The following sections provide insight into some core research contained in
the book Designing for Privacy and
Its Legal Framework, published within the Law and Governance Series of
Springer (DOI: 10.1007/978-3-319-98624-1).
I. The Evolution towards Privacy by Design
To understand the concept of privacy by design, one should first take a
step back and describe the normative and technical tools used to design for
privacy. On one hand, privacy and data protection legislation, industry
standards on data security, and guiding ethical norms on how to process
data build the «normative rationality» of how legal scholars
approach the topic of privacy and data protection. We call such approaches
legal tools to design for privacy. While these legal tools can vary
depending on who issues them (e.g., policymakers, industry agreements,
etc.) and how they are enforced, they all build upon a moral imperative.
Figuratively speaking, these tools stipulate rules such as «you are
not allowed to enter my house». On the other hand, engineers and
developers approach the topic of designing for privacy by employing
technical tools aimed at protecting the security of communications, the
autonomy of transactions, the anonymity of connections, and enhancing the transparency of
data processing operations. This more hands-on approach to privacy and data
protection is akin to «locking the door of the house ».
While numerous technical tools that incorporate technical mechanisms
(typically referred to as privacy-enhancing technologies) exist, their
focus often remains on single goals, addressing specific data protection
needs (e.g., the anonymity of email communication). Thus, the argument for
a more holistic approach to privacy and data protection emerged and took
shape in the concept of privacy by design. Ann Cavoukian pioneered the
vision of designing for privacy in 1990 when she argued for a systematic
approach to creating technology that embeds privacy into its underlying
Her vision was designed to overcome the juxtaposition of the legal and
technical rationality: Legal principles being reactive to past harms (law
as a passive observer), while technical tools are proactive measures,
enacted to prevent infringements (technology as an active preventer).
Privacy by design is an attempt to embrace a holistic approach to privacy
protection, from both a preventative and sanctioning standpoint. In this
sense, privacy by design is neither strictly a legal nor a technical tool.
It combines a principle-based rationality with the aim of finding technical
mechanisms and organizational procedures to protect informational privacy
preemptively. As privacy by design - or data protection by design and
default as Article 25 of the General Data Protection Regulation (GDPR)
calls it - has now been encoded into the European data protection
framework, it has gained even more significance, with data controllers
subject to the
being compelled to implement this new principle.
Implementing privacy by design in practice, though, is not a
straightforward process and requires legal (i.e., what the normative
principle calls for) and technical (i.e., how to design systems in a way
that are compliant with the law) knowhow. Realizing that there is often a
gap between the theoretical conception of data protection by design and its
practical implementation, «Designing for Privacy and its Legal
Framework» unveils the scope of legal principles and technical tools
used for privacy and data protection in order to address the question of
how to implement Article 25 of the GDPR. We present
an analysis of how current regulations in the European Union delegate the
implementation of technical privacy and data protection measures to data
controllers and show how policymaking must evolve to implement
privacy by design in its fullest ideal.
II. The Codification of Data Protection by Design and Default within
Before the implementation of the GDPR, the Directive 95/46/EC
obliged data controllers to «implement appropriate technical and
organizational measures to protect personal data» (Article 17). Today, Article 25 of the GDPR goes beyond
this statement and introduces a three-paragraph-long article on the
subject. Unlike the Directive 95/46/EC, this new
article specifies the principle of technical data protection and defines it
with respect to time, its scope, and the subject matter.
Firstly, Article 25 mandates that
technical tools for the protection of personal data are applied beyond the
initial design phase, throughout the life cycle of data. Secondly, the
scope of data protection by design is broadened to focus not only on data
security but also on all prerequisites established in the GDPR. Thirdly, rather than leaving the implementation of privacy by design to
the discretion of data controllers, the
stipulates that data subjects have a right to request such technical data
protection measures. Finally, the concept of data protection by default is
stressed within Article 25,
illustrating the evolution towards a more preventive and proactive regime
of data protection. A similar evolution is seen within Article 10 of the modernized
Convention for the Protection of Individuals with Regard to the Processing
of Personal Data(Convention 108+), which now
includes an obligation to enact privacy by design norms and stipulates the
need to foster privacy-friendly configurations by default.
