Privacy Preferences Research Articles

Uncovering the Neural Processes of Privacy: A Neurally Informed Behavioral Intervention to Protect Information Privacy

People are increasingly aware that their information is being tracked online. Although people generally self-report privacy to be important to them, in practice they often disclose far more private information than their self-reported privacy preferences. This “privacy paradox” could be better understood by uncovering the neural processes underlying the privacy calculus: weighing the risks against the benefits of disclosure. We assess the neural processes shaping privacy tradeoffs to characterize the neural mechanisms underlying privacy tradeoffs to design behavioral interventions that help people make better decisions that align with their privacy preferences. In Study 1, we used functional MRI (fMRI) to identify the neural correlates of the privacy calculus. In Study 2, we leveraged brain insights to design and test a neurally informed behavioral intervention to help people protect their privacy. Our results show that altering the timing when information is presented precisely at the time of decision (specifically within a second) directs attention to privacy risks versus benefits, therefore discouraging participants from disclosing their private information. Identifying the neural processes of privacy helps elucidate the privacy calculus and sheds light on the privacy paradox and guides the design of neurally informed behavioral interventions to help people protect their privacy.

Uncovering the Neural Processes of Privacy: A Neurally Informed Behavioral Intervention to Protect Information Privacy

People are increasingly aware that their information is being tracked online. Although people generally self-report privacy to be important to them, in practice they often disclose far more private information than their self-reported privacy preferences. This “privacy paradox” could be better understood by uncovering the neural processes underlying the privacy calculus: weighing the risks against the benefits of disclosure. We assess the neural processes shaping privacy tradeoffs to characterize the neural mechanisms underlying privacy tradeoffs to design behavioral interventions that help people make better decisions that align with their privacy preferences. In Study 1, we used functional MRI (fMRI) to identify the neural correlates of the privacy calculus. In Study 2, we leveraged brain insights to design and test a neurally informed behavioral intervention to help people protect their privacy. Our results show that altering the timing when information is presented precisely at the time of decision (specifically within a second) directs attention to privacy risks versus benefits, therefore discouraging participants from disclosing their private information. Identifying the neural processes of privacy helps elucidate the privacy calculus and sheds light on the privacy paradox and guides the design of neurally informed behavioral interventions to help people protect their privacy.

The Optimal Pricing in Blockchain-Enabled Enterprises Operation Considering Privacy Attitude and Privacy Protection

The traceability of Blockchain Technology (BT) has been implemented widely in many industries, but coupled with this fact is that more and more consumers focus on personal privacy information security. This paper mainly studies a BT-enabled operation system with customers’ privacy preferences and enterprises’ privacy protection. From the short-term perspective, we first investigate the impacts of different privacy attitudes from consumers on the optimal pricing and privacy protection decisions of enterprises in two scenarios with and without BT. Then, we give the conditions of enterprises adopting BT. Furthermore, from the long-term perspective, when privacy protection insurance is provided for risk-averse consumers, we also investigate the evolutionary stability strategies between enterprises and consumers using the evolutionary game theory. Finally, numerical analysis is adopted to verify the theoretical results and show that privacy protection and BT are important to the operation of enterprises and the purchasing behavior of consumers.

Privacy Policies across the Ages: Content of Privacy Policies 1996–2021

It is well known that most users do not read privacy policies but almost always tick the box to agree with them. While the length and readability of privacy policies have been well studied and many approaches for policy analysis based on natural language processing have been proposed, existing studies are limited in their depth and scope, often focusing on a small number of data practices at single point in time. In this article, we fill this gap by analyzing the 25-year history of privacy policies using machine learning and natural language processing and presenting a comprehensive analysis of policy contents. Specifically, we collect a large-scale longitudinal corpus of privacy policies from 1996 to 2021 and analyze their content in terms of the data practices they describe, the rights they grant to users, and the rights they reserve for their organizations. We pay particular attention to changes in response to recent privacy regulations such as the GDPR and CCPA. We observe some positive changes, such as reductions in data collection post-GDPR, but also a range of concerning data practices, such as widespread implicit data collection for which users have no meaningful choices or access rights. Our work is an important step toward making privacy policies machine readable on the user side, which would help users match their privacy preferences against the policies offered by web services.

