The Concept of Identifiability in ML Models
Stephanie von Maltzan
Karlsruhe Institute of Technology, Centre for Applied Legal Studies, Vincenz-Prießnitz-Str. 3, 76137 Karlsruhe, Germany
Keywords: Anonymisation, Pseudonymisation, ML Model, Adversarial Attacks, Privacy, Utility.
Abstract: Recent research indicates that the machine learning process can be reversed by adversarial attacks. These
attacks can be used to derive personal information from the training. The supposedly anonymising machine
learning process represents a process of pseudonymisation and is, therefore, subject to technical and
organisational measures. Consequently, the unexamined belief in anonymisation as a guarantor for privacy
cannot be easily upheld. It is, therefore, crucial to measure privacy through the lens of adversarial attacks and
precisely distinguish what is meant by personal data and non-personal data and above all determine whether
ML models represent pseudonyms from the training data.
The debate about Privacy Preserving and in particular
anonymisation techniques has intensified as a result
of an increasing demand for stronger and more
comprehensive protection of personal data. In recent
years many companies have considered
anonymisation to be the answer to all data protection
and privacy issues. Companies exploiting
anonymisation assume that it cannot breach privacy.
This premise poses, nevertheless, some challenges.
Two crucial assumptions should, therefore, be
considered. The first point to note is that personal data
is only considered anonymous, if it is not possible to
identify an individual. The General Data Protection
Regulation (GDPR) assumes that there is some
leeway in considering data to be anonymous.
Determining anonymity is, therefore, fraught with
difficulties and depends on criteria that change
according to technical progress or even specific
analysis, as the GDPR takes into account technical
developments to determine anonymous data. This
leads to constant uncertainty in the anonymisation
process. Government standards are, therefore,
indispensable. Secondly, a large body of research on
the volatility and vulnerability of machine learning
(ML) models points to the problem that training data
used for ML models have higher probability of re-
identification as a result of adversarial attacks
(Fredrikson et al., 2014; Shokri et al., 2017). Based
on the premise that anonymisation is not a risk-free
mechanism, as increasingly acknowledged (Brasher,
2018; Mehmood et al., 2016; Piras et al., 2019;
Pomares-Quimbaya et al., 532019), adversarial
attacks against ML models became the focus of
research. Overall, the review showed that ML models
memorise sensitive information of the data used for
training, indicating serious privacy risks (Carlini et
al.; Fredrikson et al., 2015; Hayes et al., 2019;
Hilprecht et al., 2019; Jayaraman & Evans, 2019;
Pyrgelis et al.; Shokri et al., 2017; Song et al.; Yeom
et al., 2017). This emerge to the problem that ML
models might be personal data and fall, therefore,
under the scope of the GDPR. Anonymised data is
exempt from the GDPR. If the data is not personal
data, as previously assumed for ML models (and the
output), the GDPR does not apply.
Machine learning algorithms are regularly trained
and evaluated on disjoint data sets. Hence, research
and industry have been under the erroneous belief that
it is not possible to retrospectively draw conclusions
from the ML model about the data used for training.
However, some ML techniques - as the above
mentioned research has shown - can remember the
training data of the model antiparallel to the
predefined learning process. Despite its “artificial”
nature the ML process contains some characteristics
of the properties, patterns and correlations from the
data used for training and, thus, does not protect from
linkage and attribute inference. As indicated above,
this raises the question of whether the ML models
represent pseudonyms from the training data and
could, therefore, fall under the definition of personal
data. Classifying models as personal data raises
von Maltzan, S.
The Concept of Identifiability in ML Models.
DOI: 10.5220/0011081600003194
In Proceedings of the 7th International Conference on Internet of Things, Big Data and Security (IoTBDS 2022), pages 215-222
ISBN: 978-989-758-564-7; ISSN: 2184-4976
2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
further far-reaching problems concerning the widely
used ML as a Service (MLaaS). In the event that
malicious users were able to re-identify data used to
train these models, the resulting information leakage
and privacy breach would cause serious issues.
Therefore, the unexamined belief in anonymisation as
a guarantor for privacy cannot be easily upheld.
