# Video games and speech privacy: A case study of
# Fortnite

Jarno Van Arkel, Martha Larson, Emmanuel Vincent

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Jarno Van Arkel, Martha Larson, Emmanuel Vincent. Video games and speech privacy: A case study of Fortnite. SPSC 2025 - 5th ISCA Symposium on Security and Privacy in Speech Communication, Aug 2025, Delft, Netherlands. pp.43 - 47, 10.21437/spsc.2025-7. hal-05416924

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# Video Games and Speech Privacy: A Case Study of Fortnite

Jarno van Arkel<fnref n="1" />, Martha Larson<fnref n="1" />, Emmanuel Vincent²

¹iCIS, Radboud University, Netherlands

²Université de Lorraine, CNRS, Inria, France

jarno.vanarkel@ru.nl, m.larson@cs.ru.nl, emmanuel.vincent@inria.fr

## Abstract

Digital environments such as video games increasingly include voice communications. However, papers pertaining to video game privacy concerns leave speech data unexplored. In order to illustrate threats that arise when user speech is recorded, we carried out a case study describing the video game *Fortnite*. Specifically, this case study describes the ability to link user speech to a persistent pseudonym, producing an *identifying record*. An identifying record contains all personal data that can be attributed to a person. These records can be augmented to include more personal data through data linkage based on speech content, attribute inference and speaker identification. By drawing attention to the exposure of speech data in Fortnite, this paper aims to launch a discussion on online speech privacy in video game environments.

**Index Terms:** video games, data linkage, speech privacy

## 1. Introduction

The increasing exploitation of speech data by companies and governments raises strong privacy concerns, which are regulated by laws such as the EU's General Data Protection Regulation (GDPR) and Japan's Act on the Protection of Personal Information (APPI) [1]. Research on privacy protection has mostly focused on voice anonymization, which aims to discard identifiable voice characteristics and preserve the linguistic and paralinguistic contents of one or more utterances [2]. Yet, the resulting legally validated privacy criteria [3] do not reflect the actual privacy issues in many real-life scenarios, which involve additional data. This data increases the chance that attackers build *identifying records*, which contain all personal data that can be attributed to one person via *linkage attacks* [4], i.e., attacks which connect data to identifiers.

A practical example of a set of identifying records built from data available online is the VoxCeleb dataset [5]. VoxCeleb was created by collecting celebrity interviews from the internet and applying facial recognition. Although the privacy of public figures is also of concern, here, we focus on raising awareness of the possibility of building a collection of identifying records of members of the general public, who are not necessarily aware of the availability of their spoken data. In this paper, we focus on a particular general-public context where user speech is commonly available: the video game *Fortnite*. We carry out a qualitative case study on how Fortnite players are vulnerable to linkage attacks that create a set of identifying records. Such identifying records could be constructed by a malicious attacker who aims to obtain privacy-sensitive information.

The game Fortnite is played by millions of players across the globe<fnref n="1" /> who talk to each other during gameplay. Video games are sometimes studied as a platform for online social interactions alongside online social networks [6]. In traditional social media, speech is publicly shared as a component of *user generated content* with the understanding that anyone can access it at any time. In contrast, speech in video games is typically a part of spontaneous human conversations, with the expectation that words spoken are as fleeting as they would be in face-to-face interactions. However, any player who can hear other players' speech can also collect it using screen capture software that records AV data and stores it in a file format such as *MP4*. The player may be recording their own gameplay to share on the internet. However, they player may also be an attacker, who is secretly recording other players. Attackers can also access Fortnite AV data by searching online and downloading recordings that have been previously shared.

In this paper, we study opportunities for data linkage based on AV data captured from Fortnite, providing the following two descriptions as contributions. The first description details how three linkage attacks can be used to construct an identifying record, connecting user speech to Fortnite identifiers. The second description outlines three attacks that use speech to obtain additional information relating to the victim's real life or online identity, which can be added to the identifying record. Note that, in conducting this research, we did not collect any user data nor perform an experimental application of attacks.

It is important to point out that the study of video games provides an opportunity to consider the threat of privacy attacks that are directed at a pseudonym that is not necessarily linked to a real-world identity. It is common for players to use the same pseudonym across various games, game-related websites, and communication platforms, e.g., Discord. The pseudonym becomes a central part of their online identity [7]. For this reason, the capture of user pseudonyms in AV data raises additional data linkage concerns. Using pseudonyms, an attacker can target the online identity of users across online contexts, causing discomfort and harm without knowing the real-world identity of the target.

