# A Framework for Supporting PET Selection Based on GDPR Principles

Sebastian Pape, Anis Bkakria, Badreddine Chah, Maurice Heymann, Sarah

Syed-Winkler

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Sebastian Pape, Anis Bkakria, Badreddine Chah, Maurice Heymann, Sarah Syed-Winkler. A Framework for Supporting PET Selection Based on GDPR Principles. International Conference on Availability, Reliability and Security, Aug 2025, Ghent, Belgium. hal-05081314

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# A Framework for Supporting PET Selection Based on GDPR Principles

Sebastian Pape<sup>1,2[0000-0002-0893-7856]</sup>, Anis Bkakria<sup>3[0000-0002-9758-4617]</sup>, Badreddine Chah<sup>4[0009-0003-2665-8679]</sup>, Maurice Heymann<sup>1[0009-0008-5603-8653]</sup>, and Sarah Syed-Winkler<sup>1[0000-0002-3197-9456]</sup>

**Abstract.** The General Data Protection Regulation (GDPR) lists seven privacy principles relating to the processing of personal data and requires the processor to implement appropriate technical and organizational measures (TOMs). In practice, TOMs are often addressed by organizational measures, but technical measures, i. e. Privacy-Enhancing Technologies (PETs), are rarely used to address them. One reason for that might be the challenge to find an appropriate PET. The contribution of our paper is a framework to support the selection of PETs based on GDPR principles. For that purpose, we provide mappings between the trust model and GDPR principles, and between PET types and GDPR principles. Furthermore, we provide an assessment of the different maturity levels of PETs. We evaluated our framework by applying it to three different use cases in the automotive domain and it proved to be highly effective in guiding the identification and selection of candidate PETs.

**Keywords:** Privacy Enhancing Technology Selection · GDPR Principles · Trust Model

## 1 Introduction

With the introduction of the General Data Protection Regulation (GDPR) [27] in 2018, the European Union marked a major step forward in data privacy legislation, enforcing comprehensive requirements on how organizations must handle personal data. By establishing this regulation, individuals are empowered to have more control over their personal data, while organizations are held accountable for processing, storing, and collecting their information. The GDPR lists 6 principles in Art. 5 relating to processing of personal data and additionally requires the controller to be able to demonstrate compliance with them (cf. Sect 2.1). Art. 32 further requires the processor to "implement appropriate technical and organizational measures (TOMs) to ensure a level of security appropriate to the risk". In practice, TOMs are often addressed by organizational measures,

<footnote n="1">¹ Continental Automotive Technologies Gmbh, Frankfurt, Germany,
{firstname.lastname}@continental.com</footnote>

<footnote n="2">² Goethe University Frankfurt, Germany</footnote>

<footnote n="3">³ IRT SystemX, Palaiseau, France</footnote>

<footnote n="4">⁴ CIAD UMR 7533, Univ. Bourgogne-Franche-Comté, UTBM, F-90010 Belfort, France</footnote>
that is, integrating legal agreements such as user consent or commissioned data
processing into existing processes. Technical measures, i. e. Privacy-Enhancing
Technologies (PETs), are rarely used to address the privacy principles [19]. One
of the reasons might be that it is still difficult to select, implement, configure, and
run most PETs. In this paper, we propose a framework to support the selection
of PETs based on the mentioned GDPR principles.

The proposed framework aims to assist in designing service architectures
by identifying suitable PETs that are in line with GDPR principles. Architects
must define their trust model and the data-related functions of their service.
The framework supports identifying the relevant GDPR principles that can be
enforced with PETs based on the trust model. Therefore, we map PET types
to GDPR principles to support the selection process. Furthermore, a qualitative
comparison of PETs is provided to evaluate their trade-offs in terms of utility,
practicability, and robustness. We evaluated our framework by applying it to
three different use cases in the automotive domain.

The contribution of our paper is a framework for PET selection based on
GDPR principles. It is based on mappings between trust model and GDPR
principles, and between PET types and GDPR principles; and on an assessment
of the PETs' maturity level.

The remainder of this paper is structured as follows: Section 2 provides background and reviews related work. Section 3 introduces the methodology of our research. Section 4 describes our framework along with its evaluation in Section 5 and the discussion in Section 6. Section 7 concludes the work.

## 2 Background and Related Work

In this section, we recap the GDPR privacy principles and trust models, before
briefly discussing related work.

### 2.1 Background

*Privacy Principles* According to the GDPR [27, Art. 5 1.], six principles apply when processing personal data:

**Lawfulness, Fairness, and Transparency** - data is processed lawfully, fairly, and in a transparent manner.

**Purpose Limitation** - data collection is limited to previously specified, explicit, and legitimate purposes.

**Data Minimization** - data is adequate, relevant, and limited to what is necessary in relation to the purposes of the data collection.

**Accuracy** - data is accurate and kept up to date where necessary.

**Storage Limitation** - data is kept in a form which permits identification of data subjects for no longer than necessary in order to fulfill the purposes.

**Integrity and Confidentiality** - the data controller should apply appropriate technical or organizational measures to ensure appropriate security of the personal data, including protection against unlawful processing and accidental loss, destruction, or damage.
In addition, the data controller is responsible for complying with previous principles and should be able to demonstrate compliance (**Accountability**).

