Privacy-preservation for gradient descent methods

Li Wan; Wee Keong Ng; Shuguo Han; Vincent Cheng-Siong Lee

Privacy-preservation for gradient descent methods

Li Wan, Wee Keong Ng, Shuguo Han, Vincent Cheng-Siong Lee

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

Original language	English
Title of host publication	Proceedings of the Thirteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Editors	Pavel Berkhin, Rich Cruana, Xindong Wu, Scott Gaffney
Place of Publication	New York NY USA
Publisher	Association for Computing Machinery (ACM)
Pages	775 - 783
Number of pages	9
ISBN (Print)	9781595937070
Publication status	Published - 2007
Event	ACM International Conference on Knowledge Discovery and Data Mining 2007 - San Jose, United States of America Duration: 12 Aug 2007 → 15 Aug 2007 Conference number: 13th https://dl.acm.org/doi/proceedings/10.1145/1281192

Conference

Conference	ACM International Conference on Knowledge Discovery and Data Mining 2007
Abbreviated title	KDD 2007
Country/Territory	United States of America
City	San Jose
Period	12/08/07 → 15/08/07
Internet address	https://dl.acm.org/doi/proceedings/10.1145/1281192

Cite this

@inproceedings{ca8aa6330e63486ca68a3b01027ce157,

title = "Privacy-preservation for gradient descent methods",

author = "Li Wan and Ng, {Wee Keong} and Shuguo Han and Lee, {Vincent Cheng-Siong}",

year = "2007",

language = "English",

isbn = "9781595937070",

pages = "775 -- 783",

editor = "Pavel Berkhin and Rich Cruana and Xindong Wu and Scott Gaffney",

booktitle = "Proceedings of the Thirteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",

publisher = "Association for Computing Machinery (ACM)",

address = "United States of America",

note = "ACM International Conference on Knowledge Discovery and Data Mining 2007, KDD 2007 ; Conference date: 12-08-2007 Through 15-08-2007",

url = "https://dl.acm.org/doi/proceedings/10.1145/1281192",

}

Wan, L, Ng, WK, Han, S & Lee, VC-S 2007, Privacy-preservation for gradient descent methods. in P Berkhin, R Cruana, X Wu & S Gaffney (eds), Proceedings of the Thirteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery (ACM), New York NY USA, pp. 775 - 783, ACM International Conference on Knowledge Discovery and Data Mining 2007, San Jose, California, United States of America, 12/08/07.

Privacy-preservation for gradient descent methods. / Wan, Li; Ng, Wee Keong; Han, Shuguo et al.
Proceedings of the Thirteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ed. / Pavel Berkhin; Rich Cruana; Xindong Wu; Scott Gaffney. New York NY USA: Association for Computing Machinery (ACM), 2007. p. 775 - 783.

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

TY - GEN

T1 - Privacy-preservation for gradient descent methods

AU - Wan, Li

AU - Ng, Wee Keong

AU - Han, Shuguo

AU - Lee, Vincent Cheng-Siong

N1 - Conference code: 13th

PY - 2007

Y1 - 2007

M3 - Conference Paper

SN - 9781595937070

SP - 775

EP - 783

BT - Proceedings of the Thirteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

A2 - Berkhin, Pavel

A2 - Cruana, Rich

A2 - Wu, Xindong

A2 - Gaffney, Scott

PB - Association for Computing Machinery (ACM)

CY - New York NY USA

T2 - ACM International Conference on Knowledge Discovery and Data Mining 2007

Y2 - 12 August 2007 through 15 August 2007

ER -