Unpacking privacy: Valuation of personal data protection.

Information about individual behaviour is collected regularly by organisations. This information has value to businesses, the government and third parties. It is not clear what value this personal data has to consumers themselves. Much of the modern economy is predicated on people sharing personal data, however if individuals value their privacy, they may choose to withhold this data unless the perceived benefits of sharing outweigh the perceived value of keeping the data private. One technique to assess how much individuals value their privacy is to ask them whether they might be willing to pay for an otherwise free service if paying allowed them to avoid sharing personal data. Our research extends previous work on factors affecting individuals' decisions about whether to share personal data. We take an experimental approach and focus on whether consumers place a positive value on protecting their data by examining their willingness to share personal data in a variety of data sharing environments. Using five evaluation techniques, we systematically investigate whether members of the public value keeping their personal data private. We show that the extent to which participants value protecting their information differs by data type, suggesting there is no simple function to assign a value for individual privacy. The majority of participants displayed remarkable consistency in their rankings of the importance of different types of data through a variety of elicitation procedures, a finding consistent with the existence of stable individual privacy preferences in protecting personal data. We discuss our findings in the context of research on the value of privacy and privacy preferences.

Optimal Nonlinear Pricing with Data-Sensitive Consumers

We study monopolistic screening when some consumers are data sensitive and incur a privacy cost if their purchase reveals information to the monopolist. The monopolist discriminates between data-sensitive and classical consumers using privacy mechanisms that consist of a direct mechanism and a privacy option. A privacy mechanism is optimal for large privacy costs and leaves classical consumers better off than data-sensitive consumers with the same valuation. When privacy preferences become public information, data-sensitive consumers and the monopolist gain, whereas classical consumers lose. Our results are relevant for policies targeting consumers’ data awareness, such as the European General Data Protection Regulation. (JEL D11, D42, D82, D83, L12)

Privacy protection in intelligent vehicle networking: A novel federated learning algorithm based on information fusion

Federated learning is an effective technique to solve the problem of information fusion and information sharing in intelligent vehicle networking. However, most of the existing federated learning algorithms generally have the risk of privacy leakage. To address this security risk, this paper proposes a novel personalized federated learning with privacy preservation (PDP-PFL) algorithm based on information fusion. In the first stage of its execution, the new algorithm achieves personalized privacy protection by grading users’ privacy based on their privacy preferences and adding noise that satisfies their privacy preferences. In the second stage of its execution, PDP-PFL performs collaborative training of deep models among different in-vehicle terminals for personalized learning, using a lightweight dynamic convolutional network architecture without sharing the local data of each terminal. Instead of sharing all the parameters of the model as in standard federated learning, PDP-PFL keeps the last layer local, thus adding another layer of data confidentiality and making it difficult for the adversary to infer the image of the target vehicle terminal. It trains a personalized model for each vehicle terminal by “local fine-tuning”. Based on experiments, it is shown that the accuracy of the proposed new algorithm for PDP-PFL calculation can be comparable to or better than that of the FedAvg algorithm and the FedBN algorithm, while further enhancing the protection of data privacy.

Mental health issues and illness and substance use disorder (non-)disclosure to a supervisor: a cross-sectional study on beliefs, attitudes and needs of military personnel

ObjectivesResearch suggests that military personnel frequently delay disclosing mental health issues and illness (MHI), including substance use disorder, to supervisors. This delay causes missed opportunities for support and workplace accommodations...

