Visible to the public Biblio

Filters: Author is Li, C.  [Clear All Filters]
2019-03-28
Chen, J., Xu, R., Li, C..  2018.  Research of Security Situational Awareness and Visualization Approach in Cloud Computing. 2018 International Conference on Networking and Network Applications (NaNA). :201-205.
Cloud computing is an innovative mechanism to optimize computing and storage resource utilization. Due to its cost-saving, high-efficiency advantage, the technology receives wide adoption from IT industries. However, the frequent emergences of security events become the heaviest obstacle for its advancement. The multi-layer and distributive characteristics of cloud computing make IT admins compulsively collect all necessary situational information at cloud runtime if they want to grasp the panoramic secure state, hereby practice configuration management and emergency response methods when necessary. On the other hand, technologies such as elastic resource pooling, dynamic load balancing and virtual machine real-time migration complicate the difficulty of data gathering, where secure information may come from virtual machine hypervisor, network accounting or host monitor proxies. How to classify, arrange, standardize and visualize these data turns into the most crucial issue for cloud computing security situation awareness and presentation. This dissertation borrows traditional fashion of data visualization to integrate into cloud computing features, proposes a new method for aggregating and displaying secure information which IT admins concern, and expects that by method realization cloud security monitor/management capabilities could be notably enhanced.
2018-09-05
Li, C., Palanisamy, B., Joshi, J..  2017.  Differentially Private Trajectory Analysis for Points-of-Interest Recommendation. 2017 IEEE International Congress on Big Data (BigData Congress). :49–56.

Ubiquitous deployment of low-cost mobile positioning devices and the widespread use of high-speed wireless networks enable massive collection of large-scale trajectory data of individuals moving on road networks. Trajectory data mining finds numerous applications including understanding users' historical travel preferences and recommending places of interest to new visitors. Privacy-preserving trajectory mining is an important and challenging problem as exposure of sensitive location information in the trajectories can directly invade the location privacy of the users associated with the trajectories. In this paper, we propose a differentially private trajectory analysis algorithm for points-of-interest recommendation to users that aims at maximizing the accuracy of the recommendation results while protecting the privacy of the exposed trajectories with differential privacy guarantees. Our algorithm first transforms the raw trajectory dataset into a bipartite graph with nodes representing the users and the points-of-interest and the edges representing the visits made by the users to the locations, and then extracts the association matrix representing the bipartite graph to inject carefully calibrated noise to meet έ-differential privacy guarantees. A post-processing of the perturbed association matrix is performed to suppress noise prior to performing a Hyperlink-Induced Topic Search (HITS) on the transformed data that generates an ordered list of recommended points-of-interest. Extensive experiments on a real trajectory dataset show that our algorithm is efficient, scalable and demonstrates high recommendation accuracy while meeting the required differential privacy guarantees.

Palanisamy, B., Li, C., Krishnamurthy, P..  2017.  Group Differential Privacy-Preserving Disclosure of Multi-level Association Graphs. 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS). :2587–2588.

Traditional privacy-preserving data disclosure solutions have focused on protecting the privacy of individual's information with the assumption that all aggregate (statistical) information about individuals is safe for disclosure. Such schemes fail to support group privacy where aggregate information about a group of individuals may also be sensitive and users of the published data may have different levels of access privileges entitled to them. We propose the notion ofεg-Group Differential Privacy that protects sensitive information of groups of individuals at various defined privacy levels, enabling data users to obtain the level of access entitled to them. We present a preliminary evaluation of the proposed notion of group privacy through experiments on real association graph data that demonstrate the guarantees on group privacy on the disclosed data.

2018-06-20
Zhang, L., Li, C., Li, Y., Luo, Q., Zhu, R..  2017.  Group signature based privacy protection algorithm for mobile ad hoc network. 2017 IEEE International Conference on Information and Automation (ICIA). :947–952.

