Contextual Integrity Research Articles

Expectations vs reality: teenager views of institutional privacy

ABSTRACT Teenagers are growing up under pervasive networks of institutional surveillance. However, prior work on teens’ privacy attitudes and practices has largely focused on parental or peer surveillance. To explore how teenagers understand privacy in the context of institutional surveillance, we interviewed twelve teenagers residing in the US on location-tracking within high schools, which are primary sites of youth institutional surveillance. Using the contextual integrity framework, a structured approach to analyzing privacy, we address the following research questions: How do changes in the parameters of technology-driven information flows in high schools affect teenagers’ assessments of whether such flows are appropriate? What contextual values inform teenagers’ views of the appropriateness of technology-driven information flows in high schools? We found participants’ evaluations of institutional surveillance most often considered how the technology would impact the values of safety, autonomy, and community. We found large gaps between teenagers’ prescriptive norms and descriptive norms, which indicate that teenagers frequently experience institutional privacy violations, leading many to view such violations as inevitable and hold feelings of surveillance realism. We call for more research around young people and institutional privacy, and for teenage privacy education to engage in critical discussions and hands-on decision-making about privacy at the institutional level.

Privacy for Groups Online: Context Matters

The pervasive influence of online activities in our lives, encompassing personal connections, professional engagements, and e-commerce, has amplified concerns about privacy. However, existing privacy research has predominantly concentrated on the individual level, paying less attention to the privacy practices and strategies adopted by online social groups. This research gap calls for a renewed focus on understanding and addressing privacy challenges specific to online group settings. In this paper we explore the privacy needs of online groups through the lens of Contextual Integrity. We perform two complementary studies: semi-structured qualitative interviews of Facebook Groups users (n=17), and a large-scale survey of individuals organizing in groups on Facebook, Discord, and Reddit (n=4486). We investigate the privacy needs of different contextual groups, and locate the presence of contextual norms, contextual member roles, explicit and implicit rules, and privacy concerns. We trace how this complex interplay informs privacy expectations, needs, and negotiations across groups. We find that technical systems provide limited tools to effectively enforce group privacy, allowing individuals to compromise privacy norms. Based on these findings, we offer recommendations to support the design of privacy controls for online groups.

Stewarding Contextual Integrity in Data Services for Indigenous Scholarship

ABSTRACTThe CARE Principles for Indigenous Data Governance provide essential guideposts for the stewardship of Indigenous data. To put CARE into practice in libraries and repositories, resources are needed to support implementation and integration into current research data services (RDS). Based on analysis of case studies with scholars of Indigenous language and culture, this paper articulates specific Indigenous research and data practices to guide metadata work and other areas of responsibility in RDS. The cases surface the richness of relationships and the significance of accountability in the research process—demonstrating the “relational accountability” inherent in Indigenous research methods. Robust representation of relationality is essential to retaining integrity of context in metadata for Indigenous research data. We consider the practical implications of documenting relational context with current descriptive metadata approaches and challenges in achieving CARE adherent metadata, which we argue is the backbone for broader application of CARE for Indigenous RDS.

HAD-Net: An attention U-based network with hyper-scale shifted aggregating and max-diagonal sampling for medical image segmentation