Usability and Enforceability of Global Privacy Control

Web tracking by ad networks and other data-driven businesses is often privacy-invasive. Privacy laws, such as the California Consumer Privacy Act, aim to give people more control over their data. In particular, they provide a right to opt out from web tracking via privacy preference signals, notably Global Privacy Control (GPC). GPC holds the promise of enabling people to exercise their opt out rights on the web. Broad adoption of GPC hinges on its usability. In a usability survey we find that 94% of the participants would turn on GPC indicating a need for such efficient and effective opt out mechanism. 81% of the participants in our survey also have a correct understanding of what GPC does ensuring that their intent is accurately represented by their choice. The effectiveness of GPC is dependent on whether websites' GPC compliance can be enforced. A site's GPC compliance can be analyzed based on privacy flags, such as the US Privacy String, which is used on many sites to indicate the opt out status of a web user. Leveraging the US Privacy String for GPC purposes we implement a proof-of-concept browser extension that successfully and correctly analyzes sites' GPC compliance at a rate of 89%. We further implement a web crawler for our browser extension demonstrating that our analysis approach is scalable. We find that many sites do not respect GPC opt out signals despite being legally obligated to do so. Only 54/464 (12%) sites with a US Privacy String opt out users after having received a GPC signal.

Understanding Private Preferences in Urban Development—Analysing Spatial Patterns of Food Discount Stores Locations in Switzerland

This paper examines the spatial pattern of food discount stores in Switzerland, where private actors made location decisions without interference from planning regulations until 2016. Using aerial images and a classification scheme with functional and morphological attributes, the study shows that the majority of discount stores were built in peripheral commercial areas or greenfield sites as solitary buildings, indicating a preference for minimal land acquisition costs and car orientation. Some integrated central locations were also chosen. The average density measured by floor area ratio was low. The results suggest that without planning intervention, private actors’ decisions would lead to sprawled settlement patterns with high ecological and societal costs. Further research is needed to explore the potential role of planning in mitigating this effect.

Occupant privacy perception, awareness, and preferences in smart office environments

Building management systems tout numerous benefits, such as energy efficiency and occupant comfort but rely on vast amounts of data from various sensors. Advancements in machine learning algorithms make it possible to extract personal information about occupants and their activities beyond the intended design of a non-intrusive sensor. However, occupants are not informed of data collection and possess different privacy preferences and thresholds for privacy loss. While privacy perceptions and preferences are most understood in smart homes, limited studies have evaluated these factors in smart office buildings, where there are more users and different privacy risks. To better understand occupants’ perceptions and privacy preferences, we conducted twenty-four semi-structured interviews between April 2022 and May 2022 on occupants of a smart office building. We found that data modality features and personal features contribute to people’s privacy preferences. The features of the collected modality define data modality features – spatial, security, and temporal context. In contrast, personal features consist of one’s awareness of data modality features and data inferences, definitions of privacy and security, and the available rewards and utility. Our proposed model of people’s privacy preferences in smart office buildings helps design more effective measures to improve people’s privacy.

Data Sharing Privacy Metrics Model Based on Information Entropy and Group Privacy Preference

With the development of the mobile internet, service providers obtain data and resources through a large number of terminal user devices. They use private data for business empowerment, which improves the user experience while causing users’ privacy disclosure. Current research ignores the impact of disclosing user non-sensitive attributes under a single scenario of data sharing and lacks consideration of users’ privacy preferences. This paper constructs a data-sharing privacy metrics model based on information entropy and group privacy preferences. Use information theory to model the correlation of the privacy metrics problem, the improved entropy weight algorithm to measure the overall privacy of the data, and the analytic hierarchy process to correct user privacy preferences. Experiments show that this privacy metrics model can better quantify data privacy than conventional methods, provide a reliable evaluation mechanism for privacy security in data sharing and publishing scenarios, and help to enhance data privacy protection.