As a result, the surprising ease of identifying
individuals or information about individuals in
supposedly anonymous – even synthetic (Stadler et
al., 2020b) – datasets creates a great deal of
uncertainty about which technical measures are
adequate to both legal standards and practical
expectations. The well-known tension between utility
and privacy is thus amplified. It is crucial to measure
privacy through the lens of adversarial attacks and
precisely distinguish what is meant by personal data
and non-personal data and above all determine
whether ML models represent pseudonyms from the
training data. It is, therefore, preferable to adopt an
approach that incorporates the above mentioned
vulnerability and volatility of the models into the
training while retaining utility. A GDPR-compliant
use of ML models requires technical measures
specified by government standards (yet to be
Hence, this work will first address the concept of
identifiability and the scope of privacy criteria that
lead to effective anonymisation (or
pseudonymisation) and transfer these findings to ML
models. Building on the principles arising from the
GDPR, the Guidelines from the Art. 29 Data
Protection Working Group, nowadays known as the
European Data Protection Board (EDPB) and the
European Union Agency for Cybersecurity (ENISA)
as well as the CJEU’s Breyer judgement were used to
provide the underlying rationale. Further guidance –
albeit with a contrary view – was above all also
provided by Mourby (Mourby et al., 2018), Stalla-
Bourdillon (Hu et al., 2017; Stalla-Bourdillon &
Knight, 2017) and Groos (Groos & van Veen, 2020).
Based on this research, the aim of this paper is not
only to outline the legally relevant scope of
identifiability – which has not been discussed so far
in the context of ML models – but also to combine the
respective concepts in an interdisciplinary manner.
This can only be achieved by drawing on existing
research and established legal definitions and
concepts. To the best of my knowledge, there is no
conceptual and cross-cutting work that is in line with
the recent research concerning the legal outcome of
adversarial attacks and anonymising effects of ML
models and above all the identification of ML models
as pseudonyms of the data used for training.
Approaches to define identifiability can be found in
the GDPR and are closely linked to the concept of
personal data. Personal data under Art. 4 (1) GDPR
means any information relating to an identified or
identifiable natural person (data subject); an
identifiable natural person is one who can be
identified, directly or indirectly. The terms identified
and identifiable are, therefore, of crucial importance
to distinguish the different types of data and to
determine (Stalla-Bourdillon & Knight, 2017)
whether data should be considered personal data.
These vague criteria allow different
interpretations, which leads to a dynamic and thus
different understanding and perception of the
definition. The notion of personal data is, therefore,
quite difficult to define. However, the objective
depends on the question of what personal data is.
Recital 26 expands the notion of identifiability and
makes a distinction between personal data and
anonymised data, excluding anonymised data from
the scope of the GDPR. In view of the
aforementioned risk of re-identification and in order
to avoid misunderstandings and conceptual
ambiguities, the distinction from anonymisation is of
crucial importance. This is primarily due to the fact
that uncertainties exist regarding the classification of
pseudonymised data as personal data or the
classification of technical and organisational
measures that are considered pseudonymisation
measures (Mourby et al., 2018) and not
anonymisation measures. In light of the existing
uncertainties, a differentiation of the anonymising (or
pseudonymising) effect is necessary.
2.1 Anonymising Effect of ML Models
Anonymisation effectively serves as a privacy
protection technique and as a way to remove the
personal character of the data. However, as is
increasingly acknowledged, anonymisation is not a
risk-free mechanism (Brasher, 2018; Mehmood et al.,
2016; Piras et al., 2019; Pomares-Quimbaya et al.,
532019), especially with regard to ML models; and as
demonstrated by Stadler (Stadler et al., 2020a), this
also applies to synthetic data.
Anonymisation is regarded as a process whereby
a data subject can no longer be identified, directly or
indirectly, either by the controller or by a third party
on the basis of irreversibly altered personal data. The
key factor is that a person is not or no longer
identifiable after the data has been anonymised. With
IoTBDS 2022 - 7th International Conference on Internet of Things, Big Data and Security
the criterion of all means reasonably likely to be used
Recital 26 provides guidance in addressing this issue.
The decisive factor is whether identifying the data
subjects is possible with the data and the additional
knowledge. However, the extent to which additional
knowledge and means of third parties should be
included is a matter of dispute. The previous attempts
to define the concept of identifiability and
anonymisation from literature and judiciary do not
draw a consistent picture.