*Trust Models* When dealing with privacy and data protection concerns, trust relations between the data owner and the entity processing and using the data must be considered. Focusing on the previous relation, three main adversary models are considered in the literature [28, 32]: In the *untrusted model*, the data owner does not trust any of the entities responsible for processing or using the data to be outsourced. The former is then responsible for correctly enforcing his privacy requirement on the data. In the *semi-trusted model*, the data owner considers the third-party entity going to process or use his or her data as an honest-but-curious entity. It is honest as it is supposed to not deviate from the defined protocol but may attempt to learn as much information as possible about the data owner out of the outsourced data. In the *trusted model*, the data owner trusts a third-party entity, which is in charge of enabling the processing of their data while protecting the private and sensitive parts.

## 2.2 Related Work

There are proposals for the selection and integration of PETs based on privacy patterns [48, 67], privacy strategies [44], or threats [18, 20] along some general considerations of privacy requirements in the system engineering process or building systems based on privacy by design [19, 35, 36]. Drozd [23] maps privacy patterns to privacy principles from the ISO/IEC 29100. Thus, to the best of our knowledge, there is no prior work supporting the selection of PETs based on GDPR principles. This is supported by Kostova et al. [50] who found that only roughly half of the investigated academic papers are providing mappings to PETs at all and further research identifying gaps in the privacy engineering process in practise [64]. Closest to our work is the work by Koolen et al. [49]. They offer guidance on how to assess the concept of ‘appropriate TOMs’ by considering cybersecurity maturity models. However, we focus on the selection of technical measures, PETs, specifically.

Since our use cases stem from the automotive domain, we also investigated papers discussing the selection of PETs in the automotive domain: Al-Momani et al. [2] explored the usefulness of privacy patterns in improving privacy in future automotive systems. Chah et al. [17, 18] use the LINDDUN methodology to identify and analyze privacy threats. Bella et al. [9] investigate privacy policies for cars similar to the recent Mozilla study [15] as well as user concerns and find that privacy for cars is insufficiently understood, mostly due to a lack of awareness. Syed-Winkler et al. [81] propose a system model for enforcing purpose limitation, and Pape et al. [68] propose a system model to identify suitable locations in the vehicle to integrate PETs. Löbner et al. [56?] evaluate different de-identification techniques based on two automotive use cases.
# 3 Methodology

This section provides an overview of the underlying methods we used to create the framework. For the assessments and evaluations we did, we followed a rigorous approach of the four-eyes principle: One author proposed a first assessment which together with the corresponding reasoning was discussed with a second author. In case of disagreements, those have been discussed and, if necessary, the corresponding assessment and/or reasoning adjusted.

*Application of Trust Models to GDPR Principles* To the best of our knowledge, the relevance of the different GDPR principles and the data processing entity's need to demonstrate its fulfillment to the data owner, have not been covered in related literature. Therefore, we carried out the assessment ourselves: Tab. 1 shows the result and is presented along with the reasoning in Sect. 4.1.

*Framework for Aligning PET Types with GDPR Principles* To map PETs to GDPR principles, we first categorized specific PETs into PET types and classifications. We then mapped these PET types and classifications to four distinct layers, including processing, storage, communication, and physical layer. Subsequently, we analyzed the role of each PET to determine its contribution to GDPR compliance. This involved mapping each PET to the relevant GDPR principles, with a clear justification provided for each connection. To offer a concise overview of the relationships between GDPR principles and the corresponding PETs, we present a tabular representation (cf. Tab.2 in Sec.4.2).

*Assessment of PET Maturity* For most of the qualitative assessment, we relied on literature benchmarking, drawing from established studies, technical analyses, and empirical results available in research papers and industry reports. For each PET, we referenced the specific literature from which the assessment metrics were derived, ensuring traceability and transparency of the assessment. Additionally, we provided detailed reasoning and supporting evidence to substantiate the assigned evaluations, taking into account factors such as the scope of the benchmarking, the nature of the use case, and the relevance of performance metrics (cf. Tab. 3 and Tab. 4 in Sect. 4.3). In cases where insufficient material or no comprehensive benchmarking data was available, we refrained from finalizing an evaluation. Instead, these PETs were noted as requiring further research or more robust empirical validation. This cautious approach ensures that assessments are not based on speculation or incomplete evidence, maintaining the reliability and credibility of the overall evaluation framework.

*Evaluation* We used three different use cases from the automotive domain for evaluation: *Platooning* – where a group of vehicles drive together with many privacy challenges especially when trying to find suitable tracks of platoon leaders, *Location-Based Service (LBS)* – trying not to reveal more location information as necessary and still being able to get navigation support to specific points of interests, and *Usage-Based Insurance (UBI)* – when insurance premiums are charged based on the customers’ usage and driving habits:
**Platooning** [7] is a technique closely related to fully automated driving. In this method, a designated platoon leader drives a route and allows other vehicles to join the platoon, following autonomously. This approach enhances both convenience and efficiency. The use case requires vehicles to have a basic set of sensors to sense the environment, cruise control and lane-keep assistance, and positioning including a high-definition map. Leaders announce their routes and followers privately compute their own, verifying leader trustworthiness before joining. While a server can facilitate this, platooning is possible without one. During operation, V2X messages manage smooth merging, departures, and route deviations. The main privacy challenge is to form a platoon without revealing sensitive information about participating vehicles.