This paper is broken down into the following sections. Section 2 lays a foundation by discussing established works related to video game privacy. Section 3 introduces Fortnite and illustrates the presence of speech and its ties to user pseudonyms. Section 4 provides an overview of how identifying records can be produced and transformed, and how this may lead to privacy harms. In Section 5 and Section 6, we reflect on ethical considerations and summarize our findings.

<footnote n="1">¹https://gamedo worldobserver.com/2023/11/21/fortnite-revenue-20-billion-epic-games-donald-mustard</footnote>
## 2. Related work

This section covers established video game privacy concerns that have been raised in academic research. To our knowledge, there is no existing literature focusing on user voices in video games.

### 2.1. Re-identification attacks

We start by discussing existing video game pseudonym concerns, and existing standards for online pseudonyms. An example of a video game related re-identification attack using persistent pseudonyms is seen in [8]. Here the video game console Xbox left users vulnerable through its universal display of user pseudonyms (called *gamertags*). In any online game, Fortnite included, a user's gamertag is exposed to all other players. An adversary was able to abuse data linked to gamertags to find sufficient identifying personal data and re-identify others. This resulted in users getting “swatted”, an attack where the police are informed of a fictitious extreme threat and show up armed to the victim's house: “*with nothing more than a player's gamertag, enough information could be successfully obtained from the Internet to carry out such malicious hoaxes.*” [8, p. 3240].

Hoepman [9] describes pseudonyms as *recognizable identifiers* that can be used to single out individuals. This is seen as both their purpose and a threat, as it allows data to be linked back to an individual. Considerations about the strength of online pseudonymity are given in [10]: “*how individuals are able to control disclosure*” and “*how often pseudonyms can be changed*” [10, p. 19] are especially relevant to the Xbox example, and to games such as Fortnite. Their pseudonyms cannot be changed with ease and are constantly displayed, leaving users more vulnerable to linkage attacks.

### 2.2. Attribute inference attacks

We now introduce existing inference attacks using video game data. Some video games support an official *application programming interface (API)* which can be queried to obtain information about the game and its players. By supplying a pseudonym, an API can be queried for information pertaining to a specific player. In [11], API results from the game *Dota 2* are combined with a player survey the authors performed. This provided sufficient training data for a classifier to infer privacy-sensitive attributes from API results. The authors specifically raised concern about the possibility of training a classifier to infer age, allowing an adversary to identify minors with high precision. Similarly, API results could be used to infer established friendships between players of the game *Halo Reach* [12]. The authors of [11] state the belief that their findings generalize to other video games with API support such as Fortnite.

The focus on APIs is a limitation of existing work on attribute inference attacks. Currently, video game APIs do not offer retrieval of user speech data. By restricting research of privacy threats to information available from APIs, researchers overlook large sources of personal data, which a real adversary will gladly harvest. Thus, in Section 4, we introduce an attacker who is assumed to obtain and scrape AV data from within Fortnite and online social networks.

## 3. Fortnite

This section provides a brief introduction to Fortnite. Moreover, it provides an impression of how much speech is present in Fortnite and how individual speakers can be identified.

### 3.1. Game description

Fortnite is an umbrella term for various gaming experiences within the same “Fortnite” program. While Fortnite universally implements voice chat features, the amount of speech varies. In many respects, Fortnite operates akin to a deck of cards. There are universal card games with established rules such as poker and blackjack. Similarly, Fortnite’s developer *Epic Games* has created official games. Two of these, *Save the World* and *Battle Royale*, spawned the initial success of Fortnite. In these versions of the game, players control a 3-dimensional animated character to fight against player and computer-generated enemies. Additionally, a deck of cards can also be used to create new card games with novel rules. A similar ability is granted by Fortnite as a result of *Unreal Engine* tools deployed within the game. These tools allow users to create a plethora of sub-games within Fortnite that are grouped under the name “Fortnite *Creative*”. Instead of discussing Fortnite’s official modes, the focus of this paper is on Creative. Creative’s player-generated sub-games are commonly referred to as *maps*. These maps hold a lot of value as a contemporary medium, as illustrated by the creation of a map by the Kamala Harris campaign during the 2024 U.S. presidential elections.<fnref n="2" /> A driving factor for interest in Creative maps is their support of voice chat communication between up to 50 players at once. The size of the potential volume of speech interaction makes Fortnite particularly worth studying.