Objectives:Accurate extraction of regions of interest (ROI) with variable shapes and scales is one of the primary challenges in medical image segmentation. Current U-based networks mostly aggregate multi-stage encoding outputs as an improved multi-scale skip connection. Although this design has been proven to provide scale diversity and contextual integrity, there remain several intuitive limits: (i) the encoding outputs are resampled to the same size simply, which destruct the fine-grained information. The advantages of utilization of multiple scales are insufficient. (ii) Certain redundant information proportional to the feature dimension size is introduced and causes multi-stage interference. And (iii) the precision of information delivery relies on the up-sampling and down-sampling layers, but guidance on maintaining consistency in feature locations and trends between them is lacking. Methods:To improve these situations, this paper proposed a U-based CNN network named HAD-Net, by assembling a new hyper-scale shifted aggregating module (HSAM) paradigm and progressive reusing attention (PRA) for skip connections, as well as employing a novel pair of dual-branch parameter-free sampling layers, i.e. max-diagonal pooling (MDP) and max-diagonal un-pooling (MDUP). That is, the aggregating scheme additionally combines five subregions with certain offsets in the shallower stage. Since the lower scale-down ratios of subregions enrich scales and fine-grain context. Then, the attention scheme contains a partial-to-global channel attention (PGCA) and a multi-scale reusing spatial attention (MRSA), it builds reusing connections internally and adjusts the focus on more useful dimensions. Finally, MDP and MDUP are explored in pairs to improve texture delivery and feature consistency, enhancing information retention and avoiding positional confusion. Results:Compared to state-of-the-art networks, HAD-Net has achieved comparable and even better performances with Dice of 90.13%, 81.51%, and 75.43% for each class on BraTS20, 89.59% Dice and 98.56% AUC on Kvasir-SEG, as well as 82.17% Dice and 98.05% AUC on DRIVE. Conclusions:The scheme of HSAM+PRA+MDP+MDUP has been proven to be a remarkable improvement and leaves room for further research.

Contextual Acceptance of COVID-19 Mitigation Mobile Apps in the United States: Mixed Methods Survey Study on Postpandemic Data Privacy.

The COVID-19 pandemic gave rise to countless user-facing mobile apps to help fight the pandemic ("COVID-19 mitigation apps"). These apps have been at the center of data privacy discussions because they collect, use, and even retain sensitive personal data from their users (eg, medical records and location data). The US government ended its COVID-19 emergency declaration in May 2023, marking a unique time to comprehensively investigate how data privacy impacted people's acceptance of various COVID-19 mitigation apps deployed throughout the pandemic. This research aims to provide insights into health data privacy regarding COVID-19 mitigation apps and policy recommendations for future deployment of public health mobile apps through the lens of data privacy. This research explores people's contextual acceptance of different types of COVID-19 mitigation apps by applying the privacy framework of contextual integrity. Specifically, this research seeks to identify the factors that impact people's acceptance of data sharing and data retention practices in various social contexts. A mixed methods web-based survey study was conducted by recruiting a simple US representative sample (N=674) on Prolific in February 2023. The survey includes a total of 60 vignette scenarios representing realistic social contexts that COVID-19 mitigation apps could be used. Each survey respondent answered questions about their acceptance of 10 randomly selected scenarios. Three contextual integrity parameters (attribute, recipient, and transmission principle) and respondents' basic demographics are controlled as independent variables. Regression analysis was performed to determine the factors impacting people's acceptance of initial data sharing and data retention practices via these apps. Qualitative data from the survey were analyzed to support the statistical results. Many contextual integrity parameter values, pairwise combinations of contextual integrity parameter values, and some demographic features of respondents have a significant impact on their acceptance of using COVID-19 mitigation apps in various social contexts. Respondents' acceptance of data retention practices diverged from their acceptance of initial data sharing practices in some scenarios. This study showed that people's acceptance of using various COVID-19 mitigation apps depends on specific social contexts, including the type of data (attribute), the recipients of the data (recipient), and the purpose of data use (transmission principle). Such acceptance may differ between the initial data sharing and data retention practices, even in the same context. Study findings generated rich implications for future pandemic mitigation apps and the broader public health mobile apps regarding data privacy and deployment considerations.

Four provocations for rich digital ethnographic research situated in social media networks

ABSTRACT Scholars engaging in social media research involving direct observation of users draw from a range of research methods, each with their own benefits and challenges. From an ethical perspective, research that engages with direct observation of user experiences and practices on these platforms exposes the investigator to a range of content which may have been intended for a private or semi-public audience, further complicating already debated ethical positions into what is considered ‘public’ content. This paper presents four provocations from a digital ethnography into user agency and practices on these platforms to demonstrate how direct observation methods can be practically implemented to ensure an ethical approach to data collection. It highlights how the distribution principle of contextual integrity can be used to guide the development of collection methods where content from participant’s networked connections is unavoidable, and where informed consent is unfeasible to obtain from these social media users.

