國立虎尾科技大學 |

Practical Differential Privacy for High-dimensional and Graph Data.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Practical Differential Privacy for High-dimensional and Graph Data./
作者:	Day, Wei-Yen.
面頁冊數:	1 online resource (147 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-03(E), Section: B.
Contained By:	Dissertation Abstracts International79-03B(E).
標題:	Computer science. -
電子資源:	click for full text (PQDT)
ISBN:	9780355254556

Practical Differential Privacy for High-dimensional and Graph Data.
Day, Wei-Yen.

Practical Differential Privacy for High-dimensional and Graph Data. - 1 online resource (147 pages)

Source: Dissertation Abstracts International, Volume: 79-03(E), Section: B.

Thesis (Ph.D.)

Includes bibliographical references

Differential privacy has emerged as a de facto standard of privacy notion. It is widely adopted in various domains, including data publishing, data mining, and interactive database queries. However, applying differential privacy on complex data still remains challenging due to the huge change of sensitivity. In this dissertation, we introduce three major topics about publishing information with high-dimensional and graph data under differential privacy. The first topic discusses the possibility of publishing column counts from high-dimensional data under differential privacy, with a proposed technique called sensitivity control. The idea is to limit the contribution of data records such that sensitivity can be limited. We solve the challenge of balancing the sensitivity level and remaining data utility. The second topic aims at solving the problem of high-dimensional data classification with differential privacy. We propose PrivWalk, a greedily walking algorithm that iteratively searches the optimal model and also automatically determines the number of steps given a privacy budget. In the third topic, we advance the technique to publish degree distribution from a graph under node-differential privacy. We develop a projection technique that preserves that most utility and also limits the sensitivity. Based on the projection method, we propose two approaches for publishing degree histograms. The experiments of the three topics demonstrate that our proposed techniques significantly improve the existing state of the art, making differential privacy on high-dimensional and graph data practical.

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

Mode of access: World Wide Web

ISBN: 9780355254556Subjects--Topical Terms:

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

554714
Electronic books.

Practical Differential Privacy for High-dimensional and Graph Data.
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500 $a Source: Dissertation Abstracts International, Volume: 79-03(E), Section: B.
500 $a Adviser: Ninghui Li.
502 $a Thesis (Ph.D.) $c Purdue University $d 2017.
504 $a Includes bibliographical references
520 $a Differential privacy has emerged as a de facto standard of privacy notion. It is widely adopted in various domains, including data publishing, data mining, and interactive database queries. However, applying differential privacy on complex data still remains challenging due to the huge change of sensitivity. In this dissertation, we introduce three major topics about publishing information with high-dimensional and graph data under differential privacy. The first topic discusses the possibility of publishing column counts from high-dimensional data under differential privacy, with a proposed technique called sensitivity control. The idea is to limit the contribution of data records such that sensitivity can be limited. We solve the challenge of balancing the sensitivity level and remaining data utility. The second topic aims at solving the problem of high-dimensional data classification with differential privacy. We propose PrivWalk, a greedily walking algorithm that iteratively searches the optimal model and also automatically determines the number of steps given a privacy budget. In the third topic, we advance the technique to publish degree distribution from a graph under node-differential privacy. We develop a projection technique that preserves that most utility and also limits the sensitivity. Based on the projection method, we propose two approaches for publishing degree histograms. The experiments of the three topics demonstrate that our proposed techniques significantly improve the existing state of the art, making differential privacy on high-dimensional and graph data practical.
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
655 7 $a Electronic books. $2 local $3 554714
690 $a 0984
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