國立虎尾科技大學 |

Leveraging Privacy in Data Analysis.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Leveraging Privacy in Data Analysis./
作者:	Rogers, Ryan Michael.
面頁冊數:	1 online resource (229 pages)
附註:	Source: Dissertation Abstracts International, Volume: 78-12(E), Section: B.
Contained By:	Dissertation Abstracts International78-12B(E).
標題:	Computer science. -
電子資源:	click for full text (PQDT)
ISBN:	9780355110234

Leveraging Privacy in Data Analysis.
Rogers, Ryan Michael.

Leveraging Privacy in Data Analysis. - 1 online resource (229 pages)

Source: Dissertation Abstracts International, Volume: 78-12(E), Section: B.

Thesis (Ph.D.)--University of Pennsylvania, 2017.

Includes bibliographical references

Data analysis is inherently adaptive, where previous results may influence which tests are carried out on a single dataset as part of a series of exploratory analyses. Unfortunately, classical statistical tools break down once the choice of analysis may depend on the dataset, which leads to overfitting and spurious conclusions. In this dissertation we put constraints on what type of analyses can be used adaptively on the same dataset in order to ensure valid conclusions are made. Following a line of work initiated from Dwork et al. [2015], we focus on extending the connection between differential privacy and adaptive data analysis.

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

Mode of access: World Wide Web

ISBN: 9780355110234Subjects--Topical Terms:

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

554714
Electronic books.

Leveraging Privacy in Data Analysis.
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500 $a Source: Dissertation Abstracts International, Volume: 78-12(E), Section: B.
500 $a Advisers: Michael Kearns; Aaron Roth.
502 $a Thesis (Ph.D.)--University of Pennsylvania, 2017.
504 $a Includes bibliographical references
520 $a Data analysis is inherently adaptive, where previous results may influence which tests are carried out on a single dataset as part of a series of exploratory analyses. Unfortunately, classical statistical tools break down once the choice of analysis may depend on the dataset, which leads to overfitting and spurious conclusions. In this dissertation we put constraints on what type of analyses can be used adaptively on the same dataset in order to ensure valid conclusions are made. Following a line of work initiated from Dwork et al. [2015], we focus on extending the connection between differential privacy and adaptive data analysis.
520 $a Our first contribution follows work presented in Rogers et al. [2016]. We generalize and unify previous works in the area by showing that the generalization properties of (approximately) differentially private algorithms can be used to give valid p-value corrections in adaptive hypothesis testing while recovering results for statistical and low-sensitivity queries. One of the main benefits of differential privacy is that it composes, i.e. the combination of several differentially private algorithms is itself differentially private and the privacy parameters degrade sublinearly. However, we can only apply the composition theorems when the privacy parameters are all fixed up front. Our second contribution then presents a framework for obtaining composition theorems when the privacy parameters, along with the number of procedures that are to be used, need not be fixed up front and can be adjusted adaptively Rogers et al. [2016]. These contributions are only useful if there actually exists some differentially private procedures that a data analyst would want to use. Hence, we present differentially private hypothesis tests for categorical data based on the classical chi-square hypothesis tests (Gaboardi et al. [2016], Kifer Rogers [2017]).
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
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710 2 $a University of Pennsylvania. $b Applied Mathematics and Computational Science. $3 1180480
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