Towards Privacy by Design in Personal e-Health Systems
George Drosatos
, Pavlos S. Efraimidis
, Garrath Williams
and Eleni Kaldoudi
School of Medicine, Democritus University of Thrace, Dragana, Alexandroupoli, Greece
Dept. of Electrical & Computer Engineering, Democritus University of Thrace, Kimmeria, Xanthi, Greece
Department of Politics, Philosophy and Religion, Lancaster University, Lancaster, U.K.
Keywords: Privacy by Design, Personal e-Health Systems, Privacy-Enhancing Technologies.
Abstract: Personal e-health systems are the next generation of e-health applications and their goal is to assist patients
in managing their disease and to help both patients and healthy people maintain behaviours that promote
health. To do this, e-health systems collect, process, store and communicate the individual’s personal data.
This paper presents an analysis of personal e-health systems and identifies privacy issues as a first step
towards a ‘privacy by design’ methodology and practical guidelines.
An aging population, increasing rates of chronic
diseases, and rising healthcare costs represent
important pressures towards forms of self-
management of health and disease outside health
care institutions. New techniques of self-
management have become feasible owing to the
advent of a variety of personal e-health systems,
including wearable sensors (Swan, M., 2012),
personal health records (Johansen, M. A., &
Henriksen, E., 2014) and self-management and
empowerment applications for a number of diseases
(Samoocha, D., Bruinvels, D. J., Elbers, N. A.,
Anema, J. R., van der Beek, A. J., 2010), delivered
via smart phones or other portable personal devices
(Mosa, A. S. M., Yoo, I., Sheets, L., 2012), as well
as via integrated smart home environments (Teng,
X. F., Zhang, Y. T., Poon, C. C., Bonato, P., 2008;
Pantelopoulos, A., Bourbakis, N. G., 2010).
Personal e-health systems are designed to be
used by the citizens themselves to acquire, store, and
manage personal health data. This single user access
makes it easy to forget or ignore the inherent
security and privacy risks involved. Privacy-related
legislation, e.g. the European Data Protection
Directive (European Parliament, 24 Oct. 1995) and
the HIPAA (Health Insurance Portability and
Accountability Act) (104th U.S. Congress, 21 Aug.
1996) explicitly defines the rules for protecting the
privacy of patients and covers issues such as access
rights to data, how and when data are stored,
security of data transfer, data analysis rights, and
governance policies. However, it is widely
recognized that taking a strong regulatory approach
is not always enough, and that privacy safeguards
should be built in the design, operation and
management of information processing technologies
and systems (European Commission, 2012).
This paper focuses on contemporary personal e-
health systems and offers a generic description of
their functionalities. Privacy concerns for each
modeled system’s functionality are discussed and
possible technical solutions are summarized. The
domain analysis presented here is the first step
towards a methodology for engineering privacy in
the design of a personal e-health system, and
practical guidelines for selecting and developing
appropriate privacy preserving techniques.
Information privacy refers to the legal right to
privacy in the collection and sharing of data about
oneself. Privacy concerns arise wherever uniquely
identifiable data relating to a person are collected
and stored, in digital form or otherwise (European
Parliament, 24 Oct. 1995). Privacy is related to, but
not to be confused with data security, which refers to
protecting data from risk of destruction or alteration
and from unauthorized use. Here we focus solely on
data privacy.
Drosatos, G., Efraimidis, P., Williams, G. and Kaldoudi, E.
Towards Privacy by Design in Personal e-Health Systems.
DOI: 10.5220/0005821404720477
In Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2016) - Volume 5: HEALTHINF, pages 472-477
ISBN: 978-989-758-170-0
2016 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
A basic data privacy principle refers to the
importance of individual consent and control: the
right of each individual to protect her privacy by
retaining control over her personal data and knowing
who, when and why gets access to her data. Further
principles include those of data minimisation, data
protection by design, and data protection by default
(European Commission, 2014). Data minimisation
means that when an authority/party requires some
information in order to provide support, only the
minimum amount of personal information needed to
give that support is transmitted. Data protection by
design is about engineering privacy measures into
each part of a personal data system. Finally, data
protection by default requires that the default
operation of any system should be to preserve
Primary privacy concerns include (Hansen, M.,
2012): (A) User identification: The variety of
personal data and the quantity of them constitute a
key risk factor in undermining privacy. The greater
the amount of personal data an adversary possesses,
the better able she will be to identify an individual
(Narayanan, A., Shmatikov, V., 2010). (B) Personal
data leakage: Careful management and storage of
personal data are crucial, especially when these data
are health records (Kierkegaard, P., 2012).
