Jessica Staddon

Indirect content privacy surveys: measuring privacy without asking about it

The strong emotional reaction elicited by privacy issues is well documented (e.g., [12, 8]). The emotional aspect of privacy makes it difficult to evaluate privacy concern, and directly asking about a privacy issue may result in an emotional reaction and a biased response. This effect may be partly responsible for the dramatic privacy concern ratings coming from recent surveys, ratings that often seem to be at odds with user behavior. In this paper we propose indirect techniques for measuring content privacy concerns through surveys, thus hopefully diminishing any emotional response. We present a design for indirect surveys and test the designs use as (1) a means to measure relative privacy concerns across content types, (2) a tool for predicting unwillingness to share content (a possible indicator of privacy concern), and (3) a gauge for two underlying dimensions of privacy - content importance and the willingness to share content. Our evaluation consists of 3 surveys, taken by 200 users each, in which privacy is never asked about directly, but privacy warnings are issued with increasing escalation in the instructions and individual question-wording. We demonstrate that this escalation results in statistically and practically significant differences in responses to individual questions. In addition, we compare results against a direct privacy survey and show that rankings of privacy concerns are increasingly preserved as privacy language increases in the indirect surveys, thus indicating our mapping of the indirect questions to privacy ratings is accurately reflecting privacy concerns.

I hereby leave my email to...: Data Usage Control and the Digital Estate

In most data control scenarios there is the opportunity for oversight by those who, while perhaps not directly involved in the creation of the data, understand the intended usage of the data. We argue that due to the proliferation of online data and our aging population, data owners will increasingly face requests for data access and usage when such oversight is not present because the original data owner/creator is unavailable (e.g. because of death or incapacitation). We outline the technical and user experience challenges in supporting this data usage scenario, focusing on the online service setting, and highlight some emerging research problems.

Exploring the benefits and uses of web analytics tools for non-transactional websites

Website owners use web analytics tools to better understand their visitors for a range of purposes. However, there is limited understanding of how owners of non-transactional websites use and benefit from web analytics. Through semi-structured interviews (n=18) with non-transactional web analytics users we explore these uses and benefits. Participants tend to use web analytics to improve site design, by optimizing site structure, content, or technical specifications. However, participants also use web analytics to understand their audiences without a directed purpose, often for curiosity or entertainment. The design of web analytics tools should account for this full range of functionality.

Predicting Mobile App Privacy Preferences with Psychographics

Using a multi-country data set of over 600 survey participants, we show that psychographics and various attributes of the mobile app context are predictive of user privacy preferences. In particular, we find that a users decision-making style is predictive of their privacy preferences, with users who are more independent decision makers tending to be more conservative about sharing personal information. We also provide additional insights into several of the privacy preference clusters identified in Lin et al.\@ 2014 by finding that users who are more privacy-concerned tend to be more knowledgeable of app privacy risks and more attentive to app permissions requests, and those individuals with less concern are more likely to trust well-known app companies.

A framework for privacy-conducive recommendations

Recommendations and advertisements based on consumer behavior patterns are increasingly prevalent, yet carry significant privacy concerns. We propose an easily implemented alternative framework in which publicly available Web data is mined to discover product preference associations.