**Location-Based Services** provide personalized, location-aware services such as route optimization, nearby recommendations, and contextual information to enhance the user experience. LBS rely on real-time GPS and user preferences to offer timely, relevant data. To deliver accurate services, the LBS provider (LBSP) collects and processes specific data from users, including current position (GPS data), direction of movement (for anticipating needs), and user queries (preferences for points of interest like restaurants or parks). The reliance of LBS on continuous real-time location data collection brings inherent privacy challenges for users. The sensitive nature of location information can expose users to risks if mismanaged, including potential tracking of movement patterns and revealing personal routines.

**Usage-Based Insurance** identifies risky driving behaviors to reduce road accidents. This use case focuses on Pay-As-You-Drive Insurance<fnref n="5" /> where premiums depend on driving behavior. We explore how Advanced Driver Assistance Systems (ADAS) detect hazardous driving and use vehicle connectivity to update driver profiles dynamically. However, balancing safety incentives with privacy concerns remains a challenge, as does ensuring accurate driver profiling for fair premium calculation.

To evaluate the usefulness and correctness of our framework, we investigated PET candidates based on our framework for each of the three scenarios. We then checked if they can be applied to fulfill one or more of the GDPR principles.

# 4 Framework for Selection of PETs

One of the primary goals of our framework is to provide the ability to select candidate PETs tailored to specific use cases based on GDPR principles. The use case establishes a detailed framework by defining the entities involved, the data required, and the computations necessary to achieve their intended functionalities, alongside their corresponding trust levels. Fig. 1 provides an overview of the necessary steps. As preparation (blue steps, outside of the scope of our framework), the use case needs to be described, involved entities need to be identified along

<footnote n="5">⁵ UBI Source: https://www.mobilize-fs.com/fr/actualites/pay-you-drive-et-pay-how-you-drive-nos-premieres-offres-dassurance-connectee</footnote>
<figure><img src="image_2.png" /><figcaption>**Fig. 1.** Proposed Approach: Blue/Green steps are outside/inside the scope</figcaption></figure>

with the established trust model. The next steps (in green) are in the focus of our framework: Based on the trust model, relevant GDPR principles are identified (step 3, cf. Sect. 4.1). The GDPR principles are then mapped to PET types (step 4, cf. Sect. 4.2 to identify candidate PETs that can be applied to enforce these principles. PETs provide privacy-preserving data processing with varying guarantees (see Tables 3 and 4), such as deterministic encryption, order-preserving encryption, and k-anonymity, each supporting different computational needs. Step 5 of the methodology involves mapping these processing needs to the set of PETs they provide, which allows us to select a set of candidate PETs suitable for a given scenario.

Furthermore, we provide an assessment of the maturity of PETs in terms of utility, scalability, and robustness to support the PET selection process (step 6, cf. Sect. 4.3). Finally, trade-offs between utility, privacy, and efficiency must be made specifically for the use case, which is beyond the scope of our framework.

## 4.1 Application of Trust Model to GDPR Principles

Tab. 1 shows the application of the trust model towards GDPR principles. We consider two dimensions: i) *Relevance* of the principle, which does not imply that there are irrelevant principles; however, within each trust model, certain principles may hold greater relevance than others. ii) *Demonstrability* which refers to the entity's capability not only to follow the principle, but also to provide accountability.

**Lawfulness, Fairness, and Transparency** is relevant for untrusted and semi-trusted data processors and owners, but most relevant for those who are untrusted, as this increases the impact should they be caught and their data usage model not comply with the GDPR. For trusted data processors, it is assumed that they will respect the law and may even exceed compliance requirements.

**Purpose Limitation** is mostly relevant for untrusted and semi-trusted data processors. As these entities are not expected to respect this principle, technical enforcement is crucial. Trusted data processors are expected to adhere to the agreed purpose limitation, regardless of technical enforcement.
<table><caption>**Table 1.** Relevance and Accountability of GDPR Principles in Relation to the Trust Model</caption><thead><tr><th rowspan="2">Trust Model<br>GDPR<br>Principles</th><th colspan="3">Relevance</th><th colspan="3">Accountability</th></tr><tr><th>Untrusted</th><th>Semi-trusted</th><th>Trusted</th><th>Untrusted</th><th>Semi-trusted</th><th>Trusted</th></tr></thead><tbody><tr><td>Lawfulness, Fairness, and Transparency</td><td>●</td><td>●</td><td>○</td><td>●</td><td>●</td><td>○</td></tr><tr><td>Purpose Limitation</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>○</td></tr><tr><td>Data Minimization</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>○</td></tr><tr><td>Accuracy</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>○</td></tr><tr><td>Storage Limitations</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>○</td></tr><tr><td>Integrity and Confidentiality</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>○</td></tr><tr><td>Accountability</td><td>●</td><td>●/○</td><td>●</td><td>N/A</td><td>N/A</td><td>N/A</td></tr></tbody></table>

● = high; ○ = mid; ○ = low

**Data Minimization** is relevant for all data processors, as it limits the risk of unintended data leakage. However, given that semi-trusted and untrusted data processors may be curious, it is even more important to prevent them from accessing unnecessary data.

**Accuracy** Ensuring the erasure of outdated and inaccurate data is relevant for all data processors. However, due to the difficulty of enforcement, the relation to the trust model is moderate and trusted data processors are expected to comply.

**Storage Limitation** Follows the same argumentation as accuracy.

**Integrity and Confidentiality** This principle is important for all data processors, as security vulnerabilities may lead to data leakage. However, demonstrating and proving the implementation of security measures is challenging. Therefore, the importance is lower for the trusted data processor, where it is assumed that all necessary measures are in place.