### 3.2. Audio-Visual (AV) data in Fortnite

When a player speaks in Fortnite, a visual cue (loudspeaker) is presented in the user interface on the screen. Figure 1 shows the difference between users that are speaking (NotAnonymousJVA) and users that are not (the redacted gamertag). When multiple people speak at once, a visual cue is displayed for each speaker.

<figure><img src="image_2.png" /><figcaption>Figure 1: Image created by screen capture in Fortnite. The speaker icons in the enlarged section show a visual difference between a user who is speaking (top) and a user who is not speaking (bottom).</figcaption></figure>

<footnote n="2">²https://www.forbes.com/sites/paultassi/2024/10/29/i-played-the-kamala-harris-fortnite-questline-and-heres-what-i-found/</footnote>
When the Creative maps contain a large number of speakers, a small banner pops up for each user during the duration of their speech and information about non-speakers is not displayed. This banner conveys the same visual information as is displayed for the speaking user in Figure 1. Multiple banners, containing the user identifiers of those speaking, can be displayed at once. With up to 50 players in the same map, it is a common occurrence for several players to speak simultaneously. As a result, the speaker icon alone may not be sufficient to identify what speech belongs to which speaker. Another issue of relying on the speaker icon is that it is also activated by background noise. For these reasons, any speech data processing will require a speaker diarization and non-speech identification models.

Recall that any user in a Creative map can utilize screen capture software to record AV data and store it in a file. It is common practice for users to share the resulting data on social media platforms such as Youtube for entertainment purposes. However, an attacker could also personally enter a creative map and capture AV data. Moreover, by scraping social media platforms the attacker can obtain Fortnite AV data without ever entering the game.

# 4. Fortnite privacy attacks

In this section, we explain how Fortnite AV data can be used to link speech data to identifiable users, which can then be used in various speech-based attacks.

## 4.1. Linkage attacks

Powar et al. [4] define several key elements of data linkage attacks. They state that the objective of data linkage is to unite personal data from different sources. In order to explain data linkage, the authors introduce the definition of an *identifying record*, which contains all information that can be attributed to one or more persons. The target of an identifying record can be a specific person, or a *class* of people with common attributes. Note that the identifying record may be associated with a real-world identity or may be a pseudo identifier. In this paper, we deal only with the case of a *identifying records* associated with specific individual people.

A data linkage attack is a transformation of an identifying record that adds new data that describes the identified subject [4]. We represent these transformations as functions with inputs and outputs. The inputs are identifying records and information that augments them. The output is a new identifying record, containing additional information that describes the identified subjects. The attacks this section discusses are displayed as transformation functions in Table 1 and Table 2.

The linkage opportunity is broken down into three attacks.

**AV data processing** turns captured AV data, introduced in Section 3.2, into disjoint identifying records. Image recognition processes the video track of the AV data to compose a timeline of when each players, identified by Fortnite identifier (FnId₁, ..., FnIdₖ) speaks. Recall that the video might display multiple users speaking at once. Afterwards, this timeline is combined with a (speechi₁, ..., speechiₘ) timeline produced for each FnIdᵢ in range [1,k] from the audio track of the AV data by speaker diarization. By combining the two timelines, the attacker obtains unique pairings of identifiers and speech that create base identifying records for every distinct FnIdᵢ.

**Identifying record accumulation** allows the attacker to gather speech from across different AV data files into a single identifying record. Because the FnId is rarely subject to change, disjoint identifying records associated with the same user can be iteratively combined. Effectively, this is done by adding a new sample of speechi,m+1 to an existing {speechi,1, ..., speechi,m} set of speech samples.

**External identifier linkage** operates akin to record accumulation but it instead links any data from external sources to the identifying record, not just speech. Because it is common for users to reuse the same identifier [7], an attacker can obtain data describing a target user from other platforms. Inputs are a target (FnIdᵢ, {FnDataᵢ}) and an identifying record from an external source (ExternalIdⱼ, {ExternalDataⱼ}) where FnIdᵢ == ExternalIdⱼ. The output produced is a combined record (FnIdᵢ, {FnDataᵢ, ExternalDataᵢ}).