Understanding contextual expectations for sharing wearables' data: Insights from a vignette study

People are increasingly open to sharing personal data collected by wearables, while concerns have emerged on how companies, governments and organisations process this data. This paper applies Nissenbaum's theory of contextual integrity to explore the perceived appropriateness of information flows linked to wearables. A vignette study was conducted (N = 500) to examine the influence of the type of data shared, its purpose, and the sender, on the appropriateness of different wearables' information flow scenarios. Results revealed a significant impact of information type, sharing purpose, and sender on the perceived appropriateness of data sharing. Notably, data collected for research purposes or to develop new functionalities was deemed most appropriate, while data used for advertising was viewed unfavourably. Further, the user-controlled sharing received higher appropriateness ratings. This research underscores the need for meaningful consent in data sharing and suggests that manufacturers of wearable devices should utilise user agency to supplement information flow automation based on societal and contextual privacy norms.

A contextual integrity approach to genomic information: what bioethics can learn from big data ethics

Genomic data is generated, processed and analysed at an increasingly rapid pace. This data is not limited to the medical context, but plays an important role in other contexts in society, such as commercial DNA testing, the forensic setting, archaeological research, and genetic surveillance. Genomic information also crosses the borders of these domains, e.g. forensic use of medical genetic information, insurance use of medical genomic information, or research use of commercial genomic data. This paper (1) argues that an informed consent approach for genomic information has limitations in many societal contexts, and (2) seeks to broaden the bioethical debate on genomic information by suggesting an approach that is applicable across multiple societal contexts. I argue that the contextual integrity framework, a theory rooted in information technology and big data ethics, is an effective tool to explore ethical challenges that arise from genomic information within a variety of different contexts. Rather than focusing on individual control over information, the contextual integrity approach holds that information should be shared and protected according to the norms that govern certain distinct social contexts. Several advantages of this contextual integrity approach will be discussed. The paper concludes that the contextual integrity framework helps to articulate and address a broad spectrum of ethical, social, and political factors in a variety of different societal contexts, while giving consideration to the interests of individuals, groups, and society at large.

“Can I have it non-personalised?” An Empirical Investigation of Consumer Willingness to Share Data for Personalized Services and Ads

European regulators, courts, and scholars are currently debating the legality of data processing for personalization purposes. Should businesses require separate consent for processing user data for personalized advertising, especially when offering free services reliant on such ads for revenue? Or is general consent for the contract enough, given personalized advertising’s role in fulfilling contractual obligations? This study investigates whether these legal distinctions reflect differences in people’s willingness to share data with businesses for personalization. Are consumers less willing to share their data for personalized advertising than for personalized services that they clearly contracted for? Does that change if the service is offered for free? Drawing from both the privacy calculus and privacy as contextual integrity theory, the hypothesis posits that individuals would be more inclined to share their data when it is used to personalize the services offered by businesses (e.g., music or news recommendations) rather than for personalized advertising, yet this difference will be smaller when services are offered for free. Using three vignette experiments involving music, shopping, and news services (N = 3,436), the study demonstrates that, contrary to predictions, most individuals are hesitant to share their data for personalization, regardless of whether it’s for advertisements or contracted services. This absence of distinction persists across both paid and free services. The findings strongly support the claim that if the law considers people’s reluctance to share personal data, both personalized advertising and personalized contracted services should require separate consent to data processing from consumers.

A Roadmap for Applying the Contextual Integrity Framework in Qualitative Privacy Research

Privacy is an important topic in HCI and social computing research, and the theory of contextual integrity (CI) is increasingly used to understand how sociotechnical systems-and the new kinds of information flows they introduce-can violate privacy. In empirical research, CI can serve as a conceptual framework for explaining the contextual nature of privacy as well as an analytical framework for evaluating privacy attitudes and behaviors. Analytical applications of CI in HCI primarily employ quantitative methods to identify appropriate information flows but rarely engage with the full CI framework to evaluate such flows. In this paper, we present a roadmap to guide HCI and social computing researchers on how to apply the full CI framework to qualitative projects. To help researchers envision what such an analysis can look like, each step includes an example analysis using interview data from projects on privacy and fitness tracking. We conclude by discussing how harnessing the full CI framework can address critiques of CI and identify opportunities for further theory development.

