Technological Model for the Protection of Genetic Information using
Blockchain Technology in the Private Health Sector
Julio C
esar Arroyo-Mari
nos, Karla Mariella Mejia-Valle and Willy Ugarte
Universidad Peruana de Ciencias Aplicadas (UPC), Lima, Peru
Genetic Information, Blockchain, Patient, Healthcare.
Currently, genetic information is considered a valuable element in the Health Sector, since due to more accu-
rate diagnostic samples in medical genomics this allows to offer better treatments to patients against various
types of diseases. This paper presents the development of a technological model using Blockchain as technol-
ogy to ensure the protection of genetic information in the Private Health Sector because activities related to the
storage or management of data of this information present many points of vulnerability. In addition, activities
such as registration and control of access in the exchange of genetic information have been administered in an
unauthorized manner, mainly through entities that manage the eligibility of users with genomic information
and allow access to specific data sets, which shows a lack of harmonization in access policies between the
owners of their genetic information and the health entities that manage it. A proof-of-concept has been per-
formed to validate the capabilities of the model and ensure that a larger-scale deployment can be performed.
The evaluation showed that experts agree with the proposal and that users would be willing to use proof of
concept to ensure traceability and security of their information
Today, medicine that considers the patient’s genetic
information as a variable to consider is already a real-
ity. Genetic testing within clinical practice is provid-
ing results aimed at confirming or obtaining a more
accurate diagnosis of genetic diseases, deciding treat-
ment, or assessing the risk of relapse to certain types
of diseases such as cancer and its derivatives, hepati-
tis, and others.
In Peru, the management of genetic information
in health entities is not very pronounced, in view of
this INEN shows a comparison of previous years in
which it is highlighted that the waiting time for a con-
sultation takes an average of 60 minutes. In addition,
the number of genetic information consultations dur-
ing the years 2013 to 2018 has been on average 450
consultations per year
In the face of this, the great advancement of tech-
nological development has brought with it within the
field of health an important and profound develop-
ment of the genomic information of the human being.
Chief Resolution (In Spanish) N 490-2019-J -
In view of this, various genetic analysis method-
ologies have been applied, as well as the biomedi-
cal implementations necessary for the interpretation
of identified alterations (Shabani, 2019a).
A clear example of this is the digitization of the
genome, which is born by the need to store genetic
information digitally. With this, doctors can stream-
line diagnostic and treatment processes in people.
However, as with personal information, it is of
paramount importance that a person’s genetic infor-
mation is protected for privacy reasons, and they also
bring with them a major challenge regarding the trans-
port and storage of personal genetic information in a
simple, secure and anonymous manner because peo-
ple do not want anyone to manipulate their informa-
. Encouraging open and responsible exchange of
genomic data is a central focus of many national and
international programmes and initiatives for their im-
plementation and use.
However, there are some challenges based on the
adoption of centralized approaches to data storage,
exchange and access due to the constraints faced by
these central mechanisms and the non-automated na-
ture of traditional data access and exchange to es-
Emerging Tech 2015: Digital Genome -
Arroyo-Mariños, J., Mejia-Valle, K. and Ugarte, W.
Technological Model for the Protection of Genetic Information using Blockchain Technology in the Private Health Sector.
DOI: 10.5220/0010422401710178
In Proceedings of the 7th International Conference on Information and Communication Technologies for Ageing Well and e-Health (ICT4AWE 2021), pages 171-178
ISBN: 978-989-758-506-7
2021 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
tablish central mechanisms in data access manage-
ment (Shabani, 2019b).
Genomic data security risks are highlighted be-
cause data storage and management activities that
contain genetic information have many points of vul-
nerability. Even if there are guidelines for protecting
patient data in health facilities, due to law regulations,
these are frequent target of computer attacks.
For example, 90% of health care organizations
and partner companies that responded to a Ponemon
survey in 2016 reported having experienced a data
breach, and 64% reported a violation involving the
leak of patient medical records (Ujibashi et al., 2016).
Recently technologies have emerged the context
of genomic data with the aim of improving access to
data, patient empowerment and interoperability.
For example, the use of Cloud Computing tech-
nology for the storage and sharing of genetic data,
which allows the scaling of genetic data flexibly and
ease of use for users (Yang, 2019).
However, activities such as registration and access
control in the exchange of genetic information have
been administered in an unauthorized manner, mainly
through entities that manage the eligibility of users
with genomic information and allow access to spe-
cific datasets, which shows a lack of harmonization
in access policies between the owners of their genetic
information and the health entities that manage it.
