# Raising Awareness for Privacy Risks and Supporting Protection in the Light of Digital Inequalities

Yannic Meier

► To cite this version:

Yannic Meier. Raising Awareness for Privacy Risks and Supporting Protection in the Light of Digital Inequalities. 17th IFIP International Summer School on Privacy and Identity Management (Privacy and Identity), Aug 2022, Virtual, Germany. pp.44-51, 10.1007/978-3-031-31971-6_5. hal-05315945

HAL Id: hal-05315945

https://inria.hal.science/hal-05315945v1

Submitted on 15 Oct 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
<figure><img src="image_2.png" /></figure>

ifip

International Federation for Information Processing

This document is the original author manuscript of a paper submitted to an IFIP
conference proceedings or other IFIP publication by Springer Nature. As such, there
may be some differences in the official published version of the paper. Such
differences, if any, are usually due to reformatting during preparation for publication or
minor corrections made by the author(s) during final proofreading of the publication
manuscript.
# Raising Awareness for Privacy Risks and Supporting Protection in the Light of Digital Inequalities

Yannic Meier(✉) ID

University of Duisburg-Essen, 47057 Duisburg, Germany

yannic.meier@uni-due.de

**Abstract.** Despite legal improvements in protecting privacy like the EU GDPR, most applied conceptions of privacy are individualistic, thus, still putting the responsibility for privacy management onto the users of digital technologies. A major problem with this approach is that it ignores obvious differences between user groups in being able to manage their privacy online. Recent studies show that factors like sociodemographic and -economic status create digital inequalities in people's digital behaviors, which is also true and particularly concerning for their privacy behaviors. Empirical works investigating means to assist users in their self-data management, however, are barely addressing these digital inequalities in their proposed solutions. Therefore, the present chapter will briefly summarize the empirical status quo of research focusing on privacy and digital inequalities and identify gaps in currently proposed solutions. In conclusion, it can be said that although initial research reveals digital inequalities in terms of people's privacy awareness, literacy, and behaviors, there appear to be neither empirical nor regulatory solutions to balance these inequities. Thus, I recommend that future research should more actively address and study the particular needs of vulnerable groups in finding ways of how to assist them to better manage their privacy on the internet.

**Keywords:** Privacy Protection · Digital Inequalities · Transparency · Privacy Literacy · Privacy Risk Awareness

## 1 Introduction

Using the Internet and digital technologies brings various advantages and facilitations for people's lives, but at the same time, people leave their footprints in these digital environments. Digital footprints – defined as “digitally traceable behavior and online presence associated with an individual” (Micheli et al., 2018, p. 243) – are being left behind because users are constantly required to actively provide personal information or to consent to an automated tracking of their data. However, not everyone leaves the same amount of tracks behind: some leave more detailed imprints than others while again others are more capable to conceal their traces by means of privacy protective strategies. Although individual privacy protection is viewed as a limited approach which
neglects privacy as a networked and collective social phenomenon and value (see Baruh & Popescu, 2017), current loopholes in legal approaches to privacy still necessitate individual self-protection. This approach, however, is specifically tragic since it creates and upholds inherent inequalities between different social classes and individual groups. This results in some user segments not being capable to adopt very easily applicable strategies like deleting cookies, withdrawing information, or not using websites and apps which can be counted to the most prominent protection strategies (Boerman et al., 2021; Matzner et al., 2016; Meier & Krämer, 2023). When it comes to more sophisticated and, thus, more effective strategies, the gap between user groups appears to be even larger. The more sophisticated privacy protective strategies get, the more awareness, knowledge, and skills are required to adopt them. Privacy risk awareness as well as protection knowledge and skills (also known as privacy literacy; Trepte et al., 2015) are main predictors of people's Internet privacy protection behaviors (Baruh et al., 2017; Büchi et al., 2017; Meier et al., 2020a). Recent studies with representative samples from the Switzerland and Germany revealed digital inequalities regarding people's internet privacy protection (skills) that can be traced back to sociodemographic differences. While Meier and Krämer (2023) found age and gender differences in people's privacy protective attempts with older persons and females using less protection online, Büchi et al. (2021) showed that especially internet skills (which are one of the main drivers of online protection) are unequally distributed among different user segments, and they depend on factors like age, education, and gender. While past research has primarily focused on exploring digital inequalities concerning people's privacy perceptions and behaviors, there appears to be a research gap aiming at balancing these inequalities. Although much research exists on how to inform users more transparently about potential privacy threats, for instance, by means of in situ measures such as privacy icons (Efroni et al., 2019), privacy warning messages (Ostendorf et al., 2022), or privacy scores (Meier & Krämer, 2022), these studies hardly consider that different user groups might require different kinds of information based on distinct needs or abilities. Moreover, there is no data about the consequences of unequally distributed privacy skills and knowledge, for instance, on the frequency of experienced privacy violations and how these in turn affect people's online behaviors (see Blank & Lutz, 2018; Lutz, 2019). In light of these research gaps, this chapter will outline the current state-of-the-art regarding privacy and digital inequalities and describe future directions how digital inequalities might be better addressed by privacy research.