In and of itself, Article 25 of the GDPR is a
as its notion of privacy by design relies on the other legal principles
that specify the data processing requirements. The article literally refers
to meeting the «requirements of this Regulation» through
technical and organizational measures. Thus, to translate Article 25 into practice, we need
to take three steps:
Given this basis and because privacy by design is inherently dependent upon
context, we then look at a concrete scenario and discuss how in a specific
case privacy by design can be implemented.
1. Meeting the Legal Requirements of the GDPR
Article 25 of the GDPR
states that data controllers must «implement appropriate technical and
organizational measures (...) in order to meet the requirements of this
Regulation and protect the rights of data subjects». Thereby, the data
protection by design clause refers to all principles within the
and requires technical or organizational approaches to encode them.
In light of this provision, we classify the aforementioned legal principles
into four main groups, those concerning the legality of data processing,
the design of data processing systems, the rights of data subjects, and the
compliance and enforcement of the GDPR. While other classifications of legal principles are possible, we chose
this one as we found it to be the most accessible to non-legal scholars such as
engineers and developers.
Principles concerning the legality of processing include that of lawful,
fair, and transparent processing and informed consent or other means for
lawful processing (in particular legitimate interests for processing
personal data). Such principles are core components of the
as they build the foundation for legitimizing data processing.
However, these principles do not stand alone; instead they are merely the
first hurdle data controllers must overcome in order to process personal
data of EU citizens.
One could argue that adhering to the principles concerning the design of
the data processing system presents an even greater challenge for
data controllers. These principles contain specific design requirements
such as the mandate that data controllers build data processing systems
that minimize the collection of data; to only process said data to the
minimum extent necessary to achieve the goal for which it was gathered; to
build secure data processing systems with explicit use, disclosure, and
storage limitations; and to apply anonymization or pseudonymization
techniques. These requirements define the boundaries within which
developers and engineers are free to innovate and create new data
processing services and products.
Aside from the aforementioned principles, which focus more on the duties of
data controllers, these concerning the rights of individuals provide data
subjects with specific information, access, objection, and erasure rights.
It remains the duty of data controllers to ensure that data subjects can
make use of those rights. However, these rights are - in contrast to the
ones mentioned above - «pull rights», meaning that data subjects
have to actively request access to their personal data or object to data
processing and demand erasure of said data.
Finally, the principles concerning compliance and enforcement ensure that
all the principles of the
are implemented. The
not only sanctions the lack of conformity with the core principles of the
but also enables government authorities to supervise and monitor the
activities of data controllers. Additionally, increased accountability and
liability for outsourced data processing encourages data controllers to
consider the consequences of failure to adhere to data protection rules.
2. Taxonomy of Technical and Organizational Tools
In computer science literature, the quest to preserve privacy has often
been linked to the confidentiality of data.
Even if the linkage of security and privacy has driven research in computer
science, security tools are not the only available technical data
protection measures that are proposed in the literature. In «Designing
for Privacy and its Legal Framework» we, therefore, take a broader
approach and classify the technical and organizational measures that data
controllers can realistically implement to encode the legal principles
described above. Such measures are divided into four categories, namely,
security, anonymity, autonomy, and transparency tools.
Security traditionally contains three main sub-elements, namely, the
preservation of confidentiality, integrity, and availability.
Confidentiality requires that the data is not disclosed to unauthorized
parties while in storage, in transit, or during processing. Integrity means
that information remains accurate, complete, unmodified, and consistent; in
other words, data cannot be altered without authorization. Finally,
availability stipulates that information is accessible and usable by
authorized parties. Other sub-elements, such as the preservation of
authenticity, authorization, accountability, non-repudiation, and
reliability, further add to a complete picture of a palette of security
Technical mechanisms such as cryptographic tools, overall secure
communication architectures that protect the confidentiality of data in
transit or storage, digital signatures that ensure the integrity of a
message and authenticate users, and digital certificates infrastructures
ensuring that cryptographic keys can be exchanged, all working towards
implementing the goal of security.
