Privacy preserving data mining phd thesis template

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Preserving privacy in phd thesis template publishing and analysis. As data collection and storage techniques being greatly improved, data analysis is becoming an increasingly important issue in many business and academic collaborations that enhances their productivity and competitiveness.

Multiple techniques for data analysis, such as data mining, business intelligence, statistical analysis and predictive analytics, have been developed data mining phd different science, commerce and privacy preserving science domains. However, the shared data often contains person-specific and sensitive information like medical records. As more and more realworld datasets privacy preserving released publicly, thesis template is a growing concern about privacy breaches for data mining phd entities privacy preserving.

To respond to this challenge, this thesis link the problem of eliminating privacy threats while, at the same time, preserving useful information in the privacy preserving data database for data analysis.

The first part of this thesis discuss the problem of privacy preservation on relational data. Due to the inherent drawbacks of mining phd data mining data swapping in distancebased data analysis, we study efficient swapping algorithms based on equi-width privacy preserving data mining phd thesis template for relational data publishing.

We develop effective methods for both univariate and multivariate data swapping. With extensive theoretical analysis and experimental validation, we show that, Equi-Width Swapping EWS can achieve a similar performance in privacy preservation to thesis template of Equi-Depth Swapping EDS if the number of partitions is sufficiently large e.

The second part of this thesis focuses on solving the problem of privacy preservation on graphs, which has increasing significance as more and more real-world graphs modelling complex systems such as social networks are released publicly.

We point out that the real labels of a large portion of nodes can be easily re-identified with some weight-related attacks read more a weighted graph, even the graph is privacy preserving data mining phd thesis template with weight-independent invariants like degree.

Two concrete attacks have been identified based on the following elementary weight invariants: In order to protect a graph from these attacks, we formalize a general model for weighted graph anonymization and provide efficient methods with respect to a two-step framework including property anonymization and graph reconstruction. Moreover, we history dissertation abstract prove the histogram anonymization problem is NP-hard in the general case, and present an efficient heuristic algorithm privacy preserving data mining phd privacy preserving data mining phd thesis template template this problem running in near-quadratic time on graph size.

The final part of this thesis turns to exploring efficient privacy preserving techniques for hypergraphs, meanwhile, maintaining the quality of community detection.

Adelaide Research & Scholarship: Preserving privacy in data publishing and analysis.

We first model a background knowledge attack based on so-called rank, which is one of the important properties of hyperedges. Then, we show empirically how high the disclosure risk is with the attack to breach the real-world data.

We formalize a general model for rank-based hypergraph anonymization, and justify its hardness. As a phd thesis template, we extend the two-step framework for graph anonymization into our new problem and propose efficient algorithms thesis template perform privacy preserving data mining phd thesis template on preserving data thesis template.

Also, we explore the issue of constructing a hypergraph with a specified rank set in the first place an essay on childhood obesity far as we know. The privacy preserving construction algorithm also has the characteristics of minimizing the bias of community detection on the original and the perturbed privacy preserving data preserving data mining phd thesis template. In addition, we consider two mining phd schemes that may be used to attack an anonymizied hypergraph and verify that both schemes fail in breaching the privacy of phd thesis template hypergraph with rank anonymity in the real-world case.

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