## 2 Digital Inequalities

Scholars have identified multiple stages of digital divides that comprise differences in accessing information and communication technology (ICT), differences in ICT usage and skills, and differences in the outcomes of using ICTs (van Deursen & Helsper, 2015). Digital inequalities do not necessarily refer to people's access to ICT but describe how people with the same chances of access to ICTs differ in their usage patterns as well as in the experienced outcomes of using ICTs (Hargittai, 2021). Digital inequalities are basically social inequalities in terms of people's socio-economic status, for example, sex, age, education, employment status, or ethnicity that are transferred to the digital world (Robinson et al., 2015).
In the field of privacy research, multiple empirical studies found that sociodemographic factors can shape people's privacy perceptions, abilities, and behaviors. For instance, age seems to be negatively related to the frequency and quality of applying privacy protective measures (Büchi et al., 2021; Matzner et al., 2016; Meier & Krämer, 2023). At the same time, however, younger persons seem to find privacy risks less severe (Bol et al., 2018; Meier and Krämer, 2023). A similar pattern emerges when it comes to education: higher educated persons seem to protect themselves better (Büchi et al., 2021) but are less concerned for their privacy (Smit et al., 2014). Finally, gender differences have been found. Men seem to protect better and have higher protection skills than women (Büchi et al., 2021; Meier and Krämer, 2023). These inequalities seem to be exploited, for instance, by advertisers who target user groups with different (e.g., gender-stereotypic) content and might, thus, contribute to maintaining stereotypes or marginalization (Bol et al., 2020). Additionally, elderly, less educated, and persons with a lower income were found to be less confident in handling personal (health-related) information which deterred them from active usage (Park, 2021). These studies indicate that sociodemographic and -economic factors contribute to making some user groups more vulnerable in terms of less privacy protection and advertisement manipulation. Besides having a higher risk to experience privacy invasions, some user groups also seem to have lower advantages from using ICTs.

Apart from sociodemographic factors, other variables seem to contribute to privacy inequalities as well. For instance, Redmiles and Buntain (2021) found that a lack of trust in data processing and a high level of privacy concerns prevented people from certain online behaviors. Moreover, impulsive and intuitive decision-makers seem to share personal information more carelessly as they are rather reward-driven while ignoring possible negative consequences (Ostendorf et al., 2020, 2022). Lastly, privacy cynicism (i.e., feeling powerless, resigned, and mistrusting about the privacy practices of many internet corporations) seems to be related to less privacy protection, thus, constituting another vulnerable group (Lutz et al., 2020). Hence, vulnerable groups appear to exist not only within certain social classes but also among persons who share similar personality or attitudinal characteristics. However, digital inequalities are far from being fully understood as there are also studies that did not find empirical evidence for inequalities in people's privacy perceptions and behaviors based on sociodemographic variables (e.g., Boerman et al., 2021). Thus, more research is required that is based on large and nationally representative (but also cross-national) samples to better understand digital inequalities regarding privacy perceptions, skills, and behaviors of certain social classes and groups.