**Accountability** is addressed within the discussion of the various principles. Not surprisingly, the desired level of demonstrability increases as trust in the data-owning entity decreases. However, we have not discussed why a user should trust the data owner or processor. One way to gain trust is to demonstrate responsible and lawful data processing to users.

## 4.2 Mapping GDPR Principles to PET Types

Each of the four distinct layers (Processing, Storage, Communication, and Physical) involve specific PET types to handle sensitive data. Tab. 2 presents the list of PETs for each layer. These PETs support compliance with one or more GDPR principles, and in the following section, we will clarify the connection between each PET and the corresponding GDPR requirement:

**Processing Layer** In this layer, the selected PETs align with one or more of the following GDPR principles: lawfulness, fairness, transparency; purpose
limitation; data minimization; integrity and confidentiality; and accountabil-
ity. First, *Secure and Outsourced Computation* ensures purpose limitation
by processing data for specific purposes without exposing raw data and
guarantees integrity and confidentiality by encrypting data during processing
to prevent unauthorized access and manipulation. Second, *Anonymization*
ensures data minimization so that only the necessary data are processed
while removing or concealing identifiers that are not required for the intended
purpose. Third, *traceability* techniques, such as data tagging and digital water-
marking, comply with data minimization because they allow organizations to
track and monitor data use without requiring storage or sharing of excessive
personal information. They also partially comply with lawfulness, fairness,
and transparency by tracking data use, purpose limitation by enforcing proper
usage, and accountability by creating clear audit trails to demonstrate GDPR
compliance.

**Storage Layer** We identify two types of PETs, including, first, data at rest encryption such as conventional encryption to ensure the integrity and confidentiality of the data. Then, Attribute-Based Encryption (ABE) [11] in the storage layer ensures purpose limitation by restricting access based on specific policies, data minimization by granting access only to necessary data, and integrity and confidentiality by encrypting data and limiting decryption to authorized users. In addition, TEEs [6] ensure compliance with purpose limitation, storage limitation, and the principles of integrity and confidentiality by guaranteeing data integrity, data confidentiality, and code integrity. The storage system partially enforces storage limitation policies by supporting timeline-based deletion, depending on the specific implementation case. Second, immutability, such as in blockchain-based solutions [84], ensures lawfulness, fairness, and transparency by providing a tamper-proof, transparent record of data transactions. It supports integrity and confidentiality by safeguarding data against unauthorized changes while maintaining secure access controls. Additionally, it enhances accountability by creating an auditable and verifiable trail of all actions, ensuring traceability and responsibility.

**Communication Layer** This layer incorporates three PET types, which we map to the relevant GDPR principles. First, encryption techniques clearly ensure integrity and confidentiality within the communication layer. Second, anonymity techniques like Mixnet Protocol [70], Tor-related Protocols [59], and Random Walk/DHT [73] Protocols comply with data minimization in the communication layer by obfuscating user identities and reducing the amount of identifiable information transmitted, ensuring only necessary data is exposed. Third, Authentication techniques such as Digital Signatures, Certificateless Signatures [45], Quantum-safe Signatures [77], and Homomorphic Signatures [82] ensure integrity and confidentiality by verifying data authenticity and protecting it from unauthorized access. In addition, it guarantees accountability by creating verifiable records of data interactions and user actions.