Utility-Aware and Privacy-Preserving Mobile Query Services

Location-based queries enable fundamental services for mobile users. While the benefits of location-based services (LBS) are numerous, exposure of mobile users' locations to untrusted LBS providers may lead to privacy concerns. This paper proposes StarCloak, a utility-aware and attack-resilient location anonymization service for privacy-preserving LBS usage. StarCloak combines several desirable properties. First, unlike conventional approaches which are indifferent to underlying road network structure, StarCloak uses the concept of stars and proposes cloaking graphs for effective location cloaking on road networks. Second, StarCloak supports user-specified k-user anonymity and <formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex>$l$</tex></formula> -segment indistinguishability, for enabling personalized privacy protection and for serving users with varying privacy preferences. Third, StarCloak achieves strong attack-resilience against replay and query injection attacks through randomized star selection and pruning. Finally, to enable efficient query processing with high throughput and low bandwidth overhead, StarCloak makes cost-aware star selection decisions by considering query evaluation and network communication costs. We evaluate StarCloak on two datasets using real-world road networks, under various privacy and utility constraints. Results show that StarCloak achieves improved query success rate and throughput, reduced anonymization time and network usage, and higher attack-resilience in comparison to XStar, its most relevant competitor.

Camouflage: A dominant reaction to worsening conditions

Individuals choose camouflage as a dominant response when a state’s political conditions worsen, particularly when a democracy turns authoritarian. Individuals hide their private preferences to survive under oppressive regimes. This paper argues that in many circumstances camouflage is the most rational, cost-minimizing reaction to oppression. The paper identifies five kinds of costs that induce individuals to resort to four different ideal types of camouflage: (1) avoidance of contact, (2) minimal participation, (3) restricted cooperation, and (4) full engagement with the oppressive regime. Camouflage is particularly advantageous as the costs of exit or voice are often high. Therefore, a large majority of citizens disagreeing with the ideology and policies of a regime are likely to camouflage.

Towards personalized privacy preference aware data trading: A contract theory based approach

A large number of IoT devices geographically distributed in urban areas makes it attractive to collect massive data in a crowdsourced manner. To fully exploit the value of crowdsourced data, there has been a significant growth in demand for data trading recently. However, trading data exposes data owners to privacy violations. Existing studies based on centralized differential privacy are impractical due to the assumption of a trustworthy data collector, the high risk of privacy leakage, and extensive communication costs, let alone the data owners’ personalized requirements on privacy protection. To this end, we propose a contract theory-based personalized privacy-aware data trading approach that provides a set of optimal contracts specifying different privacy-preserving levels and data trading prices to selfish data owners who upload perturbed data according to the negotiated privacy-preserving level, and finally aggregates the data using a group-weighted maximum likelihood estimation method. The proposed private data trading approach not only achieves desirable data utility in terms of accuracy but also satisfies budget feasibility, individual rationality, and incentive compatibility through theoretical analysis and extensive experiments.

Tolerant Solidarity With Violent Protesters: Evidence From a Survey Experiment

Conventional wisdom holds that violent protests alienate the public. But violence could also create tactical disagreement among protesters and weaken their unity. So, to what extent does protesters’ use of violence erode intra-movement cohesion? This article argues that protesters can preserve cohesion by falsifying their private moral preferences and restraining themselves from public denunciation, an act which I characterize as ‘tolerant solidarity’. Based on a survey experiment on participants of Hong Kong’s 2019 protests, I examine how their private and public preferences toward violence change in response to three divisive conditions: protesters taking the initiative, violence causing physical harm, and violence targeting counter-protesters. Results show while these divisive conditions make respondents more likely to morally disapprove violence, the effects are weaker in their support for public denunciation, an indication that their private preferences are partially concealed. Findings contribute to the study of contentious politics by advancing a more complex understanding of movement cohesion when protester violence is involved.

Enhancing user awareness on inferences obtained from fitness trackers data.