## 3 Current Approaches

In the EU, there are approaches to enhance transparency and to increase informational self-determination of users obligated by the GDPR. Websites must inform users about the handling of personal information in privacy policies and users must have the choice of whether to consent to the use of tracking mechanisms (e.g., cookies) that exceed what is technically required. Research data, however, illustrates the limitations of these approaches. Privacy policies are ignored by a great majority of users (European Commission, 2019) while cookie consent banners are largely evaluated as annoying and
problematic in several instances (Giese & Stabauer, 2022; Pastore, 2020). Privacy policies are written in ways that require the cognitive abilities and technical knowledge to comprehend the textual information (Obar & Oeldorf-Hirsch, 2020), thus, excluding a great majority of persons who either do not bring the skills or the motivation to read privacy policies. Therefore, it can be argued that privacy policies in their current form are contributing to inequalities in people's informedness about company privacy practices. Not only would privacy policies need a revision in terms of general length and comprehension, so that an “average user” could extract important information from it. Privacy policies should additionally be enhanced in a way to assist various user groups with different needs, abilities, and levels of knowledge (e.g., children or elderly). Concerning cookie banners, (i.e., pop-up windows informing about cookies and allowing users to decide whether to accept or reject non-essential cookies) research points to shortcomings as well. Due to inconsistent presentation of these banners, users seem to behave heterogeneously across different websites as mostly one-click options (i.e., “accept all” and “reject all”) are preferred to immediately close cookie banners (Giese & Stabauer, 2022; Pastore, 2020). These options, however, are not always present (especially the reject option) and due to the usage of so-called dark patterns (e.g., highlighting or hiding certain options) users are often manipulated to consent to extensive data tracking (Waldman, 2020). While these practices are certainly problematic to all users, there is a lack of research investigating whether certain factors make some user groups especially resistant or vulnerable to consent and dark patterns. Conceivably, due to a lack of awareness or knowledge some users might be more prone to be tricked into consent which might lead to unpleasant experiences or feelings of powerlessness. As a first step, it would be in the interest of all users to make cookie banners uniform, with options to immediately accept and reject all non-functional cookies, and without highlighting certain (invasive) options. Also, privacy-by-default and privacy-by-design are very easily implementable ways that might bridge the shortcomings of privacy policies and cookie banners and could also address privacy issues caused by digital inequalities.

# 4 Current Research Gaps

Despite or because of the shortcomings in current implementations of legal obligations, past research has developed and tested several ways of how to assist users in managing their privacy online. For instance, researchers suggest the usage of privacy nudges (Acquisti et al., 2017), privacy icons (Efroni et al., 2019), privacy scores (Meier & Krämer, 2022), warning messages (Ostendorf et al., 2022), or shortened privacy policies (Meier et al., 2020b) to assist users in more informed and deliberate privacy behaviors. While empirical findings point to the usefulness of some of these measures (Meier & Krämer, 2022; Meier et al., 2020b; Ostendorf et al., 2022), there are still certain issues to be considered. First and foremost, this strand of research tends to ignore interindividual differences between users. This means that in most cases one intervention is tested for its general functionality in one sample without considering that different user groups might benefit from different interventions. So far, scholars have rather descriptively investigated digital inequalities in the realm of privacy, but there appears to be a lack of work designing and testing interventions to close gaps between user groups. Different
interventions, however, might be useful in distinct ways for different users. For instance, providing people with shortened privacy policies might still require a minimum level of knowledge to understand certain terms and the implication of privacy icons supposes training which might be more easily feasible for some user segments. Persons who make deliberate privacy decisions might benefit from more detailed information while impulsive and intuitive decision-makers might rather need short in situ interventions. A first indication for the latter claim can be seen in the study by Meier and Krämer (2022) who found that intuitive privacy decision-makers found the tested intervention (i.e., a Nutri-Score alike privacy risk indicator) more useful than rational decision-makers. Because of these subtle differences between various user segments that suppose distinct demands and needs, I argue that there is an urgent need to investigate which user groups (e.g., elderly or less educated persons) benefit from which type of interventions (e.g., providing detailed, step-by-step information) in order to assist and protect the most vulnerable user groups.