**Physical Layer** Encryption techniques such as ABE and transciphering [58] can be applied to meet some GDPR principles. For the same reasons explained in
<table><caption>**Table 2.** Mapping PETs to GDPR Principles</caption><thead><tr><th>Layer</th><th>PET & Classification</th><th>GDPR Principles</th><th>LFT</th><th>PL</th><th>DM</th><th>ACU</th><th>SL</th><th>IC</th><th>ACT</th></tr></thead><tbody><tr><td rowspan="10">Processing</td><td rowspan="5">Secure and outsourced computation</td><td>Trusted execution environments [24, 51]</td><td>✓</td><td></td><td></td><td></td><td></td><td>✓</td><td></td></tr><tr><td>Private Information Retrieval [34, 43, 69] (✓)</td><td>✓</td><td>✓</td><td></td><td></td><td></td><td>✓</td><td></td></tr><tr><td>Homomorphic Encryption [47, 86]</td><td>✓</td><td></td><td></td><td></td><td></td><td>✓</td><td></td></tr><tr><td>Secure Multiparty Computation [4]</td><td>✓</td><td></td><td></td><td></td><td></td><td>✓</td><td></td></tr><tr><td>Zero-knowledge proofs [62]</td><td>✓</td><td></td><td></td><td></td><td></td><td>✓</td><td></td></tr><tr><td rowspan="3">Anonymization</td><td>Semantic [5, 12]</td><td></td><td></td><td>✓</td><td></td><td></td><td></td><td></td></tr><tr><td>Syntactic [61, 71]</td><td></td><td></td><td>✓</td><td></td><td></td><td></td><td></td></tr><tr><td>Perturbation [22]</td><td></td><td></td><td>✓</td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="2">Traceability</td><td>Data tainting [65]</td><td>(✓) (✓)</td><td>✓</td><td></td><td></td><td></td><td></td><td>(✓)</td><td>(✓)</td></tr><tr><td>Digital Watermarking [65]</td><td>(✓) (✓)</td><td>✓</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="5">Storage</td><td rowspan="3">Data at-rest encryption</td><td>Conventional encryption</td><td></td><td></td><td></td><td></td><td></td><td>✓</td><td></td></tr><tr><td>Attribute-based encryption [10]</td><td></td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td></td></tr><tr><td>Trusted execution environments [6]</td><td></td><td>✓</td><td></td><td>(✓)</td><td></td><td>✓</td><td></td></tr><tr><td>Immutability</td><td>Blockchain-based solutions [84]</td><td></td><td>✓</td><td></td><td></td><td></td><td>✓</td><td>✓</td></tr><tr><td>Encryption</td><td>Symmetric and Asymmetric</td><td></td><td></td><td></td><td></td><td></td><td>✓</td><td></td></tr><tr><td rowspan="7">Communication</td><td rowspan="5">Anonymity</td><td>Mixnet Protocol [70]</td><td></td><td></td><td>✓</td><td></td><td></td><td></td><td></td></tr><tr><td>Tor-related Protocols [59]</td><td></td><td></td><td>✓</td><td></td><td></td><td></td><td></td></tr><tr><td>Random Walk/DHT Protocols [73]</td><td></td><td></td><td>✓</td><td></td><td></td><td></td><td></td></tr><tr><td>DCnet Protocols [73]</td><td></td><td></td><td>✓</td><td></td><td></td><td></td><td></td></tr><tr><td>Digital Signature</td><td></td><td></td><td></td><td></td><td></td><td>✓</td><td>✓</td></tr><tr><td rowspan="3">Authentication</td><td>Certificateless signature [45]</td><td></td><td></td><td></td><td></td><td></td><td>✓</td><td>✓</td></tr><tr><td>Quantum-safe Signatures [77]</td><td></td><td></td><td></td><td></td><td></td><td>✓</td><td>✓</td></tr><tr><td>Homomorphic Signature [82]</td><td></td><td></td><td></td><td></td><td></td><td>✓</td><td>✓</td></tr><tr><td rowspan="3">Physical</td><td rowspan="2">Encryption</td><td>Transciphering [58]</td><td></td><td>✓</td><td></td><td></td><td></td><td>✓</td><td></td></tr><tr><td>Attribute-based encryption [11]</td><td></td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td></td></tr><tr><td>Anonymization</td><td>Local Differential Privacy [29]</td><td></td><td></td><td>✓</td><td></td><td></td><td></td><td></td></tr><tr><td></td><td></td><td>Peudonymization [26]</td><td></td><td></td><td>✓</td><td></td><td></td><td></td></tr></tbody></table>

LFT: Lawfulness, fairness and transparency; PL: Purpose limitation;

DM: Data minimisation; ACU: Accuracy; SL: Storage limitation;

IC: Integrity and Confidentiality; ACT: Accountability

the storage layer, the GDPR principles respected are purpose limitation, data minimization, and integrity and confidentiality. In addition, anonymization ensures data minimization for the same reason explained in the processing layer.

## 4.3 Assessment of PET Maturity

The maturity assessment of a PET is performed across three key dimensions:

**Utility** This dimension assesses whether the application of the PET preserves sufficient data usability for the intended operations. It evaluates if the desired data processing or analysis remains feasible after the PET is enforced.

**Practicality and Scalability** This dimension focuses on the practical implementation of the PET, particularly its ability to handle large volumes of data efficiently. It considers computational complexity, resource requirements, and scalability in real-world scenarios. The classification in this dimension is based on experimental results provided in the most recent published approaches. Nevertheless, some classifications may be missing due to the absence of recent experimental evaluations for certain proposed PETs.
**Robustness of Privacy Guarantees**
This dimension evaluates the strength of the privacy protection provided by the PET, categorized into three levels: i) *Low Robustness*: PETs that provide weak privacy guarantees and are vulnerable to well-known attacks (e.g., k-anonymity, which is susceptible to side-knowledge attacks). ii) *Medium Robustness*: PETs that offer formal guarantees against specific privacy risks but do not ensure full confidentiality of data. For example, differential privacy provides formal membership privacy guarantees but does not protect the confidentiality of individual data items. iii) *High Robustness*: PETs that ensure full confidentiality of data items. These include encryption schemes with strong security properties, such as IND-CPA (indistinguishability under chosen plaintext attack) [31].

These dimensions collectively provide a comprehensive framework for assessing a PET's maturity, helping to determine its suitability for specific privacy requirements and operational contexts. The qualitative evaluation of PET maturity across the different layers – Processing, Storage, Communication, and Physical – is summarized in Tables 3 and 4. For each evaluation, we provide references to the specific paper(s) that informed our assessment, ensuring transparency and traceability. The notation N/A is used to indicate that the evaluated dimension is not applicable or relevant for a particular PET, while ? is used to signify cases where the existing literature does not provide sufficient information to conduct an objective evaluation. This approach ensures clarity in presenting the evaluations while highlighting areas where further research is needed.

**5 Evaluation**

When we applied our framework to the different use cases, we had the following findings: For the *Platooning* use case [16], the main concern is about secure and outsourced computation as the crucial computations are about the locations and tracks of the platoon leaders and the vehicle trying to join a suitable platoon. Therefore, we considered homomorphic encryption as the candidate. We eventually went with partial homomorphic encryption and found that we could apply CKKS scheme (cf. Chah et al. [16]).

