Deriving Privacy Settings for Location Sharing: Are Context Factors Always the Best Choice?

Research has observed context factors like occasion and time as influential factors for predicting whether or not to share a location with online friends. In other domains like social networks, personality was also found to play an important role. Furthermore, users are seeking a fine-grained disclosement policy that also allows them to display an obfuscated location, like the center of the current city, to some of their friends. In this paper, we observe which context factors and personality measures can be used to predict the correct privacy level out of seven privacy levels, which include obfuscation levels like center of the street or current city. Our results show that a prediction is possible with a precision 20% better than a constant value. We will give design indications to determine which context factors should be recorded, and how much the precision can be increased if personality and privacy measures are recorded using either a questionnaire or automated text analysis.

[1]  Tristan Henderson,et al.  Short paper: "here i am, now pay me!": privacy concerns in incentivised location-sharing systems , 2014, WiSec '14.

[2]  Lorrie Faith Cranor,et al.  An Investigation into Facebook Friend Grouping , 2011, INTERACT.

[3]  Sameer Patil,et al.  My Privacy Policy: Exploring End-user Specification of Free-form Location Access Rules , 2012, Financial Cryptography Workshops.

[4]  Marie-Francine Moens,et al.  Computational personality recognition in social media , 2016, User Modeling and User-Adapted Interaction.

[5]  Antonio Krüger,et al.  Fine-Grained Privacy Setting Prediction Using a Privacy Attitude Questionnaire and Machine Learning , 2017, INTERACT.

[6]  Michael D. Buhrmester,et al.  Amazon's Mechanical Turk , 2011, Perspectives on psychological science : a journal of the Association for Psychological Science.

[7]  Jacqueline J. Meulman,et al.  Prediction and classification in nonlinear data analysis: Something old, something new, something borrowed, something blue , 2003 .

[8]  S. Gosling,et al.  A very brief measure of the Big-Five personality domains , 2003 .

[9]  Frederic Raber,et al.  URetail: Privacy User Interfaces for Intelligent Retail Stores , 2017, INTERACT.

[10]  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.

[11]  Eric Gilbert,et al.  What (or Who) Is Public?: Privacy Settings and Social Media Content Sharing , 2017, CSCW.

[12]  A. Joinson,et al.  Development of measures of online privacy concern and protection for use on the Internet , 2007, J. Assoc. Inf. Sci. Technol..

[13]  J. Block A contrarian view of the five-factor approach to personality description. , 1995, Psychological bulletin.

[14]  Matthias Hollick,et al.  Exploring user preferences for privacy interfaces in mobile sensing applications , 2012, MUM.

[15]  Matthias Hollick,et al.  Raising User Awareness about Privacy Threats in Participatory Sensing Applications through Graphical Warnings , 2013, MoMM '13.

[16]  Bashar Nuseibeh,et al.  On the impact of real-time feedback on users' behaviour in mobile location-sharing applications , 2010, SOUPS.

[17]  Ponnurangam Kumaraguru,et al.  Privacy Indexes: A Survey of Westin's Studies , 2005 .

[18]  Lorrie Faith Cranor,et al.  Understanding and capturing people’s privacy policies in a mobile social networking application , 2009, Personal and Ubiquitous Computing.

[19]  John Krumm,et al.  Exploring end user preferences for location obfuscation, location-based services, and the value of location , 2010, UbiComp.

[20]  Susan B. Barnes,et al.  A privacy paradox: Social networking in the United States , 2006, First Monday.

[21]  Yong Liu,et al.  Do I Do What I Say?: Observed Versus Stated Privacy Preferences , 2007, INTERACT.

[22]  Norman M. Sadeh,et al.  Capturing social networking privacy preferences: can default policies help alleviate tradeoffs between expressiveness and user burden? , 2009, Privacy Enhancing Technologies.

[23]  Kang G. Shin,et al.  Location Privacy Protection for Smartphone Users , 2014, CCS.

[24]  H. Jeff Smith,et al.  Information Privacy: Measuring Individuals' Concerns About Organizational Practices , 1996, MIS Q..

[25]  Lorrie Faith Cranor,et al.  Who's viewed you?: the impact of feedback in a mobile location-sharing application , 2009, CHI.

[26]  Eben M. Haber,et al.  Making Use of Derived Personality: The Case of Social Media Ad Targeting , 2015, ICWSM.

[27]  Lorrie Faith Cranor,et al.  Capturing location-privacy preferences: quantifying accuracy and user-burden tradeoffs , 2011, Personal and Ubiquitous Computing.

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

[29]  P. Costa,et al.  Revised NEO Personality Inventory (NEO-PI-R) and NEO-Five-Factor Inventory (NEO-FFI) , 1992 .

[30]  Naresh K. Malhotra,et al.  Internet Users' Information Privacy Concerns (IUIPC): The Construct, the Scale, and a Causal Model , 2004, Inf. Syst. Res..

[31]  Antonio Krüger,et al.  Towards Understanding the Influence of Personality on Mobile App Permission Settings , 2017, INTERACT.

[32]  Antonio Krüger,et al.  Privacy Perceiver: Using Social Network Posts to Derive Users' Privacy Measures , 2018, UMAP.

[33]  Frederic Raber,et al.  The 'Retailio' Privacy Wizard: Assisting Users with Privacy Settings for Intelligent Retail Stores , 2018 .

[34]  S. Srivastava,et al.  The Big Five Trait taxonomy: History, measurement, and theoretical perspectives. , 1999 .