Appropriate security should be applied to avoid
accidental disclosures and handle potential attacks.
(C) Linkability issues: A user may appear in several
datasets of institutional systems. Precautions have to
be taken to avoid the linkage by an adversary of the
corresponding partial profiles of the user, since this
would generate a larger and more revealing profile.
Technical countermeasures like pseudonyms and
data anonymization, and policy measures (clear
terms on data usage) can be used against this privacy
threat. Thorough treatment of privacy concerns and
principles are given elsewhere (Danezis, G.,
Domingo-Ferrer, J., Hansen, M., Hoepman, J. H.,
Metayer, D. L., Tirtea, R., & Schiffner, S., 2015).
Personal e-health systems are designed to be used by
citizens or patients themselves in order to maintain
their health and manage disease mainly outside the
healthcare context, thus promoting heath literacy,
disease prevention, integrated care, and self-
management. Apart from traditional health-related
personal data, such as health records and biomedical
sensors’ data, personal e-health systems may also
utilize data from the user’s surroundings, the user’s
web activity, and other health-related services.
In general, a personal e-health system acquires,
stores and processes personal health data, either
manually entered by the individual or collected via
other personal systems, e.g. sensors or e-health
applications. This might also be complemented by
data on the environment of the individual (e.g.
geolocation, temperature, allergens, etc.), again
usually acquired via personal sensors or the mobile
device itself. Furthermore, a personal e-health
system may require personal data from medical and
personaldat ain
personaldat ain
Figure 1: Data communication requirements in a personal e-health system.
Towards Privacy by Design in Personal e-Health Systems
other institutional systems, e.g. medical health
record segments, electronic prescriptions, insurance
and financial details. Finally, personal e-health
systems may require access to public databases, e.g.
medical ground knowledge or health educational
material. Figure 1 presents a graphical overview of
data requirements for a personal e-health system.
Based on the requirements for personal data
communication, we can identify the following five
basic personal e-health systems functionalities
(Figure 2): (1) personal data storage and processing;
(2) personal data exchange with other third party
systems (personal or institutional); (3) integration of
(personalized) public data; (4) exporting personal
data for public (e.g. statistical) use; (5) exchange of
private personal data messages.
3.1 Acquisition, Storage and Processing
Storage and processing of personal data are the core
components of a personal e-health system. When
both components are located on a user device then
privacy can generally be maintained by default.
However, nowadays, the most common case is that
storage and/or processing are located on a remote
server and most often on a cloud infrastructure.
In case personal data are stored on a remote
service, their security and privacy need to be
ensured. The most common techniques for this are
cryptographic techniques and especially techniques
that perform client-side encryption of data to protect
against untrusted service providers (i.e., Cloud
providers). A good review of the cryptographic
mechanisms for data storage in the remote services
(and especially in the Cloud) is provided in
(Kamara, S., Lauter, K., 2010) and more general
advanced cryptographic schemes are given in
chapter 5 of (Smart, N., Rijmen, V., Gierlichs, B.,
Paterson, K. G., Stam, M., Warinschi, B., Watson,
G., 2014a).
However, simple encryption of stored data is
generally not efficient because at some point some
data processing (even a simple search and retrieve)
will be required. In such a case the user would have
to allow the service provider to decrypt the data
(thus compromising privacy), or download all data
to the user-side to decrypt and process, or use some
computationally intensive approach like searchable
encryption. In general, data processing is a complex
procedure involving dedicated logical checks,
computations and searching over personal data.
Thus, there are no generic solutions to support
processing of encrypted data. There are some
approaches to this problem that offer varying
degrees of privacy and/or processing quality
assurance, as discussed below.