For the *Location and Navigation* use case, we also identified secure and out-sourced computation as a significant concern. In this case, two possible approaches were identified: semantic anonymization and the use of a trusted execution environment (TEE). While a trusted execution environment is quite useful for single-user scenarios, it is not scalable for broader applications. Therefore, we focused on semantic anonymization to define a framework for quantifying the privacy loss when interacting with location-based services [13].

For the *Pay as you Drive (PAYD)* use case, the emphasis is again on secure and outsourced computation. The insurance company needs to measure various metrics related to usage and driving behavior, but ideally, not more than necessary. Consequently, our findings indicate that fully homomorphic encryption schemes would be beneficial in this context. The code is available on GitHub^6. For most

<footnote>^6 GitHub repository removed for anonymization</footnote>
<table><caption>**Table 3.** Maturity of PETs for Processing Layer</caption><thead><tr><th>Layer</th><th>PET & Classification</th><th>Efficiency</th><th>Utility</th><th>Practicality</th><th>Robustness</th></tr></thead><tbody><tr><td rowspan="10">Processing [30]</td><td rowspan="3">Trusted execution environments</td><td>SGX</td><td>● [24, 51]</td><td>● [24, 51]</td><td>● [46, 87]①</td></tr><tr><td>Keystone</td><td>● [24, 51]</td><td>● [53]</td><td>● [53]①</td></tr><tr><td>TrustZone</td><td>● [24, 51]</td><td>●</td><td>?</td></tr><tr><td rowspan="2">Private Information Retrieval (PIR)</td><td>PIR</td><td>Computational PIR</td><td>● [43, 60]</td><td>● [43, 60]</td><td>● [43, 60]</td></tr><tr><td>Oblivious RAM</td><td>Information-theoretic PIR</td><td>● [34, 75]</td><td>● [34]</td><td>● [8]</td></tr><tr><td rowspan="10">Secure and outsourced computation</td><td rowspan="4">Encryption Based Computation</td><td>Paillier</td><td>● ②</td><td>●</td><td>●</td></tr><tr><td>CKKS</td><td>● [47, 86]③</td><td>● [47, 86]</td><td>● [47, 86]</td></tr><tr><td>BFV</td><td>● [47, 86]③</td><td>● [47, 86]</td><td>●</td></tr><tr><td>Hash-ElGamal</td><td>● [47, 86]②</td><td>● [47, 86]</td><td>●</td></tr><tr><td rowspan="4">Fully Homomorphic Encryption [1]</td><td>Boneh-Goh-Nissim</td><td>● [47, 86]③</td><td>● [47, 86]</td><td>●</td></tr><tr><td>Ideal-lattice based</td><td>● [47, 86]</td><td>● [47, 86]</td><td>●</td></tr><tr><td>Over Integers</td><td>● [47, 86]</td><td>● [47, 86]</td><td>●</td></tr><tr><td>R-LWE based</td><td>● [47, 86]</td><td>● [47, 86]</td><td>●</td></tr><tr><td rowspan="3">Secure Multiparty Computation</td><td>Functional Encryption</td><td>Multi-Client</td><td>● [57]</td><td>● [57]</td><td>●</td></tr><tr><td>Secret Sharing</td><td>Single-Client</td><td>● [57]</td><td>● [57]</td><td>●</td></tr><tr><td>Garbled Circuits</td><td>Shamir (ECIES)</td><td>● [4]</td><td>● [4]</td><td>●</td></tr><tr><td rowspan="10">Anonymization</td><td rowspan="2">Zero-knowledge proofs</td><td>Non-interactive</td><td>N/A</td><td>● [62]</td><td>●</td></tr><tr><td>Interactive</td><td>N/A</td><td>● [62]</td><td>●</td></tr><tr><td rowspan="2">Semantic</td><td>Generic</td><td>Global Differential Privacy</td><td>● [12]</td><td>● [12]</td><td>● ④</td></tr><tr><td>Location Based</td><td>Geo-Indistinguishability</td><td>● [5, 63]</td><td>● [5, 63]</td><td>● ④</td></tr><tr><td rowspan="5">Syntactic</td><td rowspan="2">Generalization-based</td><td>δ-privacy</td><td>● [85]</td><td>● [85]</td><td>● ⑤</td></tr><tr><td>k-anonymity</td><td>● [61]</td><td>● [71]</td><td>● [80]</td></tr><tr><td rowspan="2">Suppression-Based</td><td>l-Diversity</td><td>● [61]</td><td>● [61]</td><td>● [61]</td></tr><tr><td>t-Closeness</td><td>● [33]</td><td>● [33]</td><td>● [33]</td></tr><tr><td>Categorical Numerical Perturbation</td><td>Perturbation B-likeness</td><td>● [22]</td><td>● [22]</td><td>● [22]</td></tr><tr><td rowspan="2">Traceab.</td><td rowspan="2">Data tainting</td><td>Noise Addition</td><td>● [21]</td><td>● [21]</td><td>● [21]</td></tr><tr><td>Geometric perturbation</td><td>● [21]</td><td>● [21]</td><td>● [21]</td></tr><tr><td rowspan="2">Digital Watermarking</td><td>● [65]</td><td>● [65]</td><td>● [65]</td><td></td></tr></tbody></table>

① Several implementation attacks are being published each year. The considered adversary model is not always relevant (depending on the use case).

