國立虎尾科技大學 |

Learning Information from Data while Preserving Differential Privacy.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Learning Information from Data while Preserving Differential Privacy./
Author:	Ji, Zhanglong.
Description:	1 online resource (136 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 79-01(E), Section: B.
Contained By:	Dissertation Abstracts International79-01B(E).
Subject:	Computer science. -
Online resource:	click for full text (PQDT)
ISBN:	9780355313840

Learning Information from Data while Preserving Differential Privacy.
Ji, Zhanglong.

Learning Information from Data while Preserving Differential Privacy. - 1 online resource (136 pages)

Source: Dissertation Abstracts International, Volume: 79-01(E), Section: B.

Thesis (Ph.D.)

Includes bibliographical references

In this dissertation, I am going to introduce my work on differentially private data mining. There are usually two types of privacy preserving data mining algorithms: query answering algorithms and data publishing algorithms. We will present three algorithms on two differentially private query answering problems in Chapter 3. The first problem is to return SNPs most correlated to a disease, and we provide two efficient algorithms which make an accurate but previously inefficient algorithm feasible. The second problem is to learn a model minimizing empirical risk while selecting features. Our algorithm beats start-of-the-art algorithms. Then we present three algorithms on differentially private data publishing under two scenarios in Chapter 4. The first algorithm assumes existence of public data, and assigns weights to the public data so that they are statistically similar to the private data. Analysis on the weighted public data is more accurate than doing analysis on the private data directly. The two following algorithms assume that published data are for supervised learning (one algorithm for classification and the other for regression), and that the prediction rules are continuous with respect to predictors. The data these algorithms published perform very well on learning tasks.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355313840Subjects--Topical Terms:

573171
Computer science.
Index Terms--Genre/Form:

554714
Electronic books.

Learning Information from Data while Preserving Differential Privacy.
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100 1 $a Ji, Zhanglong. $3 1182250
245 1 0 $a Learning Information from Data while Preserving Differential Privacy.
264 0 $c 2017
300 $a 1 online resource (136 pages)
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500 $a Source: Dissertation Abstracts International, Volume: 79-01(E), Section: B.
500 $a Advisers: Charles Elkan; Lucila Ohno-Machado.
502 $a Thesis (Ph.D.) $c University of California, San Diego $d 2017.
504 $a Includes bibliographical references
520 $a In this dissertation, I am going to introduce my work on differentially private data mining. There are usually two types of privacy preserving data mining algorithms: query answering algorithms and data publishing algorithms. We will present three algorithms on two differentially private query answering problems in Chapter 3. The first problem is to return SNPs most correlated to a disease, and we provide two efficient algorithms which make an accurate but previously inefficient algorithm feasible. The second problem is to learn a model minimizing empirical risk while selecting features. Our algorithm beats start-of-the-art algorithms. Then we present three algorithms on differentially private data publishing under two scenarios in Chapter 4. The first algorithm assumes existence of public data, and assigns weights to the public data so that they are statistically similar to the private data. Analysis on the weighted public data is more accurate than doing analysis on the private data directly. The two following algorithms assume that published data are for supervised learning (one algorithm for classification and the other for regression), and that the prediction rules are continuous with respect to predictors. The data these algorithms published perform very well on learning tasks.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 573171
650 4 $a Artificial intelligence. $3 559380
655 7 $a Electronic books. $2 local $3 554714
690 $a 0984
690 $a 0800
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a University of California, San Diego. $b Computer Science. $3 1182161
773 0 $t Dissertation Abstracts International $g 79-01B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10601413 $z click for full text (PQDT)