Another core goal of technical tools is anonymity. While ISO guidelines
seem to approach the topic of anonymity in a binary fashion (one is either
anonymous or identifiable),
it is also possible to quantify anonymity as the inability to
«sufficiently identify the subject within a set of subjects, the
Likewise, pseudonymity is contained within the latter definition of
anonymity; a pseudonym allows the creation of a separate identity that can
- depending whether pseudonymous identities can be linked to the actual
identity - be anonymous. In contrast to anonymity, pseudonymity tools
enable the establishment of reputation.
Elements inherent to anonymity tools are unlinkability, unobservability,
and deniability. To implement these features, several anonymity tools
exist. Included in this set are mechanisms to render data in datasets
anonymous (generalization, randomization), the creation of multiple
identities and digital identity management service providers, as well as
techniques to obfuscate the data individuals leave online (e.g., by using
proxy servers which hide IP addresses of senders and receivers).
Technical measures that fulfill the ideal of privacy in computer science
are autonomy tools.
These tools allow an individual to exercise control over data processing
operations. From a technical perspective, we define autonomy to encompass
three significant points of control. The first covers mechanisms that
regulate who has access to the data. However, unlike confidentiality tools,
the focus of these autonomy tools is not only to block unauthorized access
to data but also to prevent third parties which have authorized access to
use the data for purposes to which the individual did not consent to (e.g.,
by employing data tags or using data stores). Furthermore, disposal control
mechanisms such as personal data stores or personal information management
systems exist, that provide the individual with control over when and with
whom he or she shares personal data. Lastly, deletion control mechanisms
ensure that data is not only unlinked from a database, but also entirely
erased from the user-level layer to the physical layer.
Lastly, we look at transparency tools which aim to provide users with
information on the collection, analysis, use, and erasure of data. Doing so
helps to redress information asymmetries between data controllers and data
subjects. We rely on a broad definition of transparency tools which must
provide the data subject, or a proxy acting on his or her behalf, with at
least one of the following options: (1) information about the intended
collection, analysis, implementation, or storage of data, (2) information
on how to access the data and on the logic of the processing operations, or
(3) information on how the personal data is matched to group profiles.
Transparency tools, in particular, are often not only technical tools but
also include design features (e.g., visualizations such as privacy icons)
and organizational procedures (e.g., privacy impact assessments and notice
3. GDPR on how to Implement Tools for Data Protection
While past research has primarily been mono-disciplinary, with legal
principles on one side and technical tools on the other, the book
«Designing for Privacy and its Legal Framework» considers how the
law refers to the technical tools. By doing so, we provide further guidance
to developers and engineers on how to implement technical and
To be substantiated, guidance for implementing technical tools should be
based on existing legal rules and regulations. Current regulation will,
therefore, be the foundation of the evaluation of the application of
privacy by design. Consequently, we describe how regulations invoke
technical objectives, namely, security, anonymity, autonomy, and
transparency. When data controllers seek guidance on how to implement
privacy by design, they will rely on existing rules which specify the need
to build secure, anonymous, and transparent systems that provide
individuals with control over their data.
a) Data Protection through Security
provides guidance to developers on which security tools to implement by
mentioning specific security measures in Article 32 such as «(a) the
pseudonymization and encryption of personal data; (b) the ability to ensure
the ongoing confidentiality, integrity, availability and resilience of
systems and services processing personal data; (c) the ability to restore
the availability and access to data in a timely manner in the event of a
physical or technical incident; (d) a process for regularly testing,
assessing and evaluating the effectiveness of technical and organizational
measures for ensuring the security of the processing». Whether such
security measures are appropriate depends on the risks presented by each
individual case. The
specifies certain risks, such as accidental or unlawful destruction, loss,
alteration, unauthorized disclosure of, or access to, personal data.
relies on common terms from computer science, such as confidentiality,
integrity, and availability, with which developers are more familiar.