Following the European Court of Justice’s
(CJEU) Breyer ruling, the additional knowledge of
third parties has to be attributed to the controller if the
additional knowledge "constitutes a means which
may reasonably be used to identify the data subject"
(Case C-582/14, Patrick Breyer V Bundesrepublik
Deutschland, 2016). This criterion is not met,
according to the CJEU, if the identification of the data
subject is prohibited by law or practically impossible
because it requires a disproportionate effort in terms
of time, costs and manpower, so that the risk of
identification appears to be insignificant. The legally
permissible means are therefore the decisive criterion
to be discussed.
Favouring a broad interpretation of personal data
the European Data Protection Board (EDPB, formerly
Article 29 Working Party) still refers to its Opinion
5/2014 on anonymisation techniques (Working Paper
216), in which it proposes a high threshold for
achieving successful anonymisation and refers to a
technique comparable to permanent erasure, i.e. "it
must not be possible to further process the personal
data" (Article 29 Working Party, 2014a). According
to this opinion, inference is considered one of the key
risks, which is why a very broad definition has been
chosen (Article 29 Working Party, 2014a). As far as
anonymisation is concerned, WP 216 states that the
data set can be considered anonymous if the
controller aggregates the data at a level where
individual events are no longer identifiable.
This Opinion has not become obsolete after the
Breyer decision. In determining whether a natural
person is identifiable, all the means reasonably
available, either to the controller or to any other
person, to identify the natural person directly or
indirectly should be considered. This includes all
objective factors such as the cost and time required
for identification, the technology available at the time
of processing and technological developments.
The potential additional knowledge of the
controller or a third party is, therefore, relevant. The
determining factor is whether there are (unlawful)
means that can reasonably be used to link the data
held by the controller with the additional information
from the third party to enable re-identification.
Some researchers assume that only the
capabilities of the data controller should be taken into
account, thus excluding the capabilities of third
parties or at least seeing such capabilities as
insignificant (Groos & van Veen, 2020) in regard to
time, cost and manpower. Recital 26 states means
reasonably likely to be used (...) by the controller or
another person (…), which emphasis not only the
controller’s but also the third person’s capabilities.
Notwithstanding the fact that non-mandatory law is
not binding under Article 288 of the Treaty on the
Functioning of the European Union, recitals add a
layer of understanding and define what the rules mean
in the context of a particular case. The recitals must,
therefore, be respected.
Contrary to the court's opinion and statements
from the literature, I argue that even unlawful means
should be included in the discussion. Attacks by third
parties should not be ignored here, as there are often
legally impermissible but technically easy to
implement measures for re-identifying individuals.
For the evaluation of the re-identification potential, it
is, therefore, important to consider not only the
legally permissible measures, but also the technically
possible ones. The evaluation of the likelihood must
apply an objective criterion, i.e. it must not depend on
the motivation or the intention to obtain the means or
to actually use it in a particular case. Taking into
account the high risk of re-identification mentioned
above it seems questionable whether the CJEU took
this into account in its deliberations. The risks of re-
identification, therefore, also address the way
attackers can identify data subjects in data sets. This
means that if prohibited means allow re-identification
the data is not considered anonymised. With the
attack methods mentioned above it becomes evident
that despite disjoint data sets in the ML process
inferences cannot be completely prevented. The
generated additional knowledge can be accomplished
with reasonable effort. With Privacy Preserving ML
– as will be shown – there are nevertheless measures
that mitigate the risk of re-identification (Article 29
Working Party, 2007) and can be used to a reasonable
extent. By implementing technical and organisational
measures as part of the training process, and
especially by considering the inherent risk of
adversarial attacks, all means that could reasonably
be used to identify the data subject are taken into
account in determining the risks.