Contextualizing Interpersonal Data Sharing in Smart Homes

A key feature of smart home devices is monitoring the environment and recording data. These devices provide security via motion-detection video alerts, cost-savings via thermostat usage history, and peace of mind via functions like auto-locking doors or water leak detectors. At the same time, the sharing of this information in interpersonal relationships---though necessary---is currently accomplished on an all-or-nothing basis. This can easily lead to oversharing in a multi-user environment. Although prior work has studied people's perceptions of information sharing with vendors or ISPs, the sharing of household data among users who interact personally is less well understood. Interpersonal situations make data sharing much more context-based and, thus, more complicated. In this paper, we use themes from the theory of contextual integrity in an online survey (n=1,992) to study how people perceive data sharing with others in smart homes and inform future designs and research. Our results show that data recipients in a smart home can be reduced to three major groups, and data types matter more than device types. We also found that the types of access control desired by users can vary from scenario to scenario. Depending on whom they are sharing data with and about what data, participants expressed varying levels of comfort when presented with different types of access control (e.g., explicit approval versus time-limited access). Taken together, this provides strong evidence that a more dynamic access control system is needed, and we can design it in a more usable way.

“Those blimmin Ts and Cs”: a mixed methods analysis of how people manage personal information, privacy, and impressions

ABSTRACT Interconnected and smart technologies complicate personal information management (PIM) because users delegate the storing, organizing, and retrieving of personal information to smart and mobile service providers. Meta-level PIM activities are required to maintain the privacy and security of personal information. This study provides insights into how users of location tracking, mobile apps, and smart home technologies perceive PIM and privacy. We turn to the privacy as contextual integrity (CI) and impression management (IM) literatures to explore informational norms and interpersonal dynamics in PIM. This study is based on a mixed methods design to analyze focus groups and interviews with 106 British and Dutch respondents. Combining unsupervised Latent Dirichlet allocation (LDA) topic modeling and thematic analysis, we reveal discursive patterns in respondent accounts of technology use and provide an in-depth interpretation of these patterns. Our findings indicate that PIM practices are associated with the perceived appropriateness of information flows, anthropomorphic interpretations of technologies, and interpersonal surveillance. Thus, impressions are managed toward social actors as well as technology providers. We contribute to PIM research with a demonstration of how PIM in mobile, smart, and location-based technology use cannot be separated from contextual factors and strategies to manage impressions of habits and behaviors.

Comprehensive evaluation of privacy policies using the contextual integrity framework

AbstractOnline privacy policies are often lengthy and difficult to understand. This may lead many users to avoid reading them despite increasing concerns about how their personal information is managed. This article presents a structured approach to evaluate the transparency and comprehensiveness of privacy policies using a comprehensive set of evaluation questions within the contextual integrity (CI) framework. We use these questions to identify policies' responses to key privacy concerns. Applying the CI framework, we analyze the clarity and context of these responses, identifying any vagueness and contextual issues that could impede a user's understanding of the privacy policy. Using the CI analysis, we quantify the quality of policies' responses, thereby enabling users to make informed decisions about online services or products. We apply our methodology to two popular messaging apps, Telegram and WhatsApp, using them as case studies to systematically uncover the strengths and weaknesses of their privacy policies. The findings demonstrate that our proposed methodology can effectively identify transparency issues and assess the comprehensiveness of privacy policies. This suggests that our approach could serve as a practical alternative to subjective evaluations typically conducted by privacy experts.