In the IoT era, sensitive and non-sensitive data are recorded and transmitted to multiple service providers and IoT platforms, aiming to improve the quality of our lives through the provision of high-quality services. However, in some cases these data may become available to interested third parties, who can analyse them with the intention to derive further knowledge and generate new insights about the users, that they can ultimately use for their own benefit. This predicament raises a crucial issue regarding the privacy of the users and their awareness on how their personal data are shared and potentially used. The immense increase in fitness trackers use has further increased the amount of user data generated, processed and possibly shared or sold to third parties, enabling the extraction of further insights about the users. In this work, we investigate if the analysis and exploitation of the data collected by fitness trackers can lead to the extraction of inferences about the owners routines, health status or other sensitive information. Based on the results, we utilise the PrivacyEnhAction privacy tool, a web application we implemented in a previous work through which the users can analyse data collected from their IoT devices, to educate the users about the possible risks and to enable them to set their user privacy preferences on their fitness trackers accordingly, contributing to the personalisation of the provided services, in respect of their personal data.

Global variations in online privacy concerns across 57 countries

Cross-cultural studies have found national differences in how concerned people are about online privacy. However, it has not yet been settled what causes this variation, and several factors have been proposed in the literature, including internet habituation, individualism, and uncertainty avoidance. Here we investigate these factors by two studies. In the first, we examine the association between online privacy concerns and a new measure of online self-disclosure norms that we introduce. We find that this measure is significantly associated with two established instruments of online privacy concerns in the literature. In the second, we analyze previously unpublished data from a questionnaire on online self-disclosure norms as assessed by this new measure. It includes replies from 18,046 adult respondents from 57 countries and six continents. We find that norms in favor of more restrictive online self-disclosure are weaker in countries with higher levels of internet penetration (r = −0.56, p < .001). Our findings suggest that higher internet penetration in a country reduces online privacy concerns. The results support the idea that habituation to online environments decreases privacy risk perceptions. An implication is that preferences for online privacy are likely to decline over time in countries where internet penetration is still low. Lastly, in conflict with previous studies, our analyses do not support the theory that online privacy concerns are associated with national cultures related to individualism or uncertainty avoidance as measured by Hofstede's indices.

Communication Apprehension, Privacy Preferences and L2 Willingness to Communicate: Can They Predict Communication Ability?

Being able to communicate can play an important role in achieving the desired goals in today’s world. This study investigated the interrelationships between communication apprehension (CA), privacy preferences (Pr), L2 WTC, and foreign language achievement (FLA) of learners as an indicator of their communication ability. Besides, the differences between males and females regarding these variables were examined. To this end, we administered three questionnaires to 250 English learners. Pearson’s correlation, t-test, and Structural Equation Modeling (SEM) were employed to analyze the data. The findings revealed that CA has a significant negative relationship with FLA. Considering Pr, FLA had a significant negative relationship with Anonymity. WTC and its subscales demonstrated a significant positive association with FLA. The results of the SEM denoted that CA is a negative predictor of L2 WTC, and FLA; L2 WTC positively predicted FLA; and privacy preferences neither predicted L2 WTC nor FLA. Regarding gender, the results indicated that males had a higher level of preference for Intimacy with Friends, WTW, and WTC.

Narratives on migration and political polarization: How the emphasis in narratives can drive us apart

Nowadays, immigration is a polarizing topic in politics. In this paper, we investigate how much this political polarization is driven by the depiction narratives made of immigrants vis-a-vis the natives. Furthermore, we look at whether polarization is rooted in private preferences over narratives or in how they are endorsed in public settings and social media. Our empirical strategy consists of a survey experiment in the 2021 German elections and a field experiment on Twitter in which we manipulate the “pinned tweets” of experimental users. To build our narratives, we manipulate either the policy position — hostile toward or accepting migration — or an emphasis on the out-group, on the in-group, or on economic reciprocity. We find that political polarization is driven both by the policy position and emphasis in narratives. On Twitter, the out-group emphasis drives supporters of different parties apart, and the corresponding hostile narrative becomes the only one going viral. In the survey, right-wing participants prefer the reciprocity emphasis more, but we still find evidence of more polarization when allowing the participants to go public.