The ML models can consequently be interpreted
as personal data by re-identifying the data contained
in the training data through an adversarial attack. All
The Concept of Identifiability in ML Models
information that relates to a person - no matter how
trivial or banal it may seem - is considered personal
data. If one follows this line of reasoning, information
obtained through an adversarial attack is also personal
2.2 Pseudonymising Effect of ML
As already indicated the concept of additional
information is closely linked to that of
pseudonymisation. A pseudonym is considered a
piece of information that – depending on the
pseudonymisation function – are associated to an
identifier of a data subject (with different degrees of
linkability) and, therefore, carries the risk of being
subject to a re-identification attack, as those described
above. Pseudonymisation's decisive feature is that,
according to Art. 4 No. 5 GDPR, pseudonyms can no
longer be associated with a specific person without
the use of additional information (ENISA European
Union Agency for Network and Information Security,
Both background knowledge and personal
knowledge must be included in the criterion of
additional knowledge. The latter is the information
that could be kept separately from the dataset by
technical and organisational measures whereas
background knowledge corresponds to the
knowledge that is publicly accessible to an average,
reasonably competent individual which cannot be
physically separated from the dataset and can have a
high impact on re-identification risk. Personal
knowledge, on the other hand, can vary from one
person to another and represents information that is
not publicly accessible to an average, reasonably
competent individual, but to some qualified
individuals. In combination with anonymised data,
this personal knowledge in conjunction with the
derived attribute(s) can lead to re-identification or at
least disclosure of (potentially sensitive) information
about an individual. Therefore, the use of additional
information is central to the re-identification risk and
on the same time strength of the pseudonymisation.
This process can be more or less complex depending
on the pseudonymisation function.
It remains to be ascertained if ML models
represent pseudonyms.
It is, therefore, sufficient if the data subject can be
identified and statements can be made about his or her
factual and personal circumstances (Article 29
Working Party, 2007). In the training process certain
properties of the training datasets are stored in the
model as feature vectors - regardless of whether they
are labelled or stored, which basically depends on the
application or learning technique. Support Vector
Machines or k-nearest neighbour classification
methods store the feature vectors whereas neural
networks, for example, do not, but can remember
them unintentionally (Carlini et al.). In the latter, a
model inversion attack, for instance, generates feature
vectors similar to those used to train the model by
using the outputs obtained from the model. Such
training data sets, which consist of a set of features
and an associated output, may contain sensitive
information - like medical records or images - and
thus have quasi-identifiers or values of other features
that can be used to identify individuals. According to
the GDPR, this information is considered personal
data. The ML process is reversible, insofar as an
external assignment rule remains and thus a general
possibility of re-identification exists.
Based on this, the following constellation was
developed to illustrate the effects of Model Inversion
It is assumed that there is a generated data set
with personal data A and an ML model B
trained with personal data B. Access is given
either via the model directly (white box) or via
an interface (black box).
In such a constellation model B represents the
pseudonymised version of the training data set B
while data set A represents the key or the assignment
rule which can be used to (partially) re-identify this
data. If an attacker has access to A and model B and
the model inversion attack is successful, it seems
possible to consider not only A but also model B (and
its output) as personal data. If model B has been
published and A as well as model B are kept by
different persons, model B is also considered personal
This constellation can also be applied to
Membership Inference Attacks and Model
Manipulation Attacks. In contrast to Model Inversion
Attacks, in a Membership Inference Attack, the
shadow models comparatively represent the key or
assignment rule, whereas in a (black box) Model
Manipulation Attack, the key or assignment rule is
considered to be the enriched randomly generated but
unique data that can be used to retrieve the
information stored in the labels.
This information cannot be obfuscated in ML
models that are vulnerable to adversarial attacks to
the extent that re-identification is no longer possible.
Reversibly pseudonymised data is considered
indirectly identifiable information about individuals -
even if the disclosure is not made consciously
IoTBDS 2022 - 7th International Conference on Internet of Things, Big Data and Security
possible - with the exception of the model
manipulation attack - under previously specified
conditions. Conceptually, the above is thus close to
the notion of pseudonymisation in the GDPR.
Consequently, a ML model attacked by
adversarial attacks can, therefore, no longer be
considered anonymous. Re-identification of the
training data seems to be within the realm of
possibility. Such an ML model should, therefore, be
subject to similar principles and regulations that apply
to identifiable data.
2.3 Technical and Organisational
These results are extremely problematic from the
perspective of a researcher or company that wants to
use ML models and attribute an anonymising effect
to them.
