會議論文
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 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/120181 ) |