location based mobile social networking services, how to protect the location privacy effectively for continuous query has been a hot topic for researchers. In this paper, we analyze the existing location privacy protection systems and algorithms for location based services; considering their disadvantages of slow responding time and high anonymization costs, we propose a new enhanced greedy cloaking algorithm which predicts a cloaking area at the initial query to be the cloaking region in the whole query lifetime by the comprehensive computation of privacy monitor, quality monitor, and dynamic adjuster. Privacy monitor and quality monitor charge the privacy protection level and service quality degree respectively; dynamic adjuster can adjust the cycle center point dynamically. We employ cycle as cloaking region form which can effectively alleviate the computation overhead. And we compare it with the earlier algorithm on three aspects. The experimental result shows that the enhanced greedy cloaking algorithm is better than the original greedy algorithm on average responding time or anonymization cost.

Towards Time-Bound Hierarchical Key Management in Cloud Computing

Nowadays, data outsourcing in the cloud is used widely and popularly by people. It also arises several security problems. To control access of outsourced data with different priority becomes an important research issue. Recently, Chen et al. proposed the first hierarchical access control scheme in cloud computing. However, they did not concern with the time-bound property. In some applications such as Pay-TV, the time-bound property is necessary because subscriber may subscribe some channels during one month. In this paper, we propose the first time-bound hierarchical key management scheme in cloud computing without tamper-resistant devices. The security analysis demonstrates that the proposed scheme is provably secure against outsider and insider attacks.

MDPAS: Markov Decision Process Based Adaptive Security for Sensors in Internet of Things

Nowadays chipped based sensors and RFID tags are widely employed in Internet of Things; however, for those devices, effective and flexible security mechanisms lack. In this paper we study the security requirement and propose an adaptive security framework for sensors in Internet of things, which provides dynamic confidentiality, authenticity and integrity in the networks with relative suitable overhead by context aware computing, decision making and dynamic enforcement of policies. We employ Markov Decision Process to make the decisions of security actions and adopt aspect-oriented programming technique to enforce the security policies dynamically in the working networks. We made simulations of our framework, and the performance is encouraging.

Lightweight Secure Directed Diffusion for Wireless Sensor Networks

We propose a lightweight secure routing scheme for wireless sensor networks, which provides authenticity and integrity in the routing process with relative low overhead by extending directed diffusion protocol with secure measures. We analyze the security capabilities and performance of our scheme; besides, we execute a simulation and the performance results are encouraging.

Multi-view learning via multiple graph regularized generative model

Topic models, such as probabilistic latent semantic analysis (PLSA) and latent Dirichlet allocation (LDA), have shown impressive success in many fields. Recently, multi-view learning via probabilistic latent semantic analysis (MVPLSA), is also designed for multi-view topic modeling. These approaches are instances of generative model, whereas they all ignore the manifold structure of data distribution, which is generally useful for preserving the nonlinear information. In this paper, we propose a novel multiple graph regularized generative model to exploit the manifold structure in multiple views. Specifically, we construct a nearest neighbor graph for each view to encode its corresponding manifold information. A multiple graph ensemble regularization framework is proposed to learn the optimal intrinsic manifold. Then, the manifold regularization term is incorporated into a multi-view topic model, resulting in a unified objective function. The solutions are derived based on the Expectation Maximization optimization framework. Experimental results on real-world multi-view data sets demonstrate the effectiveness of our approach.

Block linear discriminant analysis for visual tensor objects with frequency or time information

Abstract Recently, due to the advancement of acquisition techniques, visual tensor data have been accumulated in a great variety of engineering fields, e.g., biometrics, neuroscience, surveillance and remote sensing. How to analyze and learn with such tensor objects thus becomes an important and growing interest in machine learning community. In this paper, we propose a block linear discriminant analysis (BLDA) algorithm to extract features for visual tensor objects such as multichannel/hyperspectral face images or human gait videos. Taking the inherent characteristic of such tensor data into account, we unfold tensor objects according to their spatial information and frequency/time information, and represent them in a block matrix form. As a result, the block form between-class and within-class scatter matrices are constructed, and a related block eigen-decomposition is solved to extract features for classification. Comprehensive experiments have been carried out to test the effectiveness of the proposed method, and the results show that BLDA outperforms existing algorithms like DATER, 2DLDA, GTDA, UMLDA, STDA and MPCA for visual tensor object analysis.

An Efficient Solution for Time-Bound Hierarchical Key Assignment Scheme

Time-bound hierarchical key assignment (TBHKA) scheme is a cryptographic method. It can assign encryption keys depending on time to a set of security classes in a partially ordered hierarchy. Only the authorized user can compute the encryption key to access the subscribing class (including lower down class) according to the hierarchy. In 2005, Yeh firstly proposed a RSA-based TBHKA scheme supporting discrete time period. However, it had been proved insecure against user colluding attacks. Up to now, there are less study for TBHKA scheme supporting discrete time period. In this paper, we propose a secure and efficient TBHKA scheme. Our scheme is based on pairing-based public key cryptosystem and supports discrete time period. The security analysis is demonstrated that our scheme is secure against outside adversary and malicious user. Finally, we make comparisons between recently proposed two TBHKA schemes and our scheme. It will show the advantages of our scheme.