As described above, the personal identifiability of
data depends on the context. Therefore, it is necessary
to regularly assess whether data can (still) be
considered anonymous. As a consequence, personal
identifiability has to be determined dynamically and
risk-dependently. A change in the situation can also
lead to a change in the risk of (re-)identification. This
is affected, for example, by the knowledge of third
parties or by new developments in (de-
)anonymisation techniques (Article 29 Working
Party, 2014b) A non-recurrent risk analysis is
therefore insufficient. Anonymisation in general is,
therefore, subject to a number of uncertainties,
especially with regard to the fact that the relevant
technical and social circumstances can change rapidly
over time. Despite anonymisation, a residual risk may
remain for the data subject (Article 29 Working Party,
2014b). These risks also apply to ML models.
Inferences cannot be completely prevented, as seen
above. It is possible to retrospectively draw
conclusions from the ML model to the data used for
training. Adversarial attacks can, therefore, lead to a
different classification of the (output of) ML models
that were previously considered anonymous.
Consequently, if vulnerable ML models do not
represent anonymous data, methods must be found
that can guarantee both utility and data protection.
This also applies to the training process of ML
The question is how one can reliably defend the
above mentioned attacks on pseudonymisation.
Firstly, the whole training dataset and all data values
should be considered. Secondly, any knowledge and
inferences should be eliminated if possible.
Therefore, an effective privacy protection technique
should be applied. However, the challenge is to
ensure privacy protection without reducing utility.
The trade-off between protection and utility is
Based on the risks highlighted above, it is
essential to be cautious when using ML models to
process sensitive information. One has to consider not
only what kind of model is used and how it is
provided, but also how the data should be prepared
before the training process. Many algorithms
commonly used are based on the assumption that they
need raw data. However, with Privacy Preserving
ML, there are methods (Al-Rubaie & Chang, 2019;
Gambs et al., 2021; Jia et al., 2019; Milad Nasr et al.,
2018; Mukherjee et al., 2021; Nasr et al., 2019) to
reduce the effectiveness of the above attacks while
preserving utility. There are several approaches
depending on the application and model. Gambs
(Gambs et al., 2021) demonstrate, for example, an
approach for synthesised data based on Differential
Privacy that reduces the risk of adversarial attacks
while preserving utility whereas Mukherjee
(Mukherjee et al., 2021) optimise current approaches
to mitigate Membership Inference Attacks on GAN
models that previously resulted in poorer generated
sample quality. The authors stated not only that their
method provide protection against Membership
Inference Attacks “while leading to negligible loss in
downstream performances” (Mukherjee et al., 2021)
but also that their algorithm prevent memorisation of
the training data set. In order to prevent Membership
Inference Attacks it is also proposed to limit the
number of classes that a model can predict to the most
commonly used classes. Avoiding overfitting a model
can also be beneficial (Yang et al., 2020). The use of
regularisation techniques like dropout (Srivastava et
al., 2014) may contribute to prevent overfitting and
also to strengthen privacy (Jain et al., 2015) in neural
networks. However, no guarantee exists that a model
is completely invulnerable to attack. In some cases, it
has been shown that such attacks are successful even
without overfitting the model (Yeom et al., 2017).
However, overfitting is not the only reason that
causes Membership Inference Attacks. Even if ML
models are overfitted they could leak different
amounts of membership information. Specifically,
due to their different structures, they might remember
information about the data used for training.
Nonetheless, if no raw data is used for training,
the risk assessment of whether personal data is
affected could be completely different. However, it
should clearly be stated that previous approaches to
defend against Membership Inference Attacks have
limited effect on Model Inversion Attacks. To my
The Concept of Identifiability in ML Models
knowledge, there is no known method that adequately
defends both attacks.
As outlined above, the ambiguous terms of the GDPR
as well as the classification of ML models as
pseudonyms cause a number of problems which need
to be addressed. It is, therefore, important that
adjusted and comprehensive guidelines and best
practices are elaborated to eliminate the uncertainty
as to which technical offerings meet legal standards
as well as adequately match practical expectations.