Adopting an “authorized” structure using
Blockchain technology, which limits access to a
patient’s data, will allow transparency in these trans-
actions and, in addition, the owner of that genomic
information to manage and manage it.
Although the metadata that the datasets describe
is available to everyone on the system, that does not
mean that the data stored in Blockchain is readable
to everyone and this is because Blockchain “Is based
on pseudo anonymity and public key infrastructure
(PKI), allowing Blockchain content to be encrypted
in a prohibitive way to decrypt”.
Implementing such technology could enable de-
tection of existing datasets, while protecting people’s
privacy by restricting access to approved users. We
propose a technological model for the management
of genetic information security with Blockchain tech.
Our contributions are as follows:
We define a technological model, which allows to
carry out activities such as obtaining the appro-
priate information, in the right way, for the right
person, at the right time, in the appropriate place.
The components used ensure transparency and
data integrity, as the participants of the chain are
known and there would be no chance of falsifica-
tion or theft of information.
We use the Ethereum Framework for data process-
ing with low resource consumption.
This paper is organized as follows. Section II de-
scribes the relevant concepts and definitions related
to this document. Section III presents our contribu-
tion. Section IV discusses Related Works. Section
V shows the analysis of the results obtained. Finally,
section VI shows the conclusions and perspectives.
This section provides the concepts that will be ap-
plied in the development of the model to be pro-
posed for the protection of genetic information using
Blockchain technology in the Private Health Sector.
Definition 1 (Blockchain (Tripathi et al., 2020)).
Blockchain is defined as a distributed and immutable
digital ledger that provides data transparency and
user privacy.
This concept is derived from bitcoin and is
based on cryptography and Peer-to-Peer (P2P) net-
works, in which data from a given structure is or-
ganized into blocks and organized into a data chain
in chronological order in a chain structure forming a
blockchain (Cheng et al., 2020).
Likewise, there is no central authority and the dig-
ital ledger is shared among all peers for all to see.
While it is true that this concept was initially linked
to financial transactions, technological advances and
its enormous benefits have made this technology part
of the various areas where record security is of an im-
portant nature (Tripathi et al., 2020).
Example 1. The generation of each block occurs by
generating the genesis block or block “0”, and the
corresponding transactions. Each block in this struc-
ture has a unique identifier called a hash, and this
is generated each time the contents of the block are
changed, which the header of the previous block be-
comes the hash of the next block, as shown in Fig-
ure 1a. This block interconnect provides the secu-
rity feature to the blockchain, as any modification to
the data in each block requires updating hash values
throughout the chain, making it impossible to manip-
ulate or hack information (Tripathi et al., 2020).
Definition 2 (Encryption (Benil and Jasper, 2020)).
At Blockchain, encryption is responsible for ensuring
the integrity of the information and the anonymity of
the owner of that information using algorithms.
This technology is accompanied by a hash func-
tion, which allows new data to be added to the block.
ICT4AWE 2021 - 7th International Conference on Information and Communication Technologies for Ageing Well and e-Health
(a) Building a Block (b) Flow of a Smart Contract.
Figure 1: Blockchain Structure.
Specifically, any changes that are made to the
ledger such as adding a new transaction must be re-
calculated (Benil and Jasper, 2020). The use of dig-
ital signatures allows confirmation that the recipient
received the transactions and that the sender is not an
imposter (Benil and Jasper, 2020).
Definition 3 (Smart Contract (Ozercan et al., 2018)).
In Blockchain, Smart Contracts are a set of instruc-
tions that have special storage for information related
to the developing application.
This storage has a record that is distributed as a
copy to all nodes in the network (Zhang et al., 2018).
This allows, for example, the ability to automate the
use of notifications in health applications, which gen-
erates better integration between devices (Chen et al.,
2019). In addition, it allows an analysis of medical
data, which allows to activate alerts about certain un-
usual activities (Griggs et al., 2018).
Example 2. As shown in Figure 1b, two Org1 and
Org2 organizations are observed, which define a
Smart Contract for consulting, transferring and up-
dating cars
Definition 4 (Blockchain in the Health Sec-
tor (McGhin et al., 2019)). The health sector has data
security and privacy requirements due to the existence
of legal standards in the protection of patient informa-
tion established by governments.
Malicious attacks that compromise the integrity of
such information due to internet use in the exchange
of patient information (Khezr et al., 2019) are now a
matter of concern. Therefore, the requirements that
Blockchain technology conceives in the health sector
are such as the exchange and transfer of medical and
genomic data, as well as authentication and interoper-
ability (McGhin et al., 2019).