Similarly, industry standards specify which security tools to implement and
how to do so, thereby providing further guidance to developers and
engineers designing secure systems.
b) Data Protection through Anonymity
In contrast to security tools, the
provides little guidance on how to implement anonymity tools. It does not
elaborate upon the means to render personal data anonymous, but rather only
states that «objective factors, such as the cost and the amount of
time required for identification» as well as «the available
technology at the time of the processing and technological
developments» should be taken into account when assessing whether
anonymization measures yield irreversible unlinkability.
has focused more on the concept of pseudonymous data, which it mentions in
various recitals and articles. It defines pseudonymization as «the
processing of personal data in such a way that the data can no longer be
attributed to a specific data subject without the use of additional
information», and explicitly states that technical and organizational
measures shall ensure that this «additional information» is
stored separately from the identifiable information.
This language is the most specific example of the
describing guidelines intended to ensure the objectives of a user's of
anonymity, unlinkability, and pseudonymity. It does not elaborate on more
specific (technical or organizational) pseudonymization measures that data
controllers should take into consideration.
c) Data Protection through
Within the GDPR, autonomy tools, in particular access and permission control tools, are
primarily implemented through consent. The GDPR provides guidance on how to
design consent forms, such as requiring that they be presented in a clear
format and be separated from other text or information.
Furthermore, there must be an option to withdraw one's consent at any time.
mentions the term «control» several times (unlike the Directive 95/46/EC), putting more
emphasis on employing autonomy tools.
Recital 68 of the GDPR
(which by itself is not binding, but provides guidance on how to legally
understand control or autonomy) for example states that in order to
strengthen the control over personal data, data subjects must be able to
receive said data «in a structured, commonly-used, machine-readable
and interoperable format». Two points are key: First, the
acknowledges the possibility of employing machine-readable formats as a
tool to foster autonomy. In this sense, it refers to an existing concept in
computer science which can, in theory, be adopted into the design of
services and products. Second, Recital 68 introduces the concept
of interoperability and the ability of a user to more easily change service
providers (data portability). This concept brings technical features into
the foreground and has far-reaching implications for developers and
d) Data Protection through
As with autonomy tools, the
does not provide extensive guidance on which transparency tools to
implement; however, it does acknowledge the importance of visualization
efforts in order to maintain a sense of transparency.
In particular, it elaborates on data protection impact assessments and the
use of privacy certificates in order to facilitate recognition of
privacy-friendly products and services.
The goal of data protection impact assessments is to ensure compliance with
all legal requirements, and thus it can be considered a preliminary step of
any privacy by design process.
Privacy impact assessments take a process-oriented approach to privacy
protection. While data protection impact assessments generate transparency
within the company collecting and processing data, their publication is not
required by the GDPR, thus the results of such assessments remain behind closed doors. Further
transparency could be provided to data subjects by publishing a summary of
each assessment or the steps taken to minimize identified privacy threats.
Additionally, compliance with the principle of data protection by design
and default may be demonstrated by an «approved certification
mechanism pursuant to Article 42» of the GDPR.
Privacy certificates or seals attest to compliance with specific privacy
requirements, thus providing additional legal security to data controllers. Recital 100 of the GDPR states
that these mechanisms should be further fostered, as they provide clear
knowledge regarding a companies' data protection policies (i.e., applied
transparency), and signal to consumers that the company has implemented all
necessary privacy regulations.
e) Balancing of Interests
The implementation of technical and organizational measures is never an
absolute obligation. The need for privacy protection in general, and
particularly through technical tools, requires a balancing of interests.
