@inproceedings{e5a970190f7046b9ac7aa1c29c5592d7,
title = "A/P(rivacy) Testing: Assessing applications for social and institutional privacy",
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.",
keywords = "A/B testing, Controlled experiments, Privacy, Privacy-by-design, User-centered design",
author = "Oshrat Ayalon and Eran Toch",
note = "Publisher Copyright: {\textcopyright} 2019 Copyright is held by the author/owner(s).; 2019 CHI Conference on Human Factors in Computing Systems, CHI EA 2019 ; Conference date: 04-05-2019 Through 09-05-2019",
year = "2019",
month = may,
day = "2",
doi = "10.1145/3290607.3312972",
language = "American English",
series = "Conference on Human Factors in Computing Systems - Proceedings",
publisher = "Association for Computing Machinery",
booktitle = "CHI EA 2019 - Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems",
address = "United States",
}