## 4.2. Other privacy attacks

In this subsection, three attacks are described that use the identifying record to obtain additional personal information.

**Speaker identification** transforms two identifying records (FnIdᵢ, {speechᵢ}) and (Identityⱼ, {speechⱼ}) into a combined record if a speaker identification model concludes that i and j are the same speaker. Speaker identification can be applied in three forms, depending on the aims and resources of the attacker. If the attacker has a large number of identifying records associated with Fortnite IDs and a large collection of speech collected outside of Fortnite associated with real-world identities, then the attacker can align the two and re-identify as many Fortnite IDs as possible. If the attacker has a particular real-world target, then only a small sample of speech from that target associated with the target's real-world identity can be used to identify the target's Fortnite ID. Conversely, an attacker with a large collection of speech associated with real-world identities can attempt to re-identify a target Fortnite ID.

<table><caption>Table 1: Linkage attacks that produce identifying records. Fn is a shorthand for Fortnite.</caption><thead><tr><td>Attack</td><td>Input</td><td>Additional resources</td><td>Output</td></tr></thead><tbody><tr><td>AV data processing</td><td>AV data containing (FnId<sub>1</sub>, ..., FnId<sub>k</sub>) speakers and corresponding (speechi<sub>1</sub>, ..., speechi<sub>m</sub>) for every FnId<sub>i</sub> in range [1,k]</td><td>Speaker diarization, non-speech identification and image recognition models</td><td>(FnId<sub>i</sub>, speechi<sub>1</sub>), ..., (FnId<sub>i</sub>, speechi<sub>m</sub>) identifying records for every FnId<sub>i</sub> in range [1,k]</td></tr><tr><td>Identifying record accumulation</td><td>(FnId<sub>i</sub>, {speechi<sub>1</sub>, ..., speechi<sub>m</sub>}) and (FnId<sub>i</sub>, speechi<sub>m+1</sub>) identifying records</td><td>-</td><td>(FnId<sub>i</sub>, {speechi<sub>m</sub>, ..., speechi<sub>m+1</sub>}) record</td></tr><tr><td>External identifier linkage</td><td>(FnId<sub>i</sub>, {FnData<sub>i</sub>}) and (ExternalId<sub>j</sub>, {ExternalData<sub>j</sub>}) records where FnId<sub>i</sub> == ExternalId<sub>j</sub></td><td>Data from external sources</td><td>Combined (FnId<sub>i</sub>, {FnData<sub>i</sub>, ExternalData<sub>i</sub>}) record</td></tr></tbody></table>
<table><caption>Table 2: Privacy attacks that make use of speech from identifying records. Fn is a shorthand for Fortnite.</caption><thead><tr><th>Attack</th><th>Input</th><th>Additional resources</th><th>Output</th></tr></thead><tbody><tr><td>Speaker identification</td><td>(FnId<sub>i</sub>, {speech<sub>i</sub>}) and (Identity<sub>j</sub>, {speech<sub>j</sub>}) records</td><td>Speaker identification model</td><td>Combined (FnId<sub>i</sub>, Identity<sub>j</sub>, {speech<sub>i</sub>, speech<sub>j</sub>}) record if i and j are the same speaker</td></tr><tr><td>Attribute inference</td><td>(FnId<sub>i</sub>, {speech<sub>i</sub>}, {FnData<sub>i</sub>}) record</td><td>Attribute inference classifiers</td><td>(FnId<sub>i</sub>, {speech<sub>i</sub>}, {FnData<sub>i</sub>}, {attributes<sub>i</sub>}) record</td></tr><tr><td>Content analysis</td><td>(FnId<sub>i</sub>, {speech<sub>i</sub>}) record</td><td>Speech-to-text and Named Entity Recognition models</td><td>(FnId<sub>i</sub>, {speech<sub>i</sub>}, {terms of interest<sub>i</sub>}) record</td></tr></tbody></table>

**Attribute inference** employs speech-based classifiers to infer privacy-sensitive attributes. Some example attributes include gender and age, but also abstract concepts such as mental health [13]. The use of classifiers allows an attacker to add derived attributes to a (FnId<sub>i</sub>, {speech<sub>i</sub>}) record. We note that works such as [14] also discuss different types of personal data that can be found in video games. Prior research has demonstrated attributes such as age can be similarly inferred from video game data other than speech [11, 12]. Thus, we include other Fortnite data as a potential input that the attacker might obtain from the AV recording. Moreover, the external identifier linkage attack may provide an attacker with additional data that can also be fed to attribute inference classifiers. The inference of sensitive attributes offers the opportunity for an adversary to profile a large sample of identifying records, and target specific groups of users with further attacks. Given that Fortnite is largely popular with a young audience, profiling users based on attributes is especially concerning.

