Symposium On Usable Privacy and Security

2nd Annual Privacy Personas and Segmentation (PPS) Workshop

The PPS workshop is an opportunity for researchers and practitioners to explore improved methods and tools for understanding privacy concerns, facilitating the construction of privacy personas and/or segmenting users on the basis of their diverse privacy attitudes, concerns, and behaviors. These characterizations may make it possible to better predict online behavior and disclosure, to personalize interfaces, to suggest appropriate default settings, and to measure shifts in public sentiment.

Program

Please note: Authors will be given a maximum of 18 minutes to present their papers, plus time for questions and discussion. Time limits will be rigorously enforced throughout the day by session chairs.

Scope and Focus

Scholars and practitioners have long been interested in understanding and measuring privacy attitudes and concerns, and their relationship with privacy behavior. Over time, an awareness has emerged of the importance of context in privacy concerns, and the complex relationship between concerns and actual behaviors. In recent years, proposals have been made to segment individuals into more granular and detailed categories of privacy concerns or behaviors, and classifying or predicting their privacy types, or personas. Such efforts have been motivated by the goals of better understanding the relationships between privacy attitudes, concerns and behaviors, and of helping end users make better privacy decisions.

The focus of the PPS workshop is to bring together researchers and practitioners interested in privacy personas and segmentation, to encourage a paradigm shift in the measurement, modeling, and characterization of privacy concerns which recognizes the complex interaction of factors influencing it. Those interested in participating should submit a research or position paper on a relevant topic. Topics of interest include, but are not limited to:

• The use of segmentations and personas for representing privacy concerns
• Critiques of existing approaches and explorations of the inherent limitations of privacy segmentations or privacy personas
• New paradigms and instruments for understanding, measuring, and modeling privacy concerns, including machine learning based approaches
• Evaluations and critiques of existing instruments for measuring privacy concerns
• The role of context, personality, experiences, and other traits in influencing privacy concerns
• The relationship between privacy concerns, attitudes, and behavior
• Algorithms and tools for providing personalized privacy recommendations
• Other topics related to measuring, modeling, and characterizing privacy concerns

Email inquiries may be sent to: bart.k@uci.edu or woodruff@google.com

Organizers

Alessandro Acquisti, Carnegie Mellon University
Bart Knijnenburg, University of California, Irvine
Norman Sadeh, Carnegie Mellon University
Allison Woodruff, Google