In addition, although empirical research studying privacy and digital inequalities is on the rise, very critical questions remain unaddressed so far. Especially research that examines the (negative) consequences of ICT usage is scarce. Previous research has primarily examined the third digital divide with respect to the positive outcomes (i.e., benefits) of ICT use, thus, neglecting the negative outcomes (e.g., privacy invasions) (Blank & Lutz, 2018; Lutz, 2019). However, not being able to adequately manage one's privacy online might lead to particularly sensitive negative experiences. Büchi and colleagues (2021) argue that especially marginalized groups run the risk of, for instance, increased privacy invasions which might negatively impact their psychological well-being and could reinforce existing social inequalities. Hence, there is an urgent need to better understand how sociodemographic and other factors not only deter people from experiencing positive outcomes of internet use but to simultaneously experience negative consequences, for instance, in the fashion of privacy invasions.

# 5 Next Steps

To conclude the chapter, I will provide an outlook on important possible next steps in privacy and digital inequalities research, considering the above-mentioned research gaps and demands. First, more work is needed to systematically *describe* digital inequalities regarding privacy. Previous works already indicate that sociodemographic factors play an important role in the ways of how people manage their privacy online (e.g., Büchi et al., 2021; Meier & Krämer, 2023; Park, 2021). Future studies should deepen the understanding of which variables (e.g., awareness, knowledge, skills, protection behaviors) are unequally distributed across social classes and focus on the causes of these inequalities. For instance, why do younger and more educated persons engage in better protection and why do women apparently manage their privacy less than men? Do they have an easier access to information or are these disparities due to differences in socialization? Future studies could shine light on these and further questions. Second, there is a need to study whether other social (e.g., having a migration background) and trait-like factors (e.g., impulsivity or privacy cynicism) *create* digital inequalities regarding privacy. It is conceivable that language barriers, cultural norms, or interindividual differences like
being prone to immediate rewards complicate privacy protection and create particularly vulnerable groups. Also, it is unclear whether vulnerable groups are the same across different countries or cultures or whether these groups differ around the world. Third, more research is needed on how sociodemographic and other factors lead to inequities in people's *experiences* of privacy risks and invasions. The literature regarding digital inequalities has mainly focused on the experience of advantages (or lack thereof) of using ICTs. However, because not adequately managing and protecting one's personal data online can entail severe negative consequences for individuals, studying which factors make it more likely that people experience privacy invasions is of great importance (see also Blank & Lutz, 2018; Lutz, 2019). Fourth, previous works that focused on *transparency* remedies have largely neglected digital inequalities. This means that these works had the implicit assumption that certain transparency cues work similarly among different user groups. However, elderly persons might need different kinds of information than younger persons. Users who already have a high level of knowledge and skills are likely to still require in situ information about potential privacy threats, but perhaps not in such detail as less literate people. Therefore, future studies should compare different kinds of transparency cues and support measures to find out for which user group which kind of cue works best. Moreover, besides digital assistance, easily achievable and ubiquitous digital training possibilities and contact points for digital assistance might be further options to balance inequalities.

Ultimately, however, research and society should identify ways of how to balance and overcome digital inequalities because privacy is not an individual matter rather than a collective phenomenon (Baruh & Popescu, 2017). This means that it is hard if not impossible “for any one person to have privacy without all persons having a similar minimum level of privacy” (Regan, 2002, p. 399). Transparency measures such as personalized cues may be a fast and easy cure, but they do not heal the underlying misconception of privacy as an individual responsibility which neglects the networked and collective nature of privacy.