Measuring privacy through the lens of adversarial
attacks is therefore crucial. As stated earlier, one
should not rely solely on the belief in a data
anonymising ML model but also that the model be
assessed with respect to a privacy test based on
adversarial attacks to quantify the privacy protection
provided by the processing method. It is, therefore, of
crucial importance that the question of whether a data
set is considered to be personal, pseudonymised or
anonymised can be answered without ambiguity. The
GDPR could apply depending on the outcome.
Concerning ML, anonymous data itself does not
guarantee privacy without the support of other
techniques. Which does not mean that anonymisation
is a useless tool, but it must be applied with the
support of other Privacy Preserving mechanisms. In
order to properly assess and mitigate privacy threats,
a risk-based approach to be evaluated regularly
should be adopted, taking into account the purpose
and overall context of the processing of personal data,
as well as the degree of utility and scalability.
Choosing technical and organisational measures
depends on various parameters, e.g. the level of data
protection and the utility of the pseudonymised data,
which may lead to different approaches or even
variations of approaches. The trade-off between
utility and data protection should carefully be
analysed. On one side, utility need to be optimised for
the intended purposes while keeping a strong data
protection. This field of privacy preserving ML is
gradually becoming a highly debated topic and is a
challenging one, with a high dependency on matters
of context, involved entities, data types and additional
knowledge. There is consequently no single approach
that fit for all possible scenarios. A one-size-fits-all
solution is not sufficient. Applying robust
pseudonymisation to reduce the risk of adversarial
attacks and maintain the utility of the pseudonymised
data requires a high level of competence.
It is therefore necessary to develop a holistic and
legally binding concept consisting of governmental
and technical measures. The following criteria can
provide preliminary orientation.
The Data Protection Authorities as well as the
EDPB should, therefore, provide practical guidance
with regard to the assessment of the risk and best
practices in the field of pseudonymisation and
anonymisation. The definition and explanation of the
state of the art is of crucial importance. Furthermore,
the notion of identifiability and anonymisation needs
to be readdressed. The adversarial attacks are
evolving which leads to more and more challenging
anonymisation and pseudonymisation process. The
authorities should, therefore, extend the current
techniques to more advanced solutions addressing the
special challenges appearing with ML models. The
relevant EU institutions should provide support and
disseminate these efforts. To achieve more legal
certainty, manageable standards for anonymisation
associated with presumption rules in the event of
compliance should be established at EU level.
Considering the advancing technical development, it
would also be advisable to provide the standards with
a temporal validity. Furthermore, standardised test
procedures should be developed to check supposedly
anonymous data for personal identifiability.
Guidance on appropriate or inappropriate techniques
is therefore indispensable. The Article 29 Data
Protection Working Party as well as ENISA provided
guidance about the use of various privacy
technologies – including Differential Privacy. The
anonymisation and pseudonymisation techniques
should be revisited concerning the aforementioned
highly probably adversarial attacks and the inherent
flexible determinability of the degree of anonymity.
Orientation could be formulated at EU level by means
of a guideline for determining suitable anonymisation
procedures, as well as addressing criteria for
determining appropriate procedural parameters. The
regularly updated technical guideline of the
Bundesamt für Sicherheit in der Informationstechnik
(Federal Office for Information Security in Germany)
„Kryptographische Verfahren: Empfehlungen und
Schlüssellängen“ (Cryptographic Procedures:
Recommendations and Key Lengths), which gives
recommendations for cryptographic procedures could
be the role model in this respect.
This work has the limitation of being purely
theoretical. Nonetheless, it provides not only a
revisited evaluation of the notion of identifiability and
the classification of ML models as pseudonymised
data but also an insight into the inherent risk for ML
models as well as sufficient technical and
IoTBDS 2022 - 7th International Conference on Internet of Things, Big Data and Security
organisational measures which has to be standardised.
Overall, the work provided ample background
information on relevant concepts concerning
anonymisation and pseudonymisation and how to
deal with the fact that the ML process does not have
an anonymising effect. Clearly, more practical
interdisciplinary work linking up adversarial attacks
and Privacy Preserving techniques with regulation
and data protection efforts needs to ramp up. Above
all, it is important that the uncertainty associated with
adversarial attacks is surmounted by governmental
and technical standards, which will be developed in
the future.
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