**Speech content analysis** of a (FnId<sub>i</sub>, {speech<sub>i</sub>}) record by a speech-to-text model can be performed for two key purposes: surveillance and identification. An attacker could target a specific user with intentions of surveillance, collecting everything the target has said. The dissemination of collected speech may cause privacy harms such as disclosure, distortion and blackmail [15]. Instead of focusing on a target, an attacker could also perform an untargeted attack analyzing all speech for terms of interest. Terms of interest can be analyzed by a Named Entity Recognition (NER) model that allows for the detection of locations, or other external identifiers that can be used for external identifier linkage. Moreover, Section 3.2 introduced the idea that an attacker can manually capture AV data in Fortnite by entering a Creative map. This has additional implications for speech content based attacks. The participation of the attacker allows them to elicit speech regarding certain topics such as named entities that help identify individual users.

In short, various forms of personal data can be linked back to each user's pseudonym in Fortnite, putting privacy at risk. Due to the abundance of user speech in Fortnite, we consider these attacks to be entirely feasible should an attacker wish to execute them. By collecting speech of users, it is likely an attacker could obtain sufficient resources to invade the privacy of especially susceptible players who reuse their online pseudonym across the digital space.

### 4.3. Failure of existing privacy measures

Fortnite's existing privacy measures fail to protect from the attacks described in this paper. Like most other games, Fortnite is played through a digital gaming platform. Theirs is called the *Epic Games Store*, owned by Fortnite's developers. Account creation for the Epic Games Store requires players to pick a persistent pseudonym, used for identification across all of the video

games hosted on the platform. However, players using Xbox or PlayStation consoles are instead made to identify themselves with the pseudonyms associated with their respective platform. These console pseudonyms are even harder to change than a typical Fortnite identifier, and are used across more contexts. This maintains connections to personal data which were abused by attackers in [8].

In an effort to protect user privacy, Fortnite offers a setting which transforms the user identifier to “Anonymous”. However, Figure 1 was captured in a Creative map where the setting was not working as expected. Players are able to see the "NotAnonymousJVA" user pseudonym despite that user having turned on the “Anonymous” setting. Epic games could address this problem by simply extending the “Anonymous” setting to Creative. However, offering this as the only privacy protection method goes against the Full Functionality principle of Privacy by Design [16]. The setting positions privacy as a zero-sum issue that can only be protected by impairing the user experience. Privacy protections should not be limited to a forced binary choice between being fully identifiable or fully unidentifiable. Other solutions regarding user pseudonyms in Fortnite are further discussed in [17], a thesis project on which this paper is based.

## 5. Responsible disclosure

In this section we discuss the ethical concerns of our study. Publicizing our findings may inspire a malicious actor to conduct the attacks we have described. However, one key argument that motivates the publication of our study is the uncertainty regarding if similar attacks already occur in the present. There is no way to ascertain that AV data is not already being accumulated and processed at this very moment. Moreover, additional data which contributes to this opportunity for attacks is constantly published on online social networks. The vulnerability in Fortnite can only be addressed by alerting the scientific community, regulators and Fortnite's developers. Epic Games have been sent a copy of this paper on June 4, 2025.

