Sheng Zhong

Wormhole Attack Detection Algorithms in Wireless Network Coding Systems

Network coding has been shown to be an effective approach to improve the wireless system performance. However, many security issues impede its wide deployment in practice. Besides the well-studied pollution attacks, there is another severe threat, that of wormhole attacks, which undermines the performance gain of network coding. Since the underlying characteristics of network coding systems are distinctly different from traditional wireless networks, the impact of wormhole attacks and countermeasures are generally unknown. In this paper, we quantify wormholes devastating harmful impact on network coding system performance through experiments. We first propose a centralized algorithm to detect wormholes and show its correctness rigorously. For the distributed wireless network, we propose DAWN, a Distributed detection Algorithm against Wormhole in wireless Network coding systems, by exploring the change of the flow directions of the innovative packets caused by wormholes. We rigorously prove that DAWN guarantees a good lower bound of successful detection rate. We perform analysis on the resistance of DAWN against collusion attacks. We find that the robustness depends on the node density in the network, and prove a necessary condition to achieve collusion-resistance. DAWN does not rely on any location information, global synchronization assumptions or special hardware/middleware. It is only based on the local information that can be obtained from regular network coding protocols, and thus the overhead of our algorithms is tolerable. Extensive experimental results have verified the effectiveness and the efficiency of DAWN.

Joint Resource Allocation for Device-to-Device Communications Underlaying Uplink MIMO Cellular Networks

This paper presents a resource allocation framework for device-to-device (D2D) communications underlaying uplink MIMO cellular networks. At first, our aim is to address the sum-rate maximization problem of the cellular network with both D2D and cellular users. An algorithm based on pure random search is presented for obtaining the optimal resource allocation without using an exhaustive search. Then, we propose a noncooperative resource allocation game for the joint self-optimization of channel allocation, power control, and precoding of the D2D users in a more practical setting. The feasibility and existence of the pure strategy Nash equilibrium are then established. An iterative algorithm based on best response dynamic is then proposed to determine the feasible pure strategy Nash equilibrium under specific conditions. As the algorithm may not always converge, we devise a strategy refinement mechanism to tackle this issue based on the sum-rate criterion. Simulation results verify our theoretical analysis and findings.

A Game-Theoretic Approach to Stimulate Cooperation for Probabilistic Routing in Opportunistic Networks

Opportunistic networking is an important technique to enable users to communicate in an environment where contemporaneous end-to-end paths are unavailable or unstable. To support end-to-end messaging in opportunistic networks, a number of probabilistic routing protocols have been proposed. However, when nodes are selfish, they may not have incentives to participate in probabilistic routing, and the system performance will degrade significantly. In this paper, we present novel incentive schemes for probabilistic routing that stimulates selfish nodes to participate. We not only rigorously prove the properties of our schemes, but also extensively evaluate our schemes using GloMoSim. Evaluation results show that there is an up to 75.8% gain in delivery ratio compared with a probabilistic routing protocol providing no incentive.

Privacy-Preserving Data Aggregation in Mobile Phone Sensing

Mobile phone sensing provides a promising paradigm for collecting sensing data and has been receiving increasing attention in recent years. Different from most existing works, which protect participants privacy by hiding the content of their data and allow the aggregator to compute some simple aggregation functions, we propose a new approach to protect participants privacy by delinking data from its sources. This approach allows the aggregator to get the exact distribution of the data aggregation and, therefore, enables the aggregator to efficiently compute arbitrary/complicated aggregation functions. In particular, we first present an efficient protocol that allows an untrusted data aggregator to periodically collect sensed data from a group of mobile phone users without knowing which data belong to which user. Assume there are n users in the group. Our protocol achieves n-source anonymity in the sense that the aggregator only learns that the source of a piece of data is one of the n users. Then, we consider a practical scenario where users may have different source anonymity requirements and provide a solution based on dividing users into groups. This solution optimizes the efficiency of data aggregation and meets all users requirements at the same time.

Towards cheat-proof cooperative relay for cognitive radio networks

In cognitive radio networks, cooperative relay is a new technology that can significantly improve spectrum efficiency. While the existing protocols for cooperative relay are very interesting and useful, there is a crucial problem that has not been investigated: Selfish users may cheat in cooperative relay, in order to benefit themselves. Here by cheating we mean the behavior of reporting misleading channel and payment information to the primary user and other secondary users. Such cheating behavior may harm other users and thus lead to poor system throughput. Given the threat of selfish users cheating, our objective in this paper is to suppress the cheating behavior of selfish users in cooperative relay. Hence, we design the first cheat-proof scheme for cooperative relay in cognitive radio networks, and rigorously prove that under our scheme, selfish users have no incentive to cheat. Our design and analysis start in the model of strategic game for interactions among secondary users; then they are extended to the entire cooperative relay process, which is modeled as an extensive game. To make our schemes more practical, we also consider two aspects: fairness and system security. Results of extensive simulations demonstrate that our scheme suppresses cheating behavior and thus improves the system throughput in face of selfish users.