Nowadays, Vehicular ad hoc Network as a special class of Mobile ad hoc Network(MANET), provides plenty of services. However, it also brings the privacy protection issues, and there are conflicts between the privacy protection and the services. In this paper, we will propose a privacy protection algorithm based on group signature including two parts, group signature based anonymous verification and batch verification. The anonymous verification is based on the network model we proposed, which can reduce the trust authority burden by dividing the roadside units into different levels, and the batch verification can reduce the time of message verification in one group. We also prove our algorithm can satisfy the demand of privacy protection. Finally, the simulation shows that the algorithm we proposed is better than the BBS on the length of the signature, time delay and packet loss rate.

2018-06-11
Zeng, J., Dong, L., Wu, Y., Chen, H., Li, C., Wang, S..  2017.  Privacy-Preserving and Multi-Dimensional Range Query in Two-Tiered Wireless Sensor Networks. GLOBECOM 2017 - 2017 IEEE Global Communications Conference. :1–7.

With the advancement of sensor electronic devices, wireless sensor networks have attracted more and more attention. Range query has become a significant part of sensor networks due to its availability and convenience. However, It is challenging to process range query while still protecting sensitive data from disclosure. Existing work mainly focuses on privacy- preserving range query, but neglects the damage of collusion attacks, probability attacks and differential attacks. In this paper, we propose a privacy- preserving, energy-efficient and multi-dimensional range query protocol called PERQ, which not only achieves data privacy, but also considers collusion attacks, probability attacks and differential attacks. Generalized distance-based and modular arithmetic range query mechanism are used. In addition, a novel cyclic modular verification scheme is proposed to verify the data integrity. Extensive theoretical analysis and experimental results confirm the high performance of PERQ in terms of energy efficiency, security and accountability requirements.

2018-02-21
Li, C., Yang, C..  2017.  Cryptographic key management methods for mission-critical wireless networks. 2017 7th IEEE International Conference on Electronics Information and Emergency Communication (ICEIEC). :33–36.
When a large scale disaster strikes, it demands an efficient communication and coordination among first responders to save life and other community resources. Normally, the traditional communication infrastructures such as landline phone or cellular networks are damaged and dont provide adequate communication services to first responders for exchanging emergency related information. Wireless mesh networks is the promising alternatives in such type of situations. The security requirements for emergency response communications include privacy, data integrity, authentication, access control and availability. To build a secure communication system, usually the first attempt is to employ cryptographic keys. In critical-mission wireless mesh networks, a mesh router needs to maintain secure data communication with its neighboring mesh routers. The effective designs on fast pairwise key generation and rekeying for mesh routers are critical for emergency response and are essential to protect unicast traffic. In this paper, we present a security-enhanced session key generation and rekeying protocols EHPFS (enhanced 4-way handshake with PFS support). It eliminate the DoS attack problem of the 4-way handshake in 802.11s. EHPFS provides additional support for perfect forward secrecy (PFS). Even in case a Primary Master Key (PMK) is exposed, the session key PTK will not be compromised. The performance and security analysis show that EHPFS is efficient.
2018-02-06
Palanisamy, B., Li, C., Krishnamurthy, P..  2017.  Group Privacy-Aware Disclosure of Association Graph Data. 2017 IEEE International Conference on Big Data (Big Data). :1043–1052.

In the age of Big Data, we are witnessing a huge proliferation of digital data capturing our lives and our surroundings. Data privacy is a critical barrier to data analytics and privacy-preserving data disclosure becomes a key aspect to leveraging large-scale data analytics due to serious privacy risks. Traditional privacy-preserving data publishing solutions have focused on protecting individual's private information while considering all aggregate information about individuals as safe for disclosure. This paper presents a new privacy-aware data disclosure scheme that considers group privacy requirements of individuals in bipartite association graph datasets (e.g., graphs that represent associations between entities such as customers and products bought from a pharmacy store) where even aggregate information about groups of individuals may be sensitive and need protection. We propose the notion of $ε$g-Group Differential Privacy that protects sensitive information of groups of individuals at various defined group protection levels, enabling data users to obtain the level of information entitled to them. Based on the notion of group privacy, we develop a suite of differentially private mechanisms that protect group privacy in bipartite association graphs at different group privacy levels based on specialization hierarchies. We evaluate our proposed techniques through extensive experiments on three real-world association graph datasets and our results demonstrate that the proposed techniques are effective, efficient and provide the required guarantees on group privacy.