The most privacy preserving approach is to
process encrypted data. There are a number of
emerging technologies, such as (fully) homomorphic
encryption and searchable encryption, which aim to
give general solutions in this direction (Smart, N.,
Rijmen, V., Stam, M., Warinschi, B., Watson, G.,
2014b) or even simpler homomorphic techniques
that may require some pre-processing (Drosatos, G.,
Efraimidis, P. S., 2014). However, all these
techniques have the following limitations: (a) data
should be generally pre-processed before encryption;
(b) processing of encrypted data is computationally
more intensive than processing of unencrypted data;
and (c) all these techniques cannot, in practice, be
applied in all cases but have to be considered on a
case-by-case basis.
toa nonymous
(e.g.registriesorst atistical pooling)
Figure 2: Modelling basic functionalities of a personal e-health system.
HEALTHINF 2016 - 9th International Conference on Health Informatics
An alternative approach involves using a user
proxy service to sanitize data, includins such
measures as anonymizing, minimizing, transforming
and/or aggregating personal data before submitting
them for (unencrypted) remote processing (e.g.
Layouni, M., Verslype, K., Sandıkkaya, M. T., De
Decker, B., Vangheluwe, H., 2009). In this approach
anonymous credentials (Camenisch, J.,
Lysyanskaya, A., 2001) can be used to prove that the
proxy corresponds to a valid system user and at the
same time allow anonymity to be revoked under
special predefined circumstances (e.g. if a life
threatening situation is detected as a result of
From the point of view of data security, personal
data should be encrypted as close to their generation
as possible, preferably at their source. This imposes
additional demands on personal sensor devices
commonly used as a data source for a number of
personal e-health systems.
3.2 Data Exchange with Other Systems
A quite common requirement or functionality in
personal e-health systems is to share and exchange
data with other similar systems. For example,
personal health record systems usually provide the
functionality of integrating data from a number of
personal biomedical sensors, such as the free
personal health record service HealthVault
(HealthVault, accessed 1 Nov. 2015) or the personal
health avatar service by the MyHealthAvatar project
(MyHealthAvatar Project, accessed 1 Nov. 2015).
Personal decision support systems may also require
integration of other sources of personal data, e.g. the
health risk predictive system developed by the
CARRE project (CARRE Project, accessed 1 Nov.
2015). Less commonly, data might be exchanged
with institutional systems that hold personal data
(e.g. personal insurance or financial data or even
electronic health records maintained by healthcare
Such personal data exchange between two
personal systems requires that one system knows
and uses the user’s credentials for authentication in
the second system. This gives rise to two major
problems. The first concerns the potential for
malevolent use of the other system’s credentials.
This actually represents more of a security problem
and will not be further discussed here. The second
problem concerns linkability, that is, linkage of the
different user accounts in various personal systems
to a single user. Linkability is a more general
concept in personal systems. The most basic
linkability relates a system user to an actual physical
person. In personal systems this can be more easily
achieved, as the system user does not have to be
directly linked to a physical person via a strong
identifier. For example, in a personal system the user
may decide to use pseudonyms (although this may
not entirely solve the problem as a person may be
identifiable by other data even when her name is not
known). When a system knows and uses different
user accounts on different systems the use of
pseudonyms represents the most usual approach to
preserve anonymity and thus, indirectly, privacy.
However, integrating partial personal data of the
user (as residing in each individual system) to a
larger and thus more comprehensive and revealing
data set increases privacy concerns.
Generally, there is no direct remedy for this
problem. The most obvious solution involves
building dedicated middleware that will act as a user
proxy for all personal systems. This would reside on
the user side and would unlink the flow of personal
data among the systems, hiding each system and
system account from the other.
3.3 Integration of Public Data
Personal e-health systems may also involve runtime
integration of personalized public data. A common
example is to fetch on-line publicly available health
promotion and educational material suited for the
particular user’s condition, another example, to fetch
information on healthcare resources (nearest doctor,
specialty hospital, etc.). Although the data are
publicly available, just the act of linking particular
data to a specific user may cause a privacy violation,
by revealing the user’s presumed health care needs.