Changing the whole game: effects of the COVID-19 pandemic's accelerated digitalization on European bank staff's data protection capabilities

The COVID-19 pandemic accelerated the acceptance of digital banking services such as online payment and banking apps. As bank clients become more likely to use online services and contactless payment, the amount of consumer data available for banks’ digitalization strategies has increased. This acceleration in digital banking has placed a spotlight on retail banks’ efforts to protect personal data. Bank staff are on the frontlines of both protecting personal data and communicating their banks’ efforts in this respect to maintain consumer trust. Our study aimed to answer the following question: How did the sudden increase in digitalization during the pandemic affect bank staff’s capabilities in protecting personal data? In a two-stage qualitative study, we collected empirical data on bank staff’s data protection efforts during accelerated digitalization. Analyzing our findings from the perspective of technological mediation theory, which focuses on the relationships between technologies, practices, and social arrangements, we found that in banking platformization, bank staff are disempowered in supporting clients, who are responsibilized for protecting themselves from fraud. Competitive pressures push retail banks into using client data in ways beyond sector norms, endangering the contextual integrity of data flows. Further, our findings show that digitalization presents bank clients with new risks, of which they are informed only after changing their banking practices, and it may be difficult to return to former arrangements. The application of mediation theory, combined with contextual integrity theory, clarified the shifting positions of different digital technology users in the infrastructural network of platformized banking and allowed for an in-depth analysis of conflicting interests. By clarifying these interests, difficulties were identified that need to be addressed in public policy and digital innovation projects to prevent loss of trust among bank clients.

Contextual Integrity and Personal Health Information Sharing in Online Social Networks

Many online social network (OSN) users exchange health-related information on OSNs to manage their health, obtain support, and offer assistance to others facing similar medical challenges. Nonetheless, OSN users are concerned about the privacy of their health information. Extant studies on online information privacy have largely resorted to a calculus perspective, focusing on the costs and benefits of information exchange with commercial vendors. Such a perspective, however, fails to capture the nuanced flows of personal health information (PHI) on OSNs, which involves more than two parties. To address this gap and further advance the research on information privacy, this paper embraces the theory of contextual integrity to explore the distinctive contextual idiosyncrasies of PHI flow on OSNs. In particular, we examine how context-specific values and norms affect OSN users’ personal privacy norms and their PHI disclosure intentions. Our theory-driven empirical results suggest that contextual values and normative expectations related to technology control, legislation, and peer users collectively influence OSN users’ personal privacy norms. In addition, perceived personal value and perceived altruistic value serve as motivators to promote PHI disclosure intentions, working in tandem with personal privacy norms. The key findings of the study also have important implications for policymakers and OSN providers in the context of OSN-mediated healthcare through (1) advancing our understanding of the complex dynamics of PHI flow on OSNs and (2) designing effective information privacy measures (i.e., policies, technical features, and programs) to uphold contextual integrity and nurture the development of patient-centered healthcare paradigms.

Understanding Privacy in Virtual Reality Classrooms: A Contextual Integrity Perspective

Understanding Privacy in Virtual Reality Classrooms: A Contextual Integrity Perspective

Whale-optimized LSTM networks for enhanced automatic text summarization.

Automatic text summarization is a cornerstone of natural language processing, yet existing methods often struggle to maintain contextual integrity and capture nuanced sentence relationships. Introducing the Optimized Auto Encoded Long Short-Term Memory Network (OAELSTM), enhanced by the Whale Optimization Algorithm (WOA), offers a novel approach to this challenge. Existing summarization models frequently produce summaries that are either too generic or disjointed, failing to preserve the essential content. The OAELSTM model, integrating deep LSTM layers and autoencoder mechanisms, focuses on extracting key phrases and concepts, ensuring that summaries are both informative and coherent. WOA fine-tunes the model's parameters, enhancing its precision and efficiency. Evaluation on datasets like CNN/Daily Mail and Gigaword demonstrates the model's superiority over existing approaches. It achieves a ROUGE Score of 0.456, an accuracy rate of 84.47%, and a specificity score of 0.3244, all within an efficient processing time of 4,341.95 s.

