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Data Privacy Preservation In Collaborative Filtering Based Recommender Systems, Xiwei Wang Jan 2015

Data Privacy Preservation In Collaborative Filtering Based Recommender Systems, Xiwei Wang

Theses and Dissertations--Computer Science

This dissertation studies data privacy preservation in collaborative filtering based recommender systems and proposes several collaborative filtering models that aim at preserving user privacy from different perspectives.

The empirical study on multiple classical recommendation algorithms presents the basic idea of the models and explores their performance on real world datasets. The algorithms that are investigated in this study include a popularity based model, an item similarity based model, a singular value decomposition based model, and a bipartite graph model. Top-N recommendations are evaluated to examine the prediction accuracy.

It is apparent that with more customers' preference data, recommender systems …


Privacy Preserving Data Mining For Numerical Matrices, Social Networks, And Big Data, Lian Liu Jan 2015

Privacy Preserving Data Mining For Numerical Matrices, Social Networks, And Big Data, Lian Liu

Theses and Dissertations--Computer Science

Motivated by increasing public awareness of possible abuse of confidential information, which is considered as a significant hindrance to the development of e-society, medical and financial markets, a privacy preserving data mining framework is presented so that data owners can carefully process data in order to preserve confidential information and guarantee information functionality within an acceptable boundary.

First, among many privacy-preserving methodologies, as a group of popular techniques for achieving a balance between data utility and information privacy, a class of data perturbation methods add a noise signal, following a statistical distribution, to an original numerical matrix. With the help …