Smart Contracts and Chaincode -
Now we describe our proposal fo the Blockchain-
based technology model for the protection of the ge-
netic information, as shown in Figure 2.
3.1 Roles
For the design of the technological model, the roles
that have been raised are:
Generator Agent with the following activities:
Generation: The Generator Agent is respon-
sible for recording the genetic information of
the patient who has given him the registration
permit, so that the information recorded in the
blockchain ensuring traceability.
Authorization/Validation: The Generator
Agent is authorized to add genetic information
from the patient.
Consumer Agent with the following activities:
Distribution: The Consumer Agent will be
able to consult the genetic information of the
patient registered in the system
Authorization/Validation: The Consumer
Agent is authorized to consult and visualize the
genetic information of the patient
Patient with the following activities:
Generation: The patient grants registration
permission to the Generator Agent so that the
generator can add their genetic information.
Authorization/Validation: The patient grants
the respective permissions as a Generator
Agent or Consumer Agent so that they can
manage their genetic data
Distribution: The patient grants the consulta-
tion permission to the Consumer Agent so that
he/she can consume their genetic information
Technological Model for the Protection of Genetic Information using Blockchain Technology in the Private Health Sector
Figure 2: Proposed Technology Model.
Figure 3: Web Portal Architecture.
3.2 Web Site
The model will have the representation of a web por-
tal in order to show the registration of the genetic in-
formation of the patient and the control of accesses of
this information by the same actor. The technologies
involved in developing this application are HTML5
by the client view, ASP.Net for the back end, SQL
Server as the database manager, and the Microsoft
Azure Cloud platform.
The Blockchain part used the Ethereum Frame-
work for the development of the Genetic Informa-
tion Generation Smart Contract, which each time such
information is recorded, a transaction is generated
which contains the block ID, the transaction creation
date, and the cryptographic hash generated. This can
be seen in the architecture of the portal in Figure 3.
3.3 Blockchain Platform
For transactions made in the development of the
model, the Blockchain platform acts as the technol-
ogy with the ability to record transactions made in the
web application and transfer it to a network of nodes
where this network is worth the transaction. More-
over, each transaction has a cryptographic hash in or-
der to validate the claims that people make against the
tracked and managed assets in the blockchain, which
in this case is genetic information.
Now, we describe the related works of our proposal.
In (Fu et al., 2020), the authors propose a process
of light mechanism for preserving the privacy of med-
ical records, which is composed of the creation and
storage of medical information. Unlike this, our pro-
posal adds the process of distribution of information.
(Kuo et al., 2020) propose a security storage
model for medical data based on registration and au-
thentication. Our proposal contrarily gives access
control by the owner of the information and the pro-
cess of distribution of such information.
(Kulemin, 2017) propose a Blockchain-based ar-
chitecture of the Zenome system, in which the roles
ICT4AWE 2021 - 7th International Conference on Information and Communication Technologies for Ageing Well and e-Health
Table 1: Expert Survey Data.
06/10/2020 Expert 1
07/10/2020 Expert 2
06/10/2020 Expert 1
09/10/2020 Expert 2
defined in their system are 3: Person, Data Consumer
and Service Provider. However, our proposal involves
other access to the person’s information as the litera-
ture mentions levels of privacy of such information,
and in our case is granted directly to the other roles.
(Murugan et al., 2020) propose a medical data ex-
change solution using Blockchain technology, this so-
lution encompasses processes such as healthcare and
data exchange. However, our proposal covers both the
process of recording information and distributing
Most of these works cover the process of data log-
ging and storage, but they do not cover distribution or
access control, or in reverse, if they attack processes
such as distribution or access control do not cover pro-
cesses such as data logging and storage.
Therefore, with our proposal we seek to contem-
plate the processes globally both the generation and
distribution of genetic information.
Now, we test scenarios to validate the feasibility of
our proposal.
5.1 Experimental Protocol
The tools used for the experiments are as follows:
Visual Studio 2017 with C#
Microsoft SQL Server Management Studio 18
Microsoft Azure with “App Services”
Also, the entire project both the code developed
for proof-of-concept that validates the functionali-
ties of the model and the data registered in the
database is publicly available at the following link:
In addition, virtual presentations have been made
for the validation of the proof of concept in which the
project proposal was explained to the experts selected
through the Zoom platform. After the presentations
made, surveys were submitted which aimed to seek
feedback from the experts and the user.
For this we have segmented surveys into differ-
ent groups that make references to expert groups,
Blockchain Expert and Laboratory Expert, and to the
1. Blockchain Expert Survey: The Blockchain Ex-
pert survey was divided into 13 questions aimed
at receiving feedback based on your Blockchain
experience and feedback on our proposal.
