Studying Preferences and Concerns about Information Disclosure in Email Notifications

People receive dozens, or hundreds, of notifications per day and each notification poses some risk of accidental information disclosure in the presence of others; onlookers may see notifications on a mobile phone lock screen, on the periphery of a desktop or laptop display. We quantify the prevalence of these accidental disclosures in the context of email notifications, and we study people's relevant preferences and concerns. Our results are compiled from a retrospective survey of 131 respondents, and a contextual-labeling study where 169 participants labeled 1,040 meeting-email pairs. We find that, for 53% of people, at least 1 in 10 email notifications poses an information disclosure risk, and the real or perceived severity of these risks depend both on user characteristics and the meeting or email attributes. We conclude by exploring machine learning for predicting people's comfort levels, and we present implications for the design of future social-context aware notification systems.

[1]  Jinyoung Kim,et al.  "You can't block people offline": examining how facebook's affordances shape the disclosure process , 2014, CSCW.

[2]  Harry Hochheiser,et al.  Research Methods for Human-Computer Interaction , 2008 .

[3]  Allison Woodruff,et al.  Would a Privacy Fundamentalist Sell Their DNA for $1000 ... If Nothing Bad Happened as a Result? The Westin Categories, Behavioral Intentions, and Consequences , 2014, SOUPS.

[4]  Yuan Li,et al.  Theories in online information privacy research: A critical review and an integrated framework , 2012, Decis. Support Syst..

[5]  Eric Horvitz,et al.  Coordinates: Probabilistic Forecasting of Presence and Availability , 2002, UAI.

[6]  Christopher G. Atkeson,et al.  Predicting human interruptibility with sensors , 2005, TCHI.

[7]  Edward Lank,et al.  Privacy Personas: Clustering Users via Attitudes and Behaviors toward Security Practices , 2016, CHI.

[8]  Oliver Günther,et al.  Privacy in e-commerce: stated preferences vs. actual behavior , 2005, CACM.

[9]  Niels Henze,et al.  Design Guidelines for Notifications on Smart TVs , 2016, TVX.

[10]  Kirstie Hawkey,et al.  Enhancing Mobile Content Privacy with Proxemics Aware Notifications and Protection , 2016, CHI.

[11]  Alireza Sahami Shirazi,et al.  Large-scale assessment of mobile notifications , 2014, CHI.

[12]  Steve Benford,et al.  Investigating episodes of mobile phone activity as indicators of opportune moments to deliver notifications , 2011, Mobile HCI.

[13]  Airi Lampinen,et al.  We're in it together: interpersonal management of disclosure in social network services , 2011, CHI.

[14]  Mark S. Ackerman,et al.  Privacy in e-commerce: examining user scenarios and privacy preferences , 1999, EC '99.

[15]  Lujo Bauer,et al.  Some Recipes Can Do More Than Spoil Your Appetite: Analyzing the Security and Privacy Risks of IFTTT Recipes , 2017, WWW.

[16]  Alfred Kobsa,et al.  Dimensionality of information disclosure behavior , 2013, Int. J. Hum. Comput. Stud..

[17]  Bettina Berendt,et al.  E-privacy in 2nd generation E-commerce: privacy preferences versus actual behavior , 2001, EC '01.

[18]  Christopher D. Manning,et al.  Incorporating Non-local Information into Information Extraction Systems by Gibbs Sampling , 2005, ACL.

[19]  Thomas K. Landauer,et al.  Research Methods in Human-Computer Interaction , 1988 .

[20]  Brian P. Bailey,et al.  Towards an index of opportunity: understanding changes in mental workload during task execution , 2004, CHI.

[21]  Mary Czerwinski,et al.  Notification, Disruption, and Memory: Effects of Messaging Interruptions on Memory and Performance , 2001, INTERACT.

[22]  Eric Horvitz,et al.  Bayesphone: Precomputation of Context-Sensitive Policies for Inquiry and Action in Mobile Devices , 2005, User Modeling.

[23]  Daniel R. Horne,et al.  The Privacy Paradox: Personal Information Disclosure Intentions versus Behaviors , 2007 .

[24]  Paul Dourish,et al.  Collective Information Practice: Exploring Privacy and Security as Social and Cultural Phenomena , 2006, Hum. Comput. Interact..

[25]  Mirco Musolesi,et al.  InterruptMe: designing intelligent prompting mechanisms for pervasive applications , 2014, UbiComp.

[26]  Konstantin Beznosov,et al.  Sharing Health Information on Facebook: Practices, Preferences, and Risk Perceptions of North American Users , 2016, SOUPS.

[27]  Jonathan Grudin,et al.  A study of preferences for sharing and privacy , 2005, CHI Extended Abstracts.

[28]  Kim Sheehan,et al.  Toward a Typology of Internet Users and Online Privacy Concerns , 2002, Inf. Soc..

[29]  Tadayoshi Kohno,et al.  How Public Is My Private Life?: Privacy in Online Dating , 2017, WWW.

[30]  Kori Inkpen Quinn,et al.  Keeping up appearances: understanding the dimensions of incidental information privacy , 2006, CHI.

[31]  Florian Alt,et al.  Understanding Shoulder Surfing in the Wild: Stories from Users and Observers , 2017, CHI.

[32]  Tara Matthews,et al.  Location disclosure to social relations: why, when, & what people want to share , 2005, CHI.

[33]  Alessandro Acquisti,et al.  Imagined Communities: Awareness, Information Sharing, and Privacy on the Facebook , 2006, Privacy Enhancing Technologies.

[34]  Eric Horvitz,et al.  Notifications and awareness: a field study of alert usage and preferences , 2010, CSCW '10.

[35]  Eric Horvitz,et al.  Models of attention in computing and communication , 2003, Commun. ACM.

[36]  Joyce Ho,et al.  Using context-aware computing to reduce the perceived burden of interruptions from mobile devices , 2005, CHI.

[37]  Martin Pielot,et al.  An in-situ study of mobile phone notifications , 2014, MobileHCI '14.

[38]  Tadashi Okoshi,et al.  Reducing users' perceived mental effort due to interruptive notifications in multi-device mobile environments , 2015, UbiComp.

[39]  I. Ajzen,et al.  Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research , 1977 .

[40]  J. Pennebaker,et al.  The Psychological Meaning of Words: LIWC and Computerized Text Analysis Methods , 2010 .