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Exploring the relationship between dimensionality reduction and private data release

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Exploring the relationship between dimensionality reduction and private data release. / Tai, B.-C.; Li, S.-C.; Huang, Y.; Suri, Neeraj; Wang, P.-C.

2018 IEEE 23rd Pacific Rim International Symposium on Dependable Computing (PRDC). IEEE, 2018. p. 25-33.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Harvard

Tai, B-C, Li, S-C, Huang, Y, Suri, N & Wang, P-C 2018, Exploring the relationship between dimensionality reduction and private data release. in 2018 IEEE 23rd Pacific Rim International Symposium on Dependable Computing (PRDC). IEEE, pp. 25-33. https://doi.org/10.1109/PRDC.2018.00013

APA

Tai, B-C., Li, S-C., Huang, Y., Suri, N., & Wang, P-C. (2018). Exploring the relationship between dimensionality reduction and private data release. In 2018 IEEE 23rd Pacific Rim International Symposium on Dependable Computing (PRDC) (pp. 25-33). IEEE. https://doi.org/10.1109/PRDC.2018.00013

Vancouver

Tai B-C, Li S-C, Huang Y, Suri N, Wang P-C. Exploring the relationship between dimensionality reduction and private data release. In 2018 IEEE 23rd Pacific Rim International Symposium on Dependable Computing (PRDC). IEEE. 2018. p. 25-33 https://doi.org/10.1109/PRDC.2018.00013

Author

Tai, B.-C. ; Li, S.-C. ; Huang, Y. ; Suri, Neeraj ; Wang, P.-C. / Exploring the relationship between dimensionality reduction and private data release. 2018 IEEE 23rd Pacific Rim International Symposium on Dependable Computing (PRDC). IEEE, 2018. pp. 25-33

Bibtex

@inproceedings{241c4464ebff4aae9ca8d34e4db641c7,
title = "Exploring the relationship between dimensionality reduction and private data release",
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.",
keywords = "Dimensionality reduction, K-Anonymity, Private data release, Computer programming, Computer science, Data analysts, Generalization and suppressions, Privacy leakages, Privacy protection, Privacy regulation, Private data, Data reduction",
author = "B.-C. Tai and S.-C. Li and Y. Huang and Neeraj Suri and P.-C. Wang",
year = "2018",
month = dec,
day = "4",
doi = "10.1109/PRDC.2018.00013",
language = "English",
isbn = "9781538657010",
pages = "25--33",
booktitle = "2018 IEEE 23rd Pacific Rim International Symposium on Dependable Computing (PRDC)",
publisher = "IEEE",

}

RIS

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 -