Full Citation

Title: Privacy in Databases

Citation Type: Dissertation/Thesis

Publication Year: 2012

ISBN:

ISSN:

DOI:

NSFID:

PMCID:

PMID:

Abstract: In the current digital age more and more data is being collected, yet it’s unclear how to assure adequate privacy. The question of how to analyze large amounts of data while preserving privacy now prevails more than ever. In the course of history there have been many failed attempts, showing that reasoning about privacy is fraught with pitfalls. This caused an increased interest in a mathematically robust definition of privacy. We will prove that absolute disclosure prevention is impossible. In other words, a person that gains access to a database can always breach the privacy of an individual. This motivated the move to assuring relative disclosure prevention. One of the most promising definitions in this area is differential privacy. It addresses all the currently known attacks, has plenty of practical implementations, and knows many extensions that make it applicable in a wide range of situations. Networked data also poses challenging privacy issues. Both active and passive attacks, where the underlying structure of the network is used to de-anonymize individuals, are discussed in detail. Degree anonymization and algorithms to create a degree anonymous graphs will also be given. Al- though still an active research topic, privacy of networked data is considered increasingly important given the rise of social media.

Url: http://papers.mathyvanhoef.com/masterthesis.pdf

User Submitted?: No

Authors: Vanhoef, Mathy

Institution: Universiteit Hasselt

Department: Computer Science

Advisor:

Degree: master in computer science/databases

Publisher Location:

Pages: 184

Data Collections: IPUMS USA

Topics: Population Data Science

Countries: United States