When determining the appropriate level technical and organizational tools
to be implemented, Article 25(1) of the GDPR mandates
that the data controllers take into account the state-of-the-art of
technical measures, material cost of implementation, nature, scope,
context, and purposes of the data processing techniques, and the likelihood
and severity of the risks to the rights and freedoms of individuals.
While the state-of-the-art requirement is more objective and
straightforward than the others, requiring developers and engineers to
apply technical measures that are status quo in a given industry or
context, and requiring to take cost and effectiveness into consideration,
necessitate that a balance be struck between privacy-friendly design and
economic feasibility for a given data controller. The costs include all
incurred expenses from the planning and implementation of specific
Likely included in these expenses are the costs of development of
customized technical and organizational measures, secure hardware, and
implementation of a secure password administration system. Other indirect
costs, however, such as the revenue loss due to the implementation of such
technical measures, are not covered by Article 25(1) of the GDPR. In fact, economic obstacles to the implementation of technical tools
exist, as high-performance hardware equipment becomes necessary when
technical tools become computationally heavy.
Additionally, balancing the costs necessitates taking into consideration
whether a measure is appropriate with respect to its protective purpose. This balance takes the likelihood of a privacy infringement and
the damage of such an infringement into consideration. As a general rule,
the higher the risks of an infringement and the higher its damage, the more
extensive (and costly) the protection measures must be.
This requirement stipulates the undertaking of a risk analysis and
evaluation; such a risk assessment is codified in Article 25 of the GDPR. This risk
assessment aligns with Article 35 of the GDPR, which lays
out the requirements for data protection impact assessments. Unlike the
privacy by design and default provision, the provision on data protection
impact assessments provides data controllers with a non-exclusive list of
scenarios which require an assessment prior to processing. Such scenarios
include operations which systematically and automatically evaluate an
extensive amount of personal data (included here are profiling operations)
and processing operations based on a large scale of sensitive data. This
assessment should be documented and updated whenever changes to the
processing operations are foreseen.
III. Putting Data Protection by Design into Context
In order to illustrate how Article 25 of the GDPR works in
practice, we describe a product developed by a fictional startup: A smart
wearable wristband (the MySleep bracelet), measuring a user's (Alice's)
sleep cycle. We follow the data processing steps and, in each phase of the
life cycle of data, analyze the technical and organizational measures the
startup must implement to comply with Article 25 of the GDPR.
1. Collecting and Transmitting Alice's Data
We start with the collection and transmission of data via MySleep's
website, bracelet, and through third parties. This early phase determines
who collects what data from Alice. It also illustrates how the data is
transmitted between devices. In our case study, Alice purchases a MySleep
bracelet on the MySleep website. In order to do so, she enters her name,
shipping address, and credit card information into a standardized form and
arrives, Alice downloads the smartphone application and registers with her
full name and email address. Once logged into her account, Alice can
provide MySleep with further physiological information (e.g., age, height,
weight) and physical status (e.g., body aches, meals before bedtime,
overall exposure to blue light). Alice wears the bracelet at night, thereby
collecting sensor data such as her pulse, heart rate, body temperature, and
duration of sleep. Every morning, Alice connects her bracelet via Bluetooth
to her smartphone, which immediately transmits the data to a cloud server.
Both the bracelet and her smartphone only store data as long as needed to
complete the transmission to the cloud.
In order to comply with Article 25 of the GDPR, the
MySleep startup has different technical and organizational tools it must
apply during this early phase of the life cycle of data. First of all,
Alice should not be required to provide her full name to register for the
service, since such data is not necessary for MySleep's performance of the
service. The default setting should require only a username, encouraging
users to set pseudonymous identities. Additionally, the application should
encourage Alice to enter approximate physiological data (such as only the month and year instead of her exact birthdate). Additionally,
MySleep must provide Alice with transparent terms regarding the processing
of her personal data. The
elaborates in greater details than its predecessor (the Directive 95/46/EC) on the design
of privacy policies. Transparent terms under the
must include, among other requirements, who processes the data, what data
is being collected and for what purposes, to whom the data is being
disclosed, what safeguards are in place if data is disclosed aboard, and a
list of her participation rights.
