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Exploring the relationship between dimensionality reduction and private data release

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Publication date4/12/2018
Host publication2018 IEEE 23rd Pacific Rim International Symposium on Dependable Computing (PRDC)
PublisherIEEE
Pages25-33
Number of pages9
ISBN (Electronic)9781538657003
ISBN (Print)9781538657010
<mark>Original language</mark>English

Abstract

It is important to facilitate data sharing between data owners and data analysts as data owners do not always have the ability to process and analyze data. For example, governments around the world are starting to release collected data to the public to leverage data analysis competence of the crowd. However, some privacy leakage incidents have made the public to rediscover the importance of privacy protection, leading to new privacy regulations. In existing researches dimensionality reduction has played an important role in private data release mechanisms to improve utility but its influence on privacy protection has never been examined. In this study, we perform a series of experiments and found that dimensionality reduction could provide similar privacy protection effects as K-anonymity mechanisms, and it could work as a preprocessor of K-anonymity process to it to reduce the generalization and suppression needed.