Princeton University

School of Engineering & Applied Science

Information, Aggregation in Quantized Consensus, Recommender Systems, and Ranking

Shang Shang
Engineering Quadrangle J401
Tuesday, May 13, 2014 - 2:00pm to 3:30pm

In recent years, the amount of information is growing rapidly. The problem of information aggregation has received considerable attention, and finds applications in multiple disciplines. Information aggregation is the process of collecting and summarizing data, often from multiple sources. Data are combined for drawing inferences or some other purpose.  
The thriving development of big data holds great promise for many applications, but also leads to potential risk on privacy issues. This thesis addresses a variety of problems in information aggregation, including quantized consensus, recommender systems, and ranking. We are interested in both performance and privacy.
The thesis starts with investigating a class of distributed quantized consensus algorithms for arbitrary networks. An upper bound on the convergence time of the algorithms is derived for an arbitrary graph of size N. Inspired by this class of gossip consensus algorithms and Google’s PageRank, and motivated by the development of group-based social networks, a privacy preserving recommender system based on groups is proposed. The main idea is to use groups as a natural middleware to preserve users’ privacy. A novel hybrid collaborative filtering model based on random walks is constructed to provide recommendation and prediction to group members. Lastly, the error probability of ranking algorithms equipped with differential privacy is analyzed, and upper bounds on the error rates for arbitrary positional ranking rules are derived.