## References

Acquisti, A., et al.: Nudges for privacy and security: understanding and assisting users' choices online. ACM Comput. Surv. (CSUR) **50**(3), 1–41 (2017). https://doi.org/10.1145/3054926

Baruh, L., Popescu, M.: Big data analytics and the limits of privacy self-management. New Media Soc. **19**(4), 579–596 (2017). https://doi.org/10.1177/1461444815614001

Baruh, L., Secinti, E., Cemalcilar, Z.: Online privacy concerns and privacy management: a meta-analytical review. J. Commun. **67**(1), 26–53 (2017). https://doi.org/10.1111/jcom.12276

Blank, G., Lutz, C.: Benefits and harms from Internet use: a differentiated analysis of Great Britain. New Media Soc. **20**(2), 618–640 (2018). https://doi.org/10.1177/1461444816667135

Boerman, S.C., Kruikemeier, S., Zuiderveen Borgesius, F.J.: Exploring motivations for online privacy protection behavior: insights from panel data. Commun. Res. **48**(7), 953–977 (2021). https://doi.org/10.1177/0093650218800915

Bol, N., et al.: Understanding the effects of personalization as a privacy calculus: analyzing self-disclosure across health, news, and commerce contexts. J. Comput.-Mediat. Commun. **23**(6), 370–388 (2018). https://doi.org/10.1093/jcmc/zmy020
Bol, N., Strycharz, J., Helberger, N., van de Velde, B., de Vreese, C.H.: Vulnerability in a tracked society: combining tracking and survey data to understand who gets targeted with what content. *New Media Soc.* **22**(11), 1996–2017 (2020). https://doi.org/10.1177/1461444820924631

Büchi, M., Festic, N., Just, N., Latzer, M.: Digital inequalities in online privacy protection: effects of age, education and gender. In: Hargittai, E. (ed.) Handbook of Digital Inequality, pp. 296–310. Edward Elgar Publishing (2021)

Büchi, M., Just, N., Latzer, M.: Caring is not enough: the importance of Internet skills for online privacy protection. Inf. Commun. Soc. **20**(8), 1261–1278 (2017). https://doi.org/10.1080/1369118X.2016.1229001

Efroni, Z., Metzger, J., Mischau, L., Schirmbeck, M.: Privacy icons: a risk-based approach to visualisation of data processing. Eur. Data Prot. Law Rev. **5**(3), 352–366 (2019). https://doi.org/10.21552/edpl/2019/3/9

European Commission: Special Eurobarometer 487a: The general data protection regulation. European Commission (2019). https://cnpd public.lu/content/dam/cnpd/fr/actualites/international/2019/ebs487a-GDPR-sum-en.pdf

Giese, J., Stabauer, M.: Factors that influence cookie acceptance. In: Fui-Hoon Nah, F., Siau, K. (eds.) HCI in Business, Government and Organizations, pp. 272–285. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-05544-7_21

Hargittai, E.: Introduction to the handbook of digital inequality. In: Hargittai, E. (ed.) Handbook of Digital Inequality, pp. 1–7. Edward Elgar Publishing (2021)

Lutz, C.: Digital inequalities in the age of artificial intelligence and big data. Hum. Behav. Emerg. Technol. **1**(2), 141–148 (2019). https://doi.org/10.1002/hbe2.140

Lutz, C., Hoffmann, C.P., Ranzini, G.: Data capitalism and the user: an exploration of privacy cynicism in Germany. New Media Soc. **22**(7), 1168–1187 (2020). https://doi.org/10.1177/1461444820912544

Matzner, T., Masur, P.K., Ochs, C., von Pape, T.: Do-it-yourself data protection—empowerment or burden? In: Gutwirth, S., Leenes, R., De Hert, P. (eds.) Data Protection on the Move. LGTS, vol. 24, pp. 277–305. Springer, Dordrecht (2016). https://doi.org/10.1007/978-94-017-7376-8_11

Meier, Y., Krämer, N.C.: The privacy calculus revisited: an empirical investigation of online privacy decisions on between- and within-person levels. Commun. Res. (2022). https://doi.org/10.1177/00936502221102101