## 6. Conclusion

We have provided insight into Fortnite and how the opportunity it represents to collect identified user speech gives rise to privacy risks. By identifying these risks, we hope to launch a discussion on speech privacy in online video game environments which helps close a gap in video game privacy research. Moreover, we highlight pseudonyms as a method of targeting online identities. Further research is necessary to offer improved practices that impede an attacker's ability to compose identifying records which link personal data to pseudonyms. By raising awareness of these concerns to the industry and relevant legislators, our hope is that data subjects in online environments will receive better privacy protection.
7. References

[1] A. Nautsch, A. Jimenez, A. Treiber, J. Kolberg, C. Jasserand, E. Kindt, H. Delgado, M. Todisco, M. A. Hmani, A. Mtibaa, M. A. Abdelraheem, A. Abad, F. Teixeira, D. Matrouf, M. Gomez-Barrero, D. Petrovska-Delacrétaz, G. Chollet, N. Evans, T. Schneider, J.-F. Bonastre, and C. Busch, "Preserving privacy in speaker and speech characterisation," *Computer Speech and Language*, vol. 58, pp. 441–480, 2019.

[2] N. Tomashenko, B. M. L. Srivastava, X. Wang, E. Vincent, A. Nautsch, J. Yamagishi, N. Evans, J. Patino, J.-F. Bonastre, P.-G. Noé, and M. Todisco, "Introducing the VoicePrivacy initiative," in *Interspeech*, 2020, pp. 1693–1697.

[3] N. Vauquier, B. M. L. Srivastava, S. A. Hosseini, and E. Vincent, "Legally validated evaluation framework for voice anonymization," in *Interspeech*, 2025.

[4] J. Powar and A. R. Beresford, "SoK: Managing risks of linkage attacks on data privacy," *Proceedings on Privacy Enhancing Technologies*, vol. 2023, no. 2, pp. 97–116, 2023.

[5] A. Nagrani, J. S. Chung, and A. Zisserman, "VoxCeleb: A large-scale speaker identification dataset," in *Interspeech*, 2017, pp. 2616–2620.

[6] Y. Piao, K. Ye, and X. Cui, "Privacy inference attack against users in online social networks: A literature review," *IEEE Access*, vol. 9, pp. 40417–40431, 2021.

[7] N. Crenshaw and B. Nardi, "What's in a name? naming practices in online video games," in *1st ACM SIGCHI Annual Symposium on Computer-Human Interaction in Play*, 2014, p. 67–76.

[8] A. Podhradsky, R. D'Ovidio, P. Engebretson, and C. Casey, "Xbox 360 hoaxes, social engineering, and gamertag exploits," in *2013 46th Hawaii International Conference on System Sciences*, 2013, pp. 3239–3250.

[9] J. Hoepman, "Privacy is hard and seven other myths," *European Data Protection Law Review*, vol. 9, no. 2, pp. 104–111, 2023.

[10] A. Cooper, H. Tschofenig, D. B. D. Aboba, J. Peterson, J. Morris, M. Hansen, and R. Smith, "Privacy considerations for internet protocols," RFC 6973, Jul. 2013. [Online]. Available: https://www.rfc-editor.org/info/rfc6973

[11] P. P. Tricomi, L. Facciolo, G. Apruzzese, and M. Conti, "Attribute inference attacks in online multiplayer video games: A case study on Dota2," in *13th ACM Conference on Data and Application Security and Privacy*, 2023, p. 27–38.

[12] S. Merritt, A. Jacobs, W. Mason, and A. Clauset, "Detecting friendship within dynamic online interaction networks," in *International AAAI Conference on Web and Social Media*, 2021, pp. 380–389.

[13] J. L. Kröger, O. H.-M. Lutz, and P. Raschke, "Privacy implications of voice and speech analysis — information disclosure by inference," in *Privacy and Identity Management. Data for Better Living: AI and Privacy. Privacy and Identity 2019*, M. Friedewald, M. Önen, E. Lievens, S. Krenn, and S. Fricker, Eds., 2020, pp. 242–258.

[14] J. L. Kröger, P. Raschke, J. Percy Campbell, and S. Ullrich, "Surveilling the gamers: Privacy impacts of the video game industry," *Entertainment Computing*, vol. 44, p. 100537, 2023.

[15] D. J. Solove, "A taxonomy of privacy," *University of Pennsylvania Law Review*, vol. 154, no. 3, pp. 477–560, 2006.

[16] A. Cavoukian, "Privacy by design: The 7 foundational principles," 2009. [Online]. Available: https://privacy.ucsc.edu/resources/privacy-by-design---foundational-principles.pdf

[17] J. van Arkel, "Video Games and Privacy: A Study of Data Linkage Opportunities," Bachelor's Thesis, Radboud University, Nijmegen, Netherlands, 2025.