We Can Track You if You Take the Metro: Tracking Metro Riders Using Accelerometers on Smartphones

Motion sensors, especially accelerometers, on smartphones have been discovered to be a powerful side channel for spying on users privacy. In this paper, we reveal a new accelerometer-based side-channel attack which is particularly serious: malware on smartphones can easily exploit the accelerometers to trace metro riders stealthily. We first address the challenge to automatically filter out metro-related data from a mass of miscellaneous accelerometer readings, and then propose a basic attack which leverages an ensemble interval classifier built from supervised learning to infer the riding trajectory of the user. As the supervised learning requires the attacker to collect labeled training data for each station interval, this attack confronts the scalability problem in big cities with a huge metro network. We thus further present an improved attack using semi-supervised learning, which only requires the attacker to collect labeled data for a very small number of distinctive station intervals. We conduct real experiments on a large self-built dataset, which contains more than 120 h of data collected from six metro lines of three major cities. The results show that the inferring accuracy could reach 89% and 94% if the user takes the metro for four and six stations, respectively. We finally discuss possible countermeasures against the proposed attack.

On Designing Satisfaction-Ratio-Aware Truthful Incentive Mechanisms for

To protect individuals location privacy, an important privacy protection technique that can be used is k -anonymity, which requires at least k users to participate in an anonymity set, so that any user in the set cannot be distinguished from the other k-1 users. However, a significant part of users may not be concerned about their location privacy and therefore may not be interested in participating in the anonymity set. Hence, a prerequisite for achieving k-anonymity location privacy is to stimulate users to participate. In this paper, we revisit the problem of stimulating users that are privacy-indifferent to participate in the anonymity set and providing k-anonymity location privacy for privacy-sensitive users. We first study the case where all privacy-sensitive users have the same requirement of privacy. Then, we extend our study to a more general setting, where privacy-sensitive users have different requirements. For both cases, we design auction-based mechanisms and rigorously prove that the mechanisms are truthful. More importantly, our mechanisms can achieve higher satisfaction ratio than the existing work, i.e., our mechanisms greatly increase the number of privacy-sensitive users successfully winning the auction and receiving privacy protection. We evaluate our mechanisms by using extensive numerical experiments and simulations on a real-world data set. Evaluation results show that our mechanisms achieve much better performance regarding the satisfaction ratio compared with the state-of-the-art mechanisms, and that the computational efficiency is good.

k

Statistical tests are powerful tools for data analysis. Kruskal-Wallis test is a non-parametric statistical test that evaluates whether two or more samples are drawn from the same distribution. It is commonly used in various areas. But sometimes, the use of the method is impeded by privacy issues raised in fields such as biomedical research and clinical data analysis because of the confidential information contained in the data. In this work, we give a privacy-preserving solution for the Kruskal-Wallis test which enables two or more parties to coordinately perform the test on the union of their data without compromising their data privacy. To the best of our knowledge, this is the first work that solves the privacy issues in the use of the Kruskal-Wallis test on distributed data.

-Anonymity Location Privacy

Trajectory data, i.e., human mobility traces, is extremely valuable for a wide range of mobile applications. However, publishing raw trajectories without special sanitization poses serious threats to individual privacy. Recently, researchers begin to leverage differential privacy to solve this challenge. Nevertheless, existing mechanisms make an implicit assumption that the trajectories contain a lot of identical prefixes or n-grams, which is not true in many applications. This paper aims to remove this assumption and propose a differentially private publishing mechanism for more general time-series trajectories. One natural solution is to generalize the trajectories, i.e., merge the locations at the same time. However, trivial merging schemes may breach differential privacy. We, thus, propose the first differentially-private generalization algorithm for trajectories, which leverage a carefully-designed exponential mechanism to probabilistically merge nodes based on trajectory distances. Afterwards, we propose another efficient algorithm to release trajectories after generalization in a differential private manner. Our experiments with real-life trajectory data show that the proposed mechanism maintains high data utility and is scalable to large trajectory datasets.

Privacy-preserving Kruskal-Wallis test

The performance of wireless networks can be significantly improved by using network coding with opportunistic routing. In such a wireless network, selfish nodes may not cooperate when they are supposed to forward packets. This fundamental cooperation problem in packet forwarding is closely related to the incentive problem in network-coding wireless networks with opportunistic routing, and to the incentive-compatible packet-forwarding problem in conventional wireless networks, but different from both of them. In this paper, we propose incentive-compatible packet opportunistic forwarding for network-coding wireless networks (INPAC), a solution using a combination of game-theoretic and cryptographic techniques. We formally prove that, if INPAC is used, then being cooperative in packet forwarding is a subgame perfect equilibrium. That is, nodes have incentives to follow the protocol and forward packets. We have implemented and evaluated INPAC on the Orbit Lab test bed. Our evaluation results verify the incentive compatibility of INPAC and also its efficiency.