Trust, Recommendation Systems, and Collaboration - UMD - October 2014

Public Audience
Purpose: To highlight project progress. Information is generally at a higher level which is accessible to the interested public. All information contained in the report (regions 1-3) is a Government Deliverable/CDRL.

PI(s): John S. Baras, Jennifer Golbeck
Researchers: Peixin Gao (graduate student), Xiangyang Liu (graduate student)

HARD PROBLEM(S) ADDRESSED
This refers to Hard Problems, released November 2012.

#2 (Policy-Governed Secure Collaboration);
#1 (Scalability and Composability); #5 (Understanding and Accounting for Human Behavior)

PUBLICATIONS
Papers published in this quarter as a result of this research. Include title, author(s), venue published/presented, and a short description or abstract. Identify which hard problem(s) the publication addressed. Papers that have not yet been published should be reported in region 2 below.

X. Liu and J.S. Baras, “Using Trust in Distributed Consensus With Adversaries in Sensor and Other Networks,” invited paper, Proceedings of 17th International Conference on Information Fusion (FUSION 2014), Salamanca, Spain, July 7-10, 2014.

Abstract:

Extensive research efforts have been devoted to distributed consensus with adversaries. Many diverse applications drive this increased interest in this area including distributed collaborative sensor networks, sensor fusion and distributed collaborative control. We consider the problem of detecting Byzantine adversaries in a network of agents with the goal of reaching consensus. We propose a novel trust model that establishes both local trust based on local evidences and global trust based on local exchange of local trust values. We describe a trustaware consensus algorithm that integrates the trust evaluation mechanism into the traditional consensus algorithm and propose various local decision rules based on local evidence. To further enhance the robustness of trust evaluation itself, we also provide a trust propagation scheme in order to take into account evidences of other nodes in the network. The algorithm is flexible and extensible to incorporate more complicated designs of decision rules and trust models. Then we show by simulation that the trust-aware consensus algorithm can effectively detect Byzantine adversaries and exclude them from consensus iterations even in sparse networks. These results can be applied for fusion of trust evidences as well as for sensor fusion when malicious sensors are present like for example in power grid sensing and monitoring.

ACCOMPLISHMENT HIGHLIGHTS

Our goal is to develop a transformational framework for a science of trust, and its impact on local policies for collaboration, in networked multi-agent systems. The framework will take human behavior into account from the start by treating humans as integrated components of these networks, interacting dynamically with other elements. The new analytical framework will be integrated, and validated, with empirical methods of analyzing experimental data on trust, recommendation, and reputation, from several datasets available to us, in order to capture fundamental trends and patterns of human behavior, including trust and mistrust propagation, confidence in trust, phase transitions in the dynamic graph models involved in the new framework, stability or instability of collaborations.

The highlights of our results during this reporting quarter are as follows:

We developed new algorithms that effectively and provably use trust in distributed consensus problems in the presence of adversaries. Such problems are of interest in distributed fusion in sensor networks. We showed that a trust mechanism allows correct consensus to occur whereby without the trust mechanism this would not be possible.

 We developed new mathematical models for networks that carry opinions (beliefs) in their nodes, while the interaction between the nodes (agents) can be positive (friends) or negative (enemies). We analyzed the dynamics of belief evolution and emergence in such signed networks and discovered new laws governing these dynamics.

We developed a novel model and an efficient solution algorithm to the so alled "Advertisement Allocation Problem" in large social networks, using a new and innovative embedding of the graph in hyperbolic space. The new algorithm obtains the same results as other algorithms albeit with complexity lower by two orders of magnitude.

We demonstrated how physical layer security schemes can be successfully employed to creete a trusted core and provide privacy protection in distributed control and inference schemes.

We investicated several problems in crowdsourcing, by devloping novel methods and algorithms that can handle multiple domains of knowledge, multi-dimensional trust in the knowledge of people or experts, and budget constraints. We investigated analytically these problems and obtained new algorithms and results on their performance.

B) COMMUNITY INTERACTION

J.S. Baras gave the following invited, plenary and keynote lectures on the topics, approach and results in this Task:

(i) J.S. Baras, “Security and Trust in a Networked Immersed World: From Components to Systems and Beyond,” invited keynote lecture, Workshop on Security and Safety: Issues, Concepts and Ideas , 2nd Hellenic Forum for Science, Innovation and Technology, Demokritos Research Center, Athens, Greece, June 30 - July 4, 2014.