Digital Pathology Scanners and Contextual Integrity

We aim to bring both digital pathology in general and computational pathology in particular within the scope of Helen Nissenbaum’s theory of appropriate information transfer as contextual integrity. In Section 1, the main lines of the theory of contextual integrity are introduced, and reasons are given why it is not properly speaking a theory of privacy, but rather a theory of morally permissible information transfer in general. Then the theory is applied to uses of digitised pathology images for (a) patient-by-patient analysis (Section 2); and (b) computational pathology (Sections 3 and 4). Although big data exercises involving personal data are sometimes seen by Nissenbaum and colleagues as particular threats to existing data-sharing norms and other social norms, we claim that patient-by-patient digital pathology is riskier, at least in forms it has taken during the pandemic. At the end, we consider some risks in computational pathology that are due to the interaction between health institutions, particularly in the public sector, and commercial algorithm developers.

Medical Information Protection in Internet Hospital Apps in China: Scale Development and Content Analysis.

Hospital apps are increasingly being adopted in many countries, especially since the start of the COVID-19 pandemic. Web-based hospitals can provide valuable medical services and enhanced accessibility. However, increasing concerns about personal information (PI) and strict legal compliance requirements necessitate privacy assessments for these platforms. Guided by the theory of contextual integrity, this study investigates the regulatory compliance of privacy policies for internet hospital apps in the mainland of China. In this paper, we aim to evaluate the regulatory compliance of privacy policies of internet hospital apps in the mainland of China and offer recommendations for improvement. We obtained 59 internet hospital apps on November 7, 2023, and reviewed 52 privacy policies available between November 8 and 23, 2023. We developed a 3-level indicator scale based on the information processing activities, as stipulated in relevant regulations. The scale comprised 7 level-1 indicators, 26 level-2 indicators, and 70 level-3 indicators. The mean compliance score of the 52 assessed apps was 73/100 (SD 22.4%), revealing a varied spectrum of compliance. Sensitive PI protection compliance (mean 73.9%, SD 24.2%) lagged behind general PI protection (mean 90.4%, SD 14.7%), with only 12 apps requiring separate consent for processing sensitive PI (mean 73.9%, SD 24.2%). Although most apps (n=41, 79%) committed to supervising subcontractors, only a quarter (n=13, 25%) required users' explicit consent for subcontracting activities. Concerning PI storage security (mean 71.2%, SD 29.3%) and incident management (mean 71.8%, SD 36.6%), half of the assessed apps (n=27, 52%) committed to bear corresponding legal responsibility, whereas fewer than half (n=24, 46%) specified the security level obtained. Most privacy policies stated the PI retention period (n=40, 77%) and instances of PI deletion or anonymization (n=41, 79%), but fewer (n=20, 38.5%) committed to prompt third-party PI deletion. Most apps delineated various individual rights, but only a fraction addressed the rights to obtain copies (n=22, 42%) or to refuse advertisement based on automated decision-making (n=13, 25%). Significant deficiencies remained in regular compliance audits (mean 11.5%, SD 37.8%), impact assessments (mean 13.5%, SD 15.2%), and PI officer disclosure (mean 48.1%, SD 49.3%). Our analysis revealed both strengths and significant shortcomings in the compliance of internet hospital apps' privacy policies with relevant regulations. As China continues to implement internet hospital apps, it should ensure the informed consent of users for PI processing activities, enhance compliance levels of relevant privacy policies, and fortify PI protection enforcement across the information processing stages.

Private attributes: The meanings and mechanisms of “privacy-preserving” adtech

This study analyzes the meanings and technical mechanisms of privacy that leading advertising technology (adtech) companies are deploying under the banner of “privacy-preserving” adtech. We analyze this discourse by examining documents wherein Meta, Google, and Apple each propose to provide advertising attribution services—which aim to measure and optimize advertising effectiveness—while “solving” some of the privacy problems associated with online ad attribution. We find that these solutions define privacy primarily as anonymity, as limiting access to individuals’ information, and as the prevention of third-party tracking. We critique these proposals by drawing on the theory of privacy as contextual integrity. Overall, we argue that these attribution solutions not only fail to achieve meaningful privacy but also leverage privacy rhetoric to advance commercial interests.