Meier, Y., Krämer, N.C.: A longitudinal examination of internet users’ privacy protection behaviors in relation to their perceived collective value of privacy and individual privacy concerns. New Media Soc. (2023). https://doi.org/10.1177/14614448221142799

Meier, Y., Schäwel, J., Krämer, N.C.: The shorter the better? Effects of privacy policy length on online privacy decision-making. Media Commun. **8**(2), 291–301 (2020b). https://doi.org/10.17645/mac.v8i2.2846

Meier, Y., Schäwel, J., Kyewski, E., Krämer, N.C.: Applying protection motivation theory to predict Facebook users’ withdrawal and disclosure intentions. In: Gruzd, A., et al. (eds.) SMSociety 2020a: International Conference on Social Media and Society, pp. 21–29. Association for Computing Machinery (2020a). https://doi.org/10.1145/3400806.3400810

Micheli, M., Lutz, C., Büchi, M.: Digital footprints: an emerging dimension of digital inequality. J. Inf. Commun. Ethics Soc. **16**(3), 242–251 (2018). https://doi.org/10.1108/JICES-02-2018-0014

Obar, J.A., Oeldorf-Hirsch, A.: The biggest lie on the internet: ignoring the privacy policies and terms of service policies of social networking services. Inf. Commun. Soc. **23**(1), 128–147 (2020). https://doi.org/10.1080/1369118X.2018.1486870

Ostendorf, S., Meier, Y., Brand, M.: Self-disclosure on social networks - more than a rational decision-making process. Cyberpsychology: J. Psychosoc. Res. Cyberspace **16**(4), Article 2 (2022). https://doi.org/10.5817/CP2022-4-2
Ostendorf, S., Müller, S.M., Brand, M.: Neglecting long-term risks: self-disclosure on social media and its relation to individual decision-making tendencies and problematic social-networks-use. *Front. Psychol.* **11**, 543388 (2020). https://doi.org/10.3389/fpsyg.2020.543388

Park, Y.J.: Why privacy matters to digital inequality. In: Hargittai, E. (ed.) Handbook of Digital Inequality, pp. 284–295. Edward Elgar Publishing (2021)

Pastore, A.: Consent notices and cognitive cost after the GDPR: an experimental study [Doctoral dissertation, Catholic University of Portugal] (2020). http://hdl.handle.net/10400.14/31285

Redmiles, E.M., Buntain, C.L.: How feelings of trust, concern, and control of personal online data influence web use. In: Hargittai, E. (ed.) Handbook of Digital Inequality, pp. 311–325. Edward Elgar Publishing (2021)

Regan, P.M.: Privacy as a common good in the digital world. Inf. Commun. Soc. **5**(3), 382–405 (2002). https://doi.org/10.1080/13691180210159328

Robinson, L., et al.: Digital inequalities and why they matter. Inf. Commun. Soc. **18**(5), 569–582 (2015). https://doi.org/10.1080/1369118X.2015.1012532

Smit, E.G., Van Noort, G., Voorveld, H.A.: Understanding online behavioural advertising: user knowledge, privacy concerns and online coping behaviour in Europe. Comput. Hum. Behav. **32**, 15–22 (2014). https://doi.org/10.1016/j.chb.2013.11.008

Trepte, S., et al.: Do people know about privacy and data protection strategies? Towards the “online privacy literacy scale” (OPLIS). In: Gutwirth, S., Leenes, R., de Hert, P. (eds.) Reforming European Data Protection Law. LGTS, vol. 20, pp. 333–365. Springer, Dordrecht (2015). https://doi.org/10.1007/978-94-017-9385-8_14

Van Deursen, A.J., Helsper, E.J.: The third-level digital divide: who benefits most from being online? In: Communication and Information Technologies Annual. Emerald Group Publishing Limited (2015)

Waldman, A.E.: Cognitive biases, dark patterns, and the ‘privacy paradox.’ Curr. Opin. Psychol. **31**, 105–109 (2020). https://doi.org/10.1016/j.copsyc.2019.08.025