In addition to the use of anonymity and transparency tools, security tools
also are essential in this first phase of the life cycle of data. Secure
end-to-end communication channels (e.g., Transport Layer Security [TLS])
must be implemented to ensure that even if the data is intercepted, it
remains encrypted. MySleep logs the transmission of data and requests that
Alice's smartphone authenticates itself (via an authentication token)
before allowing the upload of health data; these measures help to identify
improper transmissions. Likewise, the personal data stored on the server
should be encrypted. From a privacy by design perspective, it is reasonable
to differentiate among encryption schemes depending on the risks associated
with the different types of data (see above, III.3.e Balancing of
Interests). In this case study, two different databases are set up, each
containing different types of data: The identity database requires
a higher level of data security measures, as it stores directly
identifiable data; the health database, in comparison, stores
pseudonymized data. In the case of a breach, only the identity database
poses a high risk to Alice's privacy. The health database, while containing
more sensitive information, presents a lesser risk, as the data is
pseudonymized, and Alice can only be identified in combination with the
identity database. Therefore, MySleep must be reasonably allowed under Article 25 of the GDPR to
differentiate between the measures taken to adhere to the principle of data
2. Analyzing Alice's Data on External Servers
The analysis phase looks at how and where Alice's data is stored and
analyzed, as well as who, besides MySleep, has access to her data, and
thus, the security of the infrastructure used to analyze the data is
paramount in this phase. Typically, a startup will rely on external and
scalable cloud computing services. In our case, MySleep safeguards Alice's
data in two separate databases (the identity and health database), both of
which are stored on Amazon Web Service (AWS) servers. These databases are
not publicly accessible through the Internet, in fact, they may only be
accessed through the MySleep network by a set of permitted MySleep
Before relying on an external service provider for any processing, MySleep
must ensure that these external providers or processors provide sufficient
guarantees to implement appropriate technical and organizational measures.
These guarantees are either provided by a contract between the data
controller and the external service provider (e.g., AWS) or by EU
legislation that binds the service provider. At the minimum the contract or
law must define: (1) the subject-matter and duration of the commissioned
processing, (2) the nature and purpose of the commissioned processing, (3)
the type of personal data being processed, (4) the categories of users
included, and (5) the obligations and rights of MySleep. Therefore, in
order to ensure compliance with the GDPR, MySleep must first evaluate the physical controls that AWS employs and
certifications of the company. In a second step, the security level
necessary for each database must be determined. The authorization level to
access either one or both of the two databases must be strictly defined,
and automatic restriction of access when a system receives an incorrect
identification must be technically implemented. Additionally, access to
logical systems should be automatically blocked after periods of inactivity
in order to prevent potential disclosure of data. These measures not only
require technical implementation, but must also be contained in
organizational rules that are known by employees. Depending on the
database, the encryption of data at rest and in transit may vary. While
health data must be analyzed and visualized for Alice, and since this task
requires complex computation, such data can realistically not be analyzed
in an encrypted format (even if homomorphic encryption enables that). This
balancing of cost is necessitated by Article 25 of the GDPR, which
requires that data controllers consider the various risks associated with
databases as well as the nature, context, and purpose of processing.
3. Providing Alice with the Service
Every morning, Alice uses her wearable to obtain information about her
sleep. To do so, the sensors' data is uploaded to the servers, processed,
and communicated or visualized back to her. The visualization of her data
allows Alice to screen for obvious mistakes (e.g., if the sleep logs are
clearly too long or short). She can access additional information on her
sleep quality as well as explanations of the values of her recordings.
Visualizations enable Alice to grasp the overall processed sensor data.
Results of analyses that combine her sensor data as well as physiological
and physical data are less obvious to Alice (i.e., how a result was yielded
is typically not shown to Alice). In light of Article 25 and Article 22 of the GDPR (the scope
of the latter being much debated), some information expanding upon the
logic involved behind the computations could be helpful.
