A/P(rivacy) Testing: Assessing applications for social and institutional privacy

Oshrat Ayalon; Eran Toch

doi:10.1145/3290607.3312972

A/P(rivacy) Testing: Assessing applications for social and institutional privacy

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

Abstract

The way information systems are designed has a crucial effect on users' privacy, but users are rarely involved in Privacy-by-Design processes. To bridge this gap, we investigate how User-Centered Design (UCD) methods can be used to improve the privacy of systems' designs. We present the process of developing A/P(rivacy) Testing, a platform that allows designers to compare several privacy designs alternatives, eliciting end-users' privacy perceptions of a tested system or a feature (Figure 1). We describe three online experiments, with 959 participants, in which we created and validated the reliability of a scale for Users' Perceived Systems' Privacy (UPSP), and used it to compare between privacy designs alternatives by using scenarios and different variants. We show that A/B testing is applicable for privacy purposes and that our scale is differentiating between designs that perceived as legitimate and designs that may violate users' expectations.

Original language	English
Title of host publication	CHI EA 2019 - Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems
Publisher	Association for Computing Machinery
ISBN (Electronic)	9781450359719
DOIs	https://doi.org/10.1145/3290607.3312972
State	Published - 2 May 2019
Externally published	Yes
Event	2019 CHI Conference on Human Factors in Computing Systems, CHI EA 2019 - Glasgow, United Kingdom Duration: 4 May 2019 → 9 May 2019

Publication series

Name	Conference on Human Factors in Computing Systems - Proceedings

Conference

Conference	2019 CHI Conference on Human Factors in Computing Systems, CHI EA 2019
Country/Territory	United Kingdom
City	Glasgow
Period	4/05/19 → 9/05/19

Bibliographical note

Publisher Copyright:
© 2019 Copyright is held by the author/owner(s).

Keywords

A/B testing
Controlled experiments
Privacy
Privacy-by-design
User-centered design

ASJC Scopus subject areas

Software
Human-Computer Interaction
Computer Graphics and Computer-Aided Design

Access to Document

10.1145/3290607.3312972

Cite this

Ayalon, O., & Toch, E. (2019). A/P(rivacy) Testing: Assessing applications for social and institutional privacy. In CHI EA 2019 - Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems Article 3312972 (Conference on Human Factors in Computing Systems - Proceedings). Association for Computing Machinery. https://doi.org/10.1145/3290607.3312972

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abstract = "The way information systems are designed has a crucial effect on users' privacy, but users are rarely involved in Privacy-by-Design processes. To bridge this gap, we investigate how User-Centered Design (UCD) methods can be used to improve the privacy of systems' designs. We present the process of developing A/P(rivacy) Testing, a platform that allows designers to compare several privacy designs alternatives, eliciting end-users' privacy perceptions of a tested system or a feature (Figure 1). We describe three online experiments, with 959 participants, in which we created and validated the reliability of a scale for Users' Perceived Systems' Privacy (UPSP), and used it to compare between privacy designs alternatives by using scenarios and different variants. We show that A/B testing is applicable for privacy purposes and that our scale is differentiating between designs that perceived as legitimate and designs that may violate users' expectations.",

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Ayalon, O & Toch, E 2019, A/P(rivacy) Testing: Assessing applications for social and institutional privacy. in CHI EA 2019 - Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems., 3312972, Conference on Human Factors in Computing Systems - Proceedings, Association for Computing Machinery, 2019 CHI Conference on Human Factors in Computing Systems, CHI EA 2019, Glasgow, United Kingdom, 4/05/19. https://doi.org/10.1145/3290607.3312972

A/P(rivacy) Testing: Assessing applications for social and institutional privacy. / Ayalon, Oshrat; Toch, Eran.
CHI EA 2019 - Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, 2019. 3312972 (Conference on Human Factors in Computing Systems - Proceedings).

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

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A/P(rivacy) Testing: Assessing applications for social and institutional privacy

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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