| S. No. | IEEE TITLE | ABSTRACT | IEEE YEAR |
| 1 | A Pseudonym Management System to Achieve Anonymity in Vehicular Ad Hoc Networks | In this paper we propose a framework for providing anonymity to communicating cars in VANETs. The anonymity is accomplished based on a system of pseudonym generation, distribution, and replenishing. The road side units (RSUs) play a key role in the framework by receiving the originally generated pseudonyms from the trusted authority, and then distributing pseudonym sets to cars while shuffling the sets amongst themselves to maximize anonymity. The pseudonym distribution process among the RSUs and to the vehicles is highly adaptive to accommodate the needs of the vehicles. We develop a distributed optimization algorithm for the shuffling process and a novel mechanism for cars to change their pseudonyms. Experimental evaluations based on ns3 simulations demonstrate the effectiveness of the framework through showing relatively high values of the used metric, namely the anonymity set. | 2016 |
| 2 | Enabling Fine-Grained Multi-Keyword Search Supporting Classified Sub-Dictionaries over Encrypted Cloud Data | Using cloud computing, individuals can store their data on remote servers and allow data access to public users through the cloud servers. As the outsourced data are likely to contain sensitive privacy information, they are typically encrypted before uploaded to the cloud. This, however, significantly limits the usability of outsourced data due to the difficulty of searching over the encrypted data. In this paper, we address this issue by developing the fine-grained multi-keyword search schemes over encrypted cloud data. Our original contributions are three-fold. First, we introduce the relevance scores and preference factors upon keywords which enable the precise keyword search and personalized user experience. Second, we develop a practical and very efficient multi-keyword search scheme. The proposed scheme can support complicated logic search the mixed “AND”, “OR” and “NO” operations of keywords. Third, we further employ the classified sub-dictionaries technique to achieve better efficiency on index building, trapdoor generating and query. Lastly, we analyze the security of the proposed schemes in terms of confidentiality of documents, privacy protection of index and trapdoor, and unlinkability of trapdoor. Through extensive experiments using the real-world dataset, we validate the performance of the proposed schemes. Both the security analysis and experimental results demonstrate that the proposed schemes can achieve the same security level comparing to the existing ones and better performance in terms of functionality, query complexity and efficiency. | 2016 |
| 3 | netCSI: A Generic Fault Diagnosis Algorithm for Large-Scale Failures in Computer Networks | We present a framework and a set of algorithms for determining faults in networks when large scale outages occur. The design principles of our algorithm, netCSI, are motivated by the fact that failures are geographically clustered in such cases. We address the challenge of determining faults with incomplete symptom information due to a limited number of reporting nodes. netCSI consists of two parts: a hypotheses generation algorithm, and a ranking algorithm. When constructing the hypothesis list of potential causes, we make novel use of positive and negative symptoms to improve the precision of the results. In addition, we propose pruning and thresholding along with a dynamic threshold value selector, to reduce the complexity of our algorithm. The ranking algorithm is based on conditional failure probability models that account for the geographic correlation of the network objects in clustered failures. We evaluate the performance of netCSI for networks with both random and realistic topologies. We compare the performance of netCSI with an existing fault diagnosis algorithm, MAX-COVERAGE, and demonstrate an average gain of 128 percent in accuracy for realistic topologies. | 2016 |
| 4 | Privacy Protection for Wireless Medical Sensor Data | In recent years, wireless sensor networks have been widely used in healthcare applications, such as hospital and home patient monitoring. Wireless medical sensor networks are more vulnerable to eavesdropping, modification, impersonation and replaying attacks than the wired networks. A lot of work has been done to secure wireless medical sensor networks. The existing solutions can protect the patient data during transmission, but cannot stop the inside attack where the administrator of the patient database reveals the sensitive patient data. In this paper, we propose a practical approach to prevent the inside attack by using multiple data servers to store patient data. The main contribution of this paper is securely distributing the patient data in multiple data servers and employing the Paillier and ElGamal cryptosystems to perform statistic analysis on the patient data without compromising the patients’ privacy. | 2016 |
| 5 | Wireless Communications under Broadband Reactive Jamming Attacks | A reactive jammer jams wireless channels only when target devices are transmitting; Compared to constant jamming, reactive jamming is harder to track and compensate against [2], [38]. Frequency hopping spread spectrum (FHSS) and direct sequence spread spectrum (DSSS) have been widely used as countermeasures against jamming attacks. However, both will fail if the jammer jams all frequency channels or has high transmit power. In this paper, we propose an anti-jamming communication system that allows communication in the presence of a broadband and high power reactive jammer. The proposed system transmits messages by harnessing the reaction time of a reactive jammer. It does not assume a reactive jammer with limited spectrum coverage and transmit power, and thus can be used in scenarios where traditional approaches fail. We develop a prototype of the proposed system using GNURadio. Our experimental evaluation shows that when a powerful reactive jammer is present, the prototype still keeps communication, whereas other schemes such as 802.11 DSSS fail completely. | 2016 |
| 6 | Collusion-Tolerable Privacy-Preserving Sum and Product Calculation without Secure Channel | Much research has been conducted to securely outsource multiple parties’ data aggregation to an untrusted aggregator without disclosing each individual’s privately owned data, or to enable multiple parties to jointly aggregate their data while preserving privacy. However, those works either require secure pair-wise communication channels or suffer from high complexity. In this paper, we consider how an external aggregator or multiple parties can learn some algebraic statistics (e.g., sum, product) over participants’ privately owned data while preserving the data privacy. We assume all channels are subject to eavesdropping attacks, and all the communications throughout the aggregation are open to others. We first propose several protocols that successfully guarantee data privacy under semi-honest model, and then present advanced protocols which tolerate up to k passive adversaries who do not try to tamper the computation. Under this weak assumption, we limit both the communication and computation complexity of each participant to a small constant. At the end, we present applications which solve several interesting problems via our protocols | 2015 |
| 7 | Security Optimization of Dynamic Networks with Probabilistic Graph Modeling
and Linear Programming |
Securing the networks of large organizations is technically challenging due to the complex configurations and constraints. Managing these networks requires rigorous and comprehensive analysis tools. A network administrator needs to identify vulnerable configurations, as well as tools for hardening the networks. Such networks usually have dynamic and fluidic structures, thus one may have incomplete information about the connectivity and availability of hosts. In this paper, we address the problem of statically performing a rigorous assessment of a set of network security defense strategies with the goal of reducing the probability of a successful large-scale attack in a dynamically changing and complex network architecture. We describe a probabilistic graph model and algorithms for analyzing the security of complex networks with the ultimate goal of reducing the probability of successful attacks. Our model naturally utilizes a scalable state-of-the-art optimization technique called sequential linear programming that is extensively applied and studied in various engineering problems. In comparison to related solutions on attack graphs, our probabilistic model provides mechanisms for expressing uncertainties in network configurations, which is not reported elsewhere. We have performed comprehensive experimental validation with real-world network configuration data of a sizable organization. | 2015 |