Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
TY - GEN
T1 - Exploring the relationship between dimensionality reduction and private data release
AU - Tai, B.-C.
AU - Li, S.-C.
AU - Huang, Y.
AU - Suri, Neeraj
AU - Wang, P.-C.
PY - 2018/12/4
Y1 - 2018/12/4
N2 - 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.
AB - 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.
KW - Dimensionality reduction
KW - K-Anonymity
KW - Private data release
KW - Computer programming
KW - Computer science
KW - Data analysts
KW - Generalization and suppressions
KW - Privacy leakages
KW - Privacy protection
KW - Privacy regulation
KW - Private data
KW - Data reduction
U2 - 10.1109/PRDC.2018.00013
DO - 10.1109/PRDC.2018.00013
M3 - Conference contribution/Paper
SN - 9781538657010
SP - 25
EP - 33
BT - 2018 IEEE 23rd Pacific Rim International Symposium on Dependable Computing (PRDC)
PB - IEEE
ER -