② Pailler cryptosystem provides low utility over the data to be encrypted as it allows only to perform addition on encrypted data.

③ CKK and BFV allow to perform computations that can be expressed as additions and a relatively small number of multiplications. In addition, most implementation of the BFV and CKK do not allow bootstrapping.

④ In summary, while differential privacy-based techniques are effective in protecting the presence or absence of specific entities in a database, the responsibility for safeguarding the confidentiality of the data itself lies in implementing comprehensive security measures that encompass encryption, access controls, and secure data handling practices.

⑤ Delta privacy builds upon the principles of geo-indistinguishability and takes into account the temporal correlation of location data. By introducing additional noise and perturbations, it enhances privacy protection by mitigating the risks associated with the analysis of consecutive data points and the potential inference of sensitive information.
<table><caption>**Table 4.** Maturity of PETs for Storage, Communication and Physical Layer</caption><thead><tr><th>Layer</th><th>PET & Classification</th><th>Efficiency</th><th>Utility</th><th>Practicality</th><th>Robustness</th></tr></thead><tbody><tr><td rowspan="3">Storage</td><td>Data at-rest encryption</td><td>Conventional encryption<br>Attribute-based encryption</td><td>● [10]</td><td>● [10]</td><td>● [10]</td></tr><tr><td>Immutability</td><td>Blockchain-based solutions</td><td>● [10]</td><td>● [10]</td><td>● [10]</td></tr><tr><td>Encryption</td><td>Symmetric and Asymmetric</td><td>● [70]</td><td>● [70]</td><td>● [70]</td></tr><tr><td rowspan="6">Communication</td><td rowspan="5">Anonymity [73]</td><td>Mixnet Protocol</td><td>● [70]</td><td>● [70]</td><td>● [70]</td></tr><tr><td>Tor-related Protocols</td><td>● [79]</td><td>● [79]</td><td>● [79]</td></tr><tr><td>Random Walk/DHT Protocols</td><td>● [73]</td><td>● [73]</td><td>● [73]</td></tr><tr><td>DCnet Protocols</td><td>● [73]</td><td>● [73]</td><td>● [73]</td></tr><tr><td>Digital Signature</td><td>● [73]</td><td>● [73]</td><td>● [73]</td></tr><tr><td rowspan="3">Authentication</td><td>Certificateless signature [45]</td><td>● [45]</td><td>● [45]</td><td>● [45]</td></tr><tr><td>Quantum-safe Signatures [77]</td><td>● [3]</td><td>● [3]</td><td>● [3]</td></tr><tr><td>Homomorphic Signature [82]</td><td>● [25]</td><td>● [25]</td><td>● [25]</td></tr><tr><td rowspan="4">Physical</td><td>Encryption</td><td>Transciphering</td><td>● [58]</td><td>● [58]</td><td>● [58]</td></tr><tr><td rowspan="3">Anonymization</td><td>Attribute-based encryption</td><td>● [11]</td><td>● [11]</td><td>● [11]</td></tr><tr><td>Local Differential Privacy</td><td>● [29]</td><td>● [29]</td><td>● [29]</td></tr><tr><td>Peudonymization [26]</td><td>● [29]</td><td>● [29]</td><td>● [29]</td></tr></tbody></table>

use cases, performance is not the main issue as there is no need to compute in real time. Furthermore, secret sharing as part of secure multiparty computation comes in handy as it could allow the comparison of different values without revealing more information than necessary.

For all use cases, communication anonymity could also be considered, such as using i.e. Mixnet protocols to conceal users' IP addresses. However, this consideration applies to nearly all scenarios involving online communication. Therefore, the primary concern is whether the associated delay is acceptable, rather than its specificity to our use cases. The structured process provided by our framework, while not directly addressing the selection of PETs in its entirety, proved highly effective in guiding the identification and selection of candidate PETs for the three use cases examined.

# 6 Discussion

In this section, we first highlight some findings during the creation of the framework, before we discuss limitations and future work.

Coverage of GDPR principles with PETs As visible in Tab. 2, the accuracy cannot be ensured by using PETs. While data governance and management solutions may use dedicated meta data to classify the required freshness of the data, other requirements such as the rectification of false data is more related to dedicated processes than privacy by technology. One reason for this may be that accuracy is considered a soft privacy requirement [20], where it is assumed

<footnote>① Traditional encryption methods are not well-suited for scenarios where data needs to be shared among multiple entities.</footnote>
that the data subject no longer has control over their personal data, and the
remaining measures rely on policies, access control, and audits. However, most
Privacy-Enhancing Technologies (PETs) aim to minimize data, addressing hard
privacy requirements, and therefore, they do not encompass accuracy.

*Differences between the Layers* Not all GDPR principles are relevant for all layers as the communication layer does not request or store any information; a similar case holds for the physical layer. For the other layers, we see that multiple PETs need to be combined in order to cover most GDPR principles as none can cover all (except accuracy as discussed before).

*Anonymity versus data quality* We also came across the well-known issue that the goal of PETs can be in contrast to data quality [83], in particular if we consider PETs such as perturbation which introduce noise to the data. These PETs trade privacy for data quality. On a higher level, this might even be a conflict with the accuracy privacy principle as data with perturbation may not be considered "accurate". The issue can also be observed in a more lightweight manner with data minimization as many machine learning approaches gain a better accuracy if they can be trained with larger amounts of data.

