skip to main content

Explore Research Products in the NSF-PAR

Title: Privacy as a Planned Behavior: Effects of Situational Factors on Privacy Perceptions and Plans
To account for privacy perceptions and preferences in user models and develop personalized privacy systems, we need to understand how users make privacy decisions in various contexts. Existing studies of privacy perceptions and behavior focus on overall tendencies toward privacy, but few have examined the context-specific factors in privacy decision making. We conducted a survey on Mechanical Turk (N=401) based on the theory of planned behavior (TPB) to measure the way users’ perceptions of privacy factors and intent to disclose information are affected by three situational factors embodied hypothetical scenarios: information type, recipients’ role, and trust source. Results showed a positive relationship between subjective norms and perceived behavioral control, and between each of these and situational privacy attitude; all three constructs are significantly positively associated with intent to disclose. These findings also suggest that, situational factors predict participants’ privacy decisions through their influence on the TPB constructs.  more » « less
Award ID(s):
1657774
NSF-PAR ID:
10223377
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
UMAP ’21, June 21–25, 2021, Utrecht, Netherlands © 2021 Association for Computing Machinery.
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ( , Information and computer security)
    Furnell, Steven (Ed.)
    A huge amount of personal and sensitive data is shared on Facebook, which makes it a prime target for attackers. Adversaries can exploit third-party applications connected to a user’s Facebook profile (i.e., Facebook apps) to gain access to this personal information. Users’ lack of knowledge and the varying privacy policies of these apps make them further vulnerable to information leakage. However, little has been done to identify mismatches between users’ perceptions and the privacy policies of Facebook apps. We address this challenge in our work. We conducted a lab study with 31 participants, where we received data on how they share information in Facebook, their Facebook-related security and privacy practices, and their perceptions on the privacy aspects of 65 frequently-used Facebook apps in terms of data collection, sharing, and deletion. We then compared participants’ perceptions with the privacy policy of each reported app. Participants also reported their expectations about the types of information that should not be collected or shared by any Facebook app. Our analysis reveals significant mismatches between users’ privacy perceptions and reality (i.e., privacy policies of Facebook apps), where we identified over-optimism not only in users’ perceptions of information collection, but also on their self-efficacy in protecting their information in Facebook despite experiencing negative incidents in the past. To the best of our knowledge, this is the first study on the gap between users’ privacy perceptions around Facebook apps and the reality. The findings from this study offer directions for future research to address that gap through designing usable, effective, and personalized privacy notices to help users to make informed decisions about using Facebook apps. 
    more » « less
  2. ; ( , ARES 2021: The 16th International Conference on Availability, Reliability and Security)
    In order to create user-centric and personalized privacy management tools, the underlying models must account for individual users’ privacy expectations, preferences, and their ability to control their information sharing activities. Existing studies of users’ privacy behavior modeling attempt to frame the problem from a request’s perspective, which lack the crucial involvement of the information owner, resulting in limited or no control of policy management. Moreover, very few of them take into the consideration the aspect of correctness, explainability, usability, and acceptance of the methodologies for each user of the system. In this paper, we present a methodology to formally model, validate, and verify personalized privacy disclosure behavior based on the analysis of the user’s situational decision-making process. We use a model checking tool named UPPAAL to represent users’ self-reported privacy disclosure behavior by an extended form of finite state automata (FSA), and perform reachability analysis for the verification of privacy properties through computation tree logic (CTL) formulas. We also describe the practical use cases of the methodology depicting the potential of formal technique towards the design and development of user-centric behavioral modeling. This paper, through extensive amounts of experimental outcomes, contributes several insights to the area of formal methods and user-tailored privacy behavior modeling. 
    more » « less
  3. ; ; ; ; ; ( , 2018 IEEE World Congress on Services (SERVICES))
    In Location-Based Services (LBS), users are required to disclose their precise location information to query a service provider. An untrusted service provider can abuse those queries to infer sensitive information on a user through spatio-temporal and historical data analyses. Depicting the drawbacks of existing privacy-preserving approaches in LBS, we propose a user-centric obfuscation approach, called KLAP, based on the three fundamental obfuscation requirements: k number of locations, l-diversity, and privacy area preservation. Considering user's sensitivity to different locations and utilizing Real-Time Traffic Information (RTTI), KLAP generates a convex Concealing Region (CR) to hide user's location such that the locations, forming the CR, resemble similar sensitivity and are resilient against a wide range of inferences in spatio-temporal domain. For the first time, a novel CR pruning technique is proposed to significantly improve the delay between successive CR submissions. We carry out an experiment with a real dataset to show its effectiveness for sporadic, frequent, and continuous service use cases. 
    more » « less
  4. ; ; ( , Physics Education Research Conference 2022)
    Frank, Brian W. ; Jones, Dyan L. ; Ryan, Qing X. (Ed.)
    The ways in which physics majors make career decisions is a critical, yet understudied, aspect of the undergraduate experience. Such decisions are important to students, physics departments, and administrators. In this project, we specifically examine how students develop interests and intent to pursue specific subfields of physics by interviewing 13 physics majors from all years of study. The interviews examined factors that led students to choose their most preferred and least preferred subfields. Interviews leveraged the framework of Social Cognitive Career Theory, a model that describes how several constructs such as self-efficacy, learning experiences, and outcome expectations relate to decision-making. Findings highlight the differences in decision-making between upper-division students and beginning students. For instance, we see how popular culture and popular science provide an initial learning experience about certain subfields, such as astronomy and astrophysics, which strongly affect beginning students' perceptions of that subfield. Initial exposure to biology and chemistry in high school or early undergraduate classes often negatively affected students' interests in fields like biophysics or chemical physics. Data also suggests a splitting between students with respect to their outcome expectations of a desirable career in science. While some students prioritize using science to help people, others prioritize discovery of new knowledge though science, and some are in between. Students in both groups form perceptions about subfields that do not align with their identities and hence make decisions based on these perceptions. For instance, a student who prioritizes helping others through science may be quick to reject astrophysics as a subfield choice as they do not think that astrophysics can help people enough. 
    more » « less
  5. ; ( , the PrivateNLP 2020 Workshop on Privacy and Natural Language Processing Colocated with 13th ACM International WSDM Conference, 2020, in Houston, Texas, USA.)
    null (Ed.)
    Data and information privacy is a major concern of today’s world. More specifically, users’ digital privacy has become one of the most important issues to deal with, as advancements are being made in information sharing technology. An increasing number of users are sharing information through text messages, emails, and social media without proper awareness of privacy threats and their consequences. One approach to prevent the disclosure of private information is to identify them in a conversation and warn the dispatcher before the conveyance happens between the sender and the receiver. Another way of preventing information (sensitive) loss might be to analyze and sanitize a batch of offline documents when the data is already accumulated somewhere. However, automating the process of identifying user-centric privacy disclosure in textual data is challenging. This is because the natural language has an extremely rich form and structure with different levels of ambiguities. Therefore, we inquire after a potential framework that could bring this challenge within reach by precisely recognizing users’ privacy disclosures in a piece of text by taking into account - the authorship and sentiment (tone) of the content alongside the linguistic features and techniques. The proposed framework is considered as the supporting plugin to help text classification systems more accurately identify text that might disclose the author’s personal or private information. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email