자료유형  

 학위논문

Control Number  

0017163537

International Standard Book Number  

9798384016533

Dewey Decimal Classification Number  

004

Main Entry-Personal Name  

Nanayakkara, Priyanka.

Publication, Distribution, etc. (Imprint  

[S.l.] : Northwestern University., 2024

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2024

Physical Description  

170 p.

General Note  

Source: Dissertations Abstracts International, Volume: 86-02, Section: B.

General Note  

Advisor: Hullman, Jessica.

Dissertation Note  

Thesis (Ph.D.)--Northwestern University, 2024.

Summary, Etc.  

요약It is often useful to learn patterns about a population while protecting individuals' privacy.Differential privacy is a state-of-the-art framework for limiting how much information is revealed about individuals during analysis. Under differential privacy, statistical noise is injected into analyses to obscure individual contributions while maintaining overall patterns. The amount of noise is calibrated by a unit-less privacy loss parameter, ϵ, which controls a tradeoff between strength of privacy protection and accuracy of estimates. This tradeoff is difficult to reason about because it is probabilistic, non-linear, and inherently value-laden. However, people across the data ecosystem must be able to effectively reason about it in order for differential privacy to be broadly usable.Moreover, applying differential privacy in real-world settings introduces a host of socio technical challenges around communicating its guarantees and its use more broadly.To make differential privacy usable, we develop human-centered tools for data curators,data analysts, and data subjects to reason about differential privacy. Specifically, we present (1) an interactive visualization interface for data curators setting ϵ, (2) an interactive paradigm instantiated in an interactive visualization interface for analysts to spend ϵ efficiently during exploratory analysis, and (3) explanations of ϵ's privacy guarantees for data subjects. Furthermore, we present(4) an analysis of debates around the U.S. Census Bureau's use of differential privacy for the 2020 census to propose communication strategies that can facilitate more productive discussions and ensure smoother deployments going forward. In sum, this dissertation aims to increase the usability of differential privacy as a promising approach with potential to promote data privacy.

Subject Added Entry-Topical Term  

Computer science.

Subject Added Entry-Topical Term  

Communication.

Index Term-Uncontrolled  

Data privacy

Index Term-Uncontrolled  

Differential privacy

Index Term-Uncontrolled  

Privacy protection

Index Term-Uncontrolled  

Usability

Added Entry-Corporate Name  

Northwestern University Computer Science

Host Item Entry  

Dissertations Abstracts International. 86-02B.

Electronic Location and Access  

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Control Number  

joongbu:655526