In order to ensure that different categories of Alice's data are used for
the purpose she agreed to, technical separation controls must be
implemented in order to ensure that data collected for each individual
purpose are processed separately. Separation is already achieved through
the splitting of databases, as well as by imposing handling restrictions
vis-a-vis who within MySleep is able to process various data types. Other
technical tools that exist to ensure compliance with the purpose and
disclosure limitation include privacy obligations and tags. Additionally,
access rights on a «need-to-know» basis ensure that employees at
MySleep are not able to access both databases and, in turn, reidentify
Alice. Lastly, internal guidelines that explain how employees should cope
with data breaches as well as protocols in case of such a breach must be in
place. Compliance training should be used to raise awareness on such
4. Deleting Alice's Data
In the event that Alice decides to delete her account, MySleep must ensure
that her data in the identity database (which is not anonymized or
pseudonymized) is immediately erased from the server. Slowly, her data will
then be erased from the company's backup systems. Likewise, if Alice stops
using her bracelet and the application, MySleep does not need the data any
longer for the purpose of providing Alice with a service and should,
therefore, have a policy in place to automatically delete a users account
after a long inactive period. How long the inactivity must last before
MySleep needs to erase Alice's data is not explicitly defined within the GDPR, and best practices within the industry should be consulted in this
respect. Additionally, MySleep needs to have a system in place, which
notifies third-party providers (e.g., if Alice allowed optional cookies for
improved targeting functions by third parties) that may have access to
Alice's data, that she requested her personal data to be erased. Thus,
deletion of Alice's data requires not only technical but also
organizational measures, such as guidelines directed to employees in order
that they know how to respond to erasure requests and completion of secure
deletion (i.e., deleting the content of the data layer-by-layer). Overall,
the deletion process must be logged, and not all MySleep employes should be
able to actually trigger such a process.
In «Designing for Privacy and Its Legal Framework» we aim to
enhance both developers' and policymakers' understanding of what the
principle of privacy by design entails in practice. Policymakers should
consider how they want legal principles to regulate behavior and be
implemented by data controllers.
The more precise guidance each principle prevails to data controllers and
the more clearly each principle aligns with technical objectives, the
easier it will be for developers and engineers to adhere to. In turn, the
more abstract and unaligned with the technical objectives, the more of a
discrepancy will exist between the law and the implementation of technical
tools. Self-regulatory standards can help to bridge this gap from the
abstract legal sphere to the concrete technical sphere; yet, developers
will require more guidance on how to comply «technically» with
legal principles. Concrete scenarios and case studies help to break the
legal principles down into more concrete implementation schemes.
More clarity regarding which tools are appropriate to use can be provided
by development of privacy engineering guidelines. Such guidelines aim to
define the technical measures that should be implemented to minimize
identified privacy risks. Additionally, the education of engineers and
users must play a vital role in every regulatory strategy to achieve better
privacy and data protection. Likewise, privacy professionals, such as Chief
Privacy Officers, will help to ensure compliance with data protection
legislation within a company. Such measures and positions are also crucial
in light of the rapidly changing technological and social environment that
includes ever more digitalization and dependence on it. New regulatory
solutions will be required in the coming time, but can only be brought
about through regulatory and perspective shifts toward alignment with the
principle of privacy by design and default.
Tamò-Larrieux, Designing for Privacy and its Legal Framework -
Data Protection by Design and Default for the Internet of Things,
Springer 2018, p. 21.
See Cavoukian, Privacy by Design in Law, Policy, and Practice. A
White Paper for Regulators, Decision-makers and Policy-makers,
2011, pp. 3 et seqq.
The General Data Protection Regulation (GDPR) of the European
Parliament and council, Regulation (EU) 2016/679.
Brinhack/Toch/Hadar, Privacy Mindset, Technological Mindset.
