Tingting Chen

Stimulating Cooperation in Vehicular Ad Hoc Networks: A Coalitional Game Theoretic Approach

In vehicular ad hoc networks (VANETs), because of the nonexistence of end-to-end connections, it is essential that nodes take advantage of connection opportunities to forward messages to make end-to-end messaging possible. Thus, it is crucial to make sure that nodes have incentives to forward messages for others, despite the fact that the routing protocols in VANETs are different from traditional end-to-end routing protocols. In this paper, we study how to stimulate message forwarding in VANETs. Our approach is based on coalitional game theory. In particular, we propose an incentive scheme for VANETs and rigorously show that with our scheme, faithfully following the routing protocol is in the best interest of each node. In addition, we extend our scheme to taking the limited storage space of each node into consideration. Experiments on testbed trace data verify that our scheme is effective in stimulating cooperation of message forwarding in VANETs.

Incentive-compatible opportunistic routing for wireless networks

User-contributed wireless mesh networks are a disruptive technology that may fundamentally change the economics of edge network access and bring the benefits of a computer network infrastructure to local communities at low cost, anywhere in the world. To achieve high throughput despite highly unpredictable and lossy wireless channels, it is essential that such networks take advantage of transmission opportunities wherever they emerge. However, as opportunistic routing departs from the traditional but less effective deterministic, shortest-path based routing, user nodes in such networks may have less incentive to follow protocols and contribute. In this paper, we present the first routing protocols in which it is incentive-compatible for each user node to honestly participate in the routing despite opportunistic transmissions. We not only rigorously prove the properties of our protocols but also thoroughly evaluate a complete implementation of our protocols. Experiments show that there is a 5.8%-58.0% gain in throughput when compared with an opportunistic routing protocol that does not provide incentives and users can act selfishly.

Privacy preserving Back-propagation neural network learning over arbitrarily partitioned data

Neural networks have been an active research area for decades. However, privacy bothers many when the training dataset for the neural networks is distributed between two parties, which is quite common nowadays. Existing cryptographic approaches such as secure scalar product protocol provide a secure way for neural network learning when the training dataset is vertically partitioned. In this paper, we present a privacy preserving algorithm for the neural network learning when the dataset is arbitrarily partitioned between the two parties. We show that our algorithm is very secure and leaks no knowledge (except the final weights learned by both parties) about other party’s data. We demonstrate the efficiency of our algorithm by experiments on real world data.

INPAC: An Enforceable Incentive Scheme for Wireless Networks using Network Coding

Wireless mesh networks have been widely deployed to provide broadband network access, and their performance can be significantly improved by using a new technology called network coding. In a wireless mesh network using network coding, selfish nodes may deviate from the protocol when they are supposed to forward packets. This fundamental problem of packet forwarding incentives is closely related to the incentive compatible routing problem in wireless mesh networks using network coding, and to the incentive compatible packet forwarding problem in conventional wireless networks, but different from both of them. In this paper, we propose INPAC, the first incentive scheme for this fundamental problem, which uses a combination of game theoretic and cryptographic techniques to solve it. We formally prove that, if INPAC is used, then following the protocol faithfully is a subgame perfect equilibrium. To make INPAC more practical, we also provide an extension that achieves two improvements: (a) an online authority is no longer needed; (b) the computation and communication overheads are reduced. We have implemented and evaluated INPAC on the Orbit Lab testbed. Our evaluation results verify the incentive compatibility of INPAC and demonstrate that it is efficient.

k-Anonymous data collection

To protect individual privacy in data mining, when a miner collects data from respondents, the respondents should remain anonymous. The existing technique of Anonymity-Preserving Data Collection partially solves this problem, but it assumes that the data do not contain any identifying information about the corresponding respondents. On the other hand, the existing technique of Privacy-Enhancing k-Anonymization can make the collected data anonymous by eliminating the identifying information. However, it assumes that each respondent submits her data through an unidentified communication channel. In this paper, we propose k-Anonymous Data Collection, which has the advantages of both Anonymity-Preserving Data Collection and Privacy-Enhancing k-Anonymization but does not rely on their assumptions described above. We give rigorous proofs for the correctness and privacy of our protocol, and experimental results for its efficiency. Furthermore, we extend our solution to the fully malicious model, in which a dishonest participant can deviate from the protocol and behave arbitrarily.

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.

A reputation system for wireless mesh networks using network coding

Using network coding, wireless mesh networks can significantly improve their performance. However, since many wireless mesh networks have user contributed devices as their nodes, to guarantee the cooperation of such selfish nodes is a highly challenging problem. In this paper, we study how to stimulate selfish nodes to cooperate in wireless mesh networks using network coding. We propose a simple, practical reputation system that rewards cooperative behavior in routing and packet forwarding and penalizes non-cooperative behavior. Simulation results verify that our reputation system is very efficient and that it effectively stimulates cooperation.

Perfectly fair channel assignment in non-cooperative multi-radio multi-channel wireless networks

The channel assignment problem is very important in wireless networks. In this letter, we study the non-cooperative channel assignment problem in competitive multi-radio multi-channel wireless networks, with a focus on fairness issues. We propose a Nash equilibrium solution with stronger guarantee on fairness among players than existing works, by requiring a payment from each player. We show that, in our scheme, when system converges to a stable status achieving Nash equilibrium, all players obtain the same throughput. Simulation results verify that our scheme achieves perfect fairness.

An open platform telepresence robot with natural human interface

This paper explains our low-cost approach to building an open platform telepresence robot, which uses an iRobot Create, ROS (Robot Operating System) and mobile devices. Besides using existing ROS packages we developed an Android application based on rosjava_core and android_core to control and view a live video stream from the remote robot. The robot could be used to have a video conference with people at a remote location. It is also equipped with capabilities such as autonomous navigation, hand-gesture recognition, and speech recognition through cloud computing services provided by Google. Experimental results verify the effectiveness of the developed telepresence robot.

An efficient identity-based protocol for private matching

An important application of the Internet is that people can find partners satisfying their requirements from the huge number of users. In this paper, we present a cryptographic protocol for private matching, in which a user finds her desired partners without revealing her requirements, and the potential partners do not need to reveal their profiles. An additional advantage of our protocol is that it is identity-based, which means the involved parties do not need to have a priori knowledge of each others public keys; as long as they know each others identities, the protocol can be executed. Copyright