Exploring the Relationship Between Dimensionality Reduction and Private Data Release
學年 107
學期 1
發表日期 2018-12-04
作品名稱 Exploring the Relationship Between Dimensionality Reduction and Private Data Release
作品名稱（其他語言）
著者 Bo-Chen Tai; Szu-Chuang Li; Yennun Huang; Neeraj Suri
作品所屬單位
出版者
會議名稱 IEEE PRDC 2018
會議地點 Taipei, Taiwan
摘要 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.
關鍵字 dimensionality reduction;k-anonymity;private data release
語言 en
收錄於
會議性質 國際
校內研討會地點 無
研討會時間 20181204~20181207
通訊作者
國別 TWN
公開徵稿
出版型式
出處 2018 IEEE 23rd Pacific Rim International Symposium on Dependable Computing (PRDC), p.25-33
相關連結

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