Book chapter
A Generalized Approach for Social Network Integration and Analysis with Privacy Preservation
Data Mining and Knowledge Discovery for Big Data
01 Jan 2014
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Social network analysis is very useful in discovering the embedded knowledge in social network structures, which is applicable in many practical domains including homeland security, publish safety, epidemiology, public health, electronic commerce, marketing, and social science. However, social network data is usually distributed and no single organization is able to capture the global social network. For example, a law enforcement unit in Region A has the criminal social network data of her region; similarly, another law enforcement unit in Region B has another criminal social network data of Region B. Unfortunately, due the privacy concerns, these law enforcement units may not be allowed to share the data, and therefore, neither of them can benefit by analyzing the integrated social network that combines the data from the social networks in Region A and Region B. In this chapter, we discuss aspects of sharing the insensitive and generalized information of social networks to support social network analysis while preserving the privacy at the same time. We discuss the generalization approach to construct a generalized social network in which only insensitive and generalized information is shared. We will also discuss the integration of the generalized information and how it can satisfy a prescribed level of privacy leakage tolerance which is measured independently to the privacy-preserving techniques.
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Details
- Title
- A Generalized Approach for Social Network Integration and Analysis with Privacy Preservation
- Creators
- Chris Yang - Drexel Univ, Philadelphia, PA 19104 USABhavani Thuraisingham - The University of Texas at Dallas
- Contributors
- W W Chu (Editor)
- Publication Details
- Data Mining and Knowledge Discovery for Big Data
- Series
- Studies in Big Data
- Publisher
- Springer Nature; BERLIN
- Number of pages
- 22
- Resource Type
- Book chapter
- Language
- English
- Academic Unit
- Information Science (Informatics)
- Web of Science ID
- WOS:000344730000009
- Scopus ID
- 2-s2.0-85132885207
- Other Identifier
- 991019182649804721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Interdisciplinary Applications