Jurimetris: Journal of Law, Science & Technology, 55, 2014,
55-114, pp. 55 et seqq.
Amending protocol to the Convention for the Protection of
Individuals with Regard to the Processing of Personal Data, adopted
by the Committee of Ministers at its 128th Session in Elsinore on
18 May 2018.
Tamò-Larrieux, (Fn. 1), p. 209.
Tamò-Larrieux, (Fn. 1), pp. 87 et seqq.
Bygrave, Data Protection Law-Approaching Its Rationale, Logic and
Limits, Kluwer International 2002, p. 58 et seq.
Tamò-Larrieux, (Fn. 1), pp. 91 et seqq.
Tamò-Larrieux, (Fn. 1), pp. 93 et seqq.
See Gürses, Multilateral Privacy Requirements Analysis in
Online Social Network Services. Dissertation, Department of
Computer Science, Katholieke Universiteit Leuven, 2010, pp. 36 et
Tamò-Larrieux, (Fn. 1), pp. 101 et seqq.
See, e.g., ISO/IEC 27000: 2016 standard on information technology
(overview and vocabulary).
See for further references Tamò-Larrieux, (Fn. 1), pp.
Tamò-Larrieux, (Fn. 1), pp. 109-123.
ISO/IEC 29000: 2011 and ISO/IEC 15408-2: 2008.
Pfitzmann/Hansen, A terminology for talking about privacy by data
minimization: Anonymity, Unlinkability, Undetectability,
Unobservability, Pseudonymity, and Identity Management, Version
v.0.34, 2010, p. 10.
Pfitzmann/Hansen, (Fn. 18), p. 33.
Tamò-Larrieux, (Fn. 1), pp. 123-131.
See Pfleeger/Pfleeger, Security in Computing, 4th edition, 2007, p.
Tamò-Larrieux, (Fn. 1), pp. 131-137.
Tamò-Larrieux, (Fn. 1), pp. 108-109.
Tamò-Larrieux, (Fn. 1), pp. 137-141.
Tamò-Larrieux, (Fn. 1), pp. 167 et seqq.
Art. 4(5) of the GDPR. Cf. also Recital 29 of the GDPR:
«In order to create incentives to apply pseudonymization when
processing personal data, measures of pseudonymization should,
whilst allowing general analysis, be possible within the same
controller when that controller has taken technical and
organizational measures necessary to ensure, for the processing
concerned, that this Regulation is implemented, and that additional
information for attributing the personal data to a specific data
subject is kept separately. The controller processing the
personal data should indicate the authorized persons within the
same controller.» (emphasis added).
Plath/von dem Bussche, in Plath (ed.), Kommentar zum BDSG und zur
DSGVO sowie den Datenschutzbestimmungen des TMG und TKG, 2nd
edition, 2016, Art. 35, marginal No. 1.
Article 29 Working Party, Guidelines on Data Protection Impact
Assessment (DPIA) and determining whether processing is "likely to
result in a high risk" for the purposes of Regulation 2016/679,
WP248 rev. 01, (17/EN), 4 October 2017, p. 18.
See Paal/Pauly/Martini, in Paal & Pauly (eds.),
Datenschutz-Grundverordnung, 2017, Art. 25, marginal No. 41-42.
Paal/Pauly/Martini, Art. 25, marginal No. 41 relying on the wording
of Art. 25(1) of the GDPR which refers only to "the cost of
Paal/Pauly/Martini, Art. 25, marginal No. 37.
For more details on this fictional case see Tamò-Larrieux,
(Fn. 1), pp. 203 et seqq. (Chapter 9 - Privacy by Design for the
Internet of Things: A Startup Scenario).
See for a comprehensive overview of the functions of legislation:
Gasser, Perspectives on the Future of Digital Privacy. Rechtsfragen
im digitalen Zeitalter. Schweizerischer Juristentag 2015, ZSR Band
134 II, 337-448, pp. 368 et seqq.