There are a number of proposed techniques to
conceal user requirements by altering the initial
request, e.g. by expanding and generalizing the
request for public data. These techniques fetch a
large amount of data to the user application and then
a second round of local processing extracts the
specific data relevant to the user (Drosatos, G.,
Efraimidis, P. S., Arampatzis, A., Stamatelatos, G.,
Athanasiadis, I. N., 2015). Other emerging
approaches require the cooperation of a group of
users in the system to conceal one another’s requests
(Romero-Tris, C., Viejo, A., Castellà-Roca, J.,
An alternative is to use anonymous network
technologies that protect the physical address of user
from the public service. A representative example is
the TOR service (Dingledine, R., Mathewson, N.,
Syverson, P., 2004), which creates a network of
Towards Privacy by Design in Personal e-Health Systems
proxies over the internet and allows recursive
message encryption along the chain of proxies.
3.4 Exporting Personal Data
Personal e-health systems may need to export
anonymised personal data to external services (e.g.
medical registries) and/or provide data for statistical
use. Exporting personal data to medical registries
raises the problem of de-identification (Fung, B.,
Wang, K., Chen, R., Yu, P. S., 2010). Here data
should be minimized and stripped of all identifiable
parts. Some examples of data that can be used to
identify an individual include the identity number, or
a combination of the birth date and the zip code.
However, it is vital to remember that it may be
possible to identify a person even when seemingly
non-identifiable data are released. One of many
interesting example involves the identification of a
woman in the United States based on processing of
anonymous web search engine query-logs from
about 650,000 users over three months (Pass, G.,
Chowdhury, A., Torgeson, C., 2006).
When exporting personal data for statistical data
pooling, privacy preservation can be promoted by a
number of techniques that compute aggregated
results (e.g. Lindell, Y., Pinkas, B., 2009; Drosatos,
G., Efraimidis, P. S., 2014). The privacy issues that
arise in this type of system depend on the number of
patients who are included in an aggregated result
too small a number may still reveal sensitive
information about the participants. Another sort of
privacy goal involves concealing personal
information from the processing module (similarly
to a voting system). Here, the selection of
appropriate techniques depends on the location
(remote or at the user) of storage and the particular
form of statistical processing.
3.5 Exchange of Private Data Messages
Occasionally, personal e-health systems may need to
exchange private data messages with trusted parties.
This includes communicating with a medical
professional or a family member. This data
communication may be eponymous, that is the user
chooses to reveal their identity. However, following
the privacy by default principle, the general case
must require the anonymous exchange of personal
data messages. In this process the receiving party is
unaware of the identity of the sender; however, they
can still respond and return a data message. This can
be achieved using anonymous credential techniques
(Camenisch, J., Lysyanskaya, A., 2001). Messages
can be exchanged via a bulletin board where the
original data message and the response are
published. The confidentiality of exchanged data
messages between the end users (sender and
receiver) can be achieved using a secret pre-agreed
key for the encryption of messages. A representative
example for privately and unlinkably exchanging
messages is presented in (Hoepman, J. H., 2015).
This paper focuses on contemporary personal e-
health systems and presents a generic high-level
model of their functionalities. Privacy concerns for
each functionality have been discussed and possible
technical solutions presented.
Here we have given only a high-level overview
of personal e-health system functionalities; more
detailed or even case-by-case analysis would be
required to thoroughly cover the plethora of personal
e-health applications. Furthermore, the focus of our
analysis is on privacy. Data security, while essential,
is not discussed as it is generally treated as a lower
level storage and communication prerequisite.
Work in progress takes into account the analysis
presented here as the basis of a formal step-by-step
methodology for building privacy preserving
personal e-heath applications. Such a methodology
can then be combined with available reviews of
privacy strategies and technical solutions (e.g.
Danezis, G., Domingo-Ferrer, J., Hansen, M.,
Hoepman, J. H., Metayer, D. L., Tirtea, R., &
Schiffner, S., 2015) to create a set of practical
guidelines for selecting the ideal privacy enhancing
technologies in the development of new personal e-
health systems.
This work was supported by the FP7-ICT project
CARRE (No. 611140), funded in part by the
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Towards Privacy by Design in Personal e-Health Systems