*From selection to implementation* Additionally, it needs to be mentioned that the correct implementation and usage are crucial. This is a part that is not covered in detail within this work but reflected in the practicability. For example, many semantic anonymizations have the major disadvantage that they are very vulnerable against background knowledge attacks. On the other hand, anonymization except for ZKPs is rather easy to implement. Overall, we observed that Secure MPC and Fully Homomorphic Encryption pose a rather huge computational overhead which reduces the number of potential use cases. Although PHE is orders of magnitude faster than FHE [74], its main issue is its very limited application area due to being reduced to only a single mathematical gate (e.g., secure voting using additive homomorphism). Because of this, FHE, even though libraries such as Microsoft SEAL make continuous progress [72], is hardly used in real-world applications. Similarly, Private Information Retrieval (PIR) in the single-server setting was rather inefficient for a long time. By leveraging a pre-processing phase, SimplePIR could reduce the computational overhead significantly (shifting it to an offline phase still having $O(N)$ with a database size of $N$) so that the actual computation of the query's answer was at very low cost. But still the communication cost of $O(\sqrt{N})$ is rather high [43]. For the multi-server setting, multiple non-colluding servers are required which is impractical in many real-world applications. On the other hand, there are quite efficient schemes available achieving $N^{o(1)}$ after a near-linear pre-processing phase with a sub-linear communication complexity [52]. Similarly, Günther et al. have demonstrated the potential impact of using GPUs for the pre-processing phase achieving $2.1 \times$ speedup compared to the CPU version [34].
## 6.1 Limitations

While our framework provides valuable insights into the evaluation of PETs, there are several limitations that must be acknowledged.

*General limitations* First, our approach uses the privacy principles of the GDPR as a starting point, which may not encompass all relevant privacy regulations. Second, while we provide a general evaluation of PET efficiency, this cannot replace a dedicated assessment of whether the PET meets specific project requirements, such as response time, storage space, or communication overhead. Such detailed evaluations would need to be tailored to the unique constraints of each application scenario. Furthermore, one needs to evaluate a PET based on their planned trust model, for example ABE requires a semi-trusted third party which does not suit every use case.

*Applicability to other domains* Although the evaluation was conducted on three scenarios from the automotive domain, we did not rely on properties unique to this domain. Therefore, we are confident that our framework can be adapted for use in other domains; however, additional validation in diverse application areas would further substantiate this claim.

*Combination of PETs* Currently, the framework does not support the combination of PETs. It neither accommodates the scenario where different privacy principles necessitate different PETs, potentially increasing the complexity of implementation and configuration — thus making alternative PET choices more attractive — nor does it address the scenario where a single privacy principle may be best implemented by applying multiple PETs.

## 6.2 Future Work

*Cover remaining steps from framework* Our framework did not cover certain preparation steps, such as describing the use case, involved entities, and determining the underlying trust model. Furthermore, the step of aligning the processing needs of the use case with capability of the PETs is only partially supported. While our maturity assessment of the PETs provides some input, the selection process itself is not supported. Future work could address these remaining steps of the framework or at least provide basic support, as they may vary significantly depending on the use case.

*Further dimensions for maturity of PETs* The evaluation of PETs also in terms of performance overheads in realistic settings could be expanded. In particular, the availability of easily (re-)usable artifacts (e. g. library, package, component) with the aim to include the dimension of developer-friendliness and necessary implementation efforts in the framework. Those have already been identified by Pallas et al. [64] as important for PET adoption.
*Coverage of further privacy principles* Our work is limited to the privacy principles listed in the GDPR in Article 5.1. Not only could the work be expanded to other privacy principles within the GDPR, but also to other privacy-related regulations, such as California Privacy Rights Act of 2020 (CPRA), California Consumer Privacy Act (CCPA) [14], the Brazilian General Data Protection Law (LGPD), or the Act on the Protection of Personal Information (APPI) in Japan. Furthermore, there is no need to restrict the idea to regulatory frameworks. Thus, privacy principles from non-regulatory frameworks, such as the Fair Information Practice Principles (FIPPs) or the OECD Privacy Principles, clearly call for further principles to be properly reflected in the design and implementation of real-world systems as well.

*Considering user acceptance criteria for PET selection* Furthermore, it would be interesting to consider any user acceptance effects and criteria for PETs [66] in our framework, i. e. for machine learning [54, 55]. User perceptions of PETs considering technology use behavior [42], willingness to pay [37], privacy concerns [39], risk beliefs [40], privacy literacy [41], and perceived anonymity and trust [38] have been investigated in the past by Harborth et al. and shown to have a huge influence on the acceptance of PETs by end users. Therefore, it would be another challenge to include these into the selection process, perhaps along with other economic criteria such as licenses of the PETs, fees and operating costs, and availability of knowledgeable experts.

# 7 Conclusion and Future Work

We proposed a framework to support the selection of PETs based on GDPR privacy principles. For that purpose, we provided mappings between a trust model and the GDPR principles and between different PET types and GDPR principles. Furthermore, we assessed different PETs with regards to utility, scalability, and robustness. We evaluated our framework by applying it to three different use cases from the automotive domain and it proved highly effective in guiding the identification and selection of candidate PETs.

While our work helps on the selection of PETs, there is still a gap when it comes to the implementation, configuration and combination of PETs. Furthermore, the challenge of integrating PETs into (existing) architectures of a system may not be trivial as well.

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