Two Blockchain experts have also been selected.
2. Laboratory Expert Survey: The survey to be con-
ducted by the Laboratory Expert was divided into
14 questions whose objective is to receive feed-
back at the functional level of the Model and com-
ments towards our proposal.
Two laboratory experts are also selected.
3. User Survey: A survey was conducted on the user,
who represents the patient in our model.
For the purposes of our proposal we have a sample
of 40 people, which belong to an age range of 20
to 30 years.
All these sample users are resident in the city of
Lima Metropolitana and genetic testing has been
done at least once.
Some of the questions to be answered by them are:
“What level of importance do you think should
be given to the security of your genetic informa-
tion?” and “At what level would you be willing to
share your genetic information with others?” This
survey is divided into 9 questions and the user sur-
vey link is as follows:
Table 1 gives the links of the Survey given to the
Blockchain and Laboratory experts, the link of the
recordings made, the date of the sessions and the plat-
form on which the presentation was made.
Similarly, statistical data collected by INEN were
obtained, which can be seen in Table 2, which writes
the average genetic consultation time, which is esti-
mated at 60 minutes ranging from patient identifica-
tion, genetic testing registration and diagnosis.
In addition, Figure 5 shows a statistic collected by
INEN4 describing the increase in genetic information
Chief Resolution (in spanish) N 490-2019-J -
Technological Model for the Protection of Genetic Information using Blockchain Technology in the Private Health Sector
Figure 4: Final Proposal for the Technology Model.
Table 2: Average genetic consultation time report
Medical Genetics
Average Time
Patient Admission 5
Medical Consultation 5
Exams 40
Genetic Pretest Counseling 10
Total Average time 60
Figure 5: Relationship between total consultations per year
and new consultations of the Medical Genetics Office
consultation transactions from 2013 to 2018 achiev-
ing an average of 450 consultations per year and iden-
tifying a gap between supply and demand.
5.2 Experimental Results
On the one hand, Blockchain experts agree that pro-
tecting genetic information transparently and using
Blockchain as a technology to ensure the management
and assurance of this asset is a correct choice. They
also agree with the idea that the patient owns and can
manage their genetic information. However, they in-
dicated that additional encryption processes should be
established to the model in order to achieve better as-
surance of genetic information, to define the compo-
nents of the model, since, being a model, these can be
reused for later architectures and solutions.
On the other hand, laboratory experts indicated
that they agree with the project presented as it facil-
itates the registration and access of genetic informa-
tion and safeguards it with Blockchain technology.
They also found the need identified by the vulner-
abilities that occur frequently within health systems
correct, and the design of the model is simple and
easy to understand, the roles managed are sufficient
and prefer a web environment in terms of proof of
concept for management issues in health facilities.
Whereas they emphasized that not always the pa-
tient can manage their information since depending
on the clinical condition that he presents would need
the support of a doctor for his management and that
a classification of genetic information can be estab-
lished as paternity studies, autoimmune diseases, de-
generative diseases, among others.
Based on the comments and recommendations
provided by the experts, the following components
were added in Figure 4:
The encryption layer, which adds additional en-
cryption processes to provide greater security.
A “Health Regulations” component, which aims
to detail that the Legal Rules regarding the use of
patient information are considered.
A “Doctor” role, which assume the patient’s ac-
tivities according to the patient’s clinical picture.
From the users surveyed, Figure 6a shows that
58% rate as “Very High” the level of importance to be
considered with the security of their genetic informa-
tion. Figure 6b also shows that 45% of all respondents
define “Unlikely” in sharing their genetic information
ICT4AWE 2021 - 7th International Conference on Information and Communication Technologies for Ageing Well and e-Health
(a) Security importance level of genetic information (b) Level of sharing of genetic information with other
(c) Perception of having genetic information over the In-
(d) Level of willingness to share genetic information
with our proof of concept
Figure 6: Comparison of users’ perceptions.
Figure 7: Comparison of average genetic consultation time.
Figure 8: Comparison of average genetic consultation time.
with others. And this is supported by Figure 6c, as
the percentage of respondents who do not feel con-
fident that their genetic information is on the internet
reaches 63% of the total. However, by using our proof
of concept, 60% would be willing to share your infor-
mation, this can be viewed in Figure 6d.
Similarly, we have the metric of the average time
of genetic consultations which with our proposal we
seek to reduce the time of this metric, for which a
comparison was made with the current time and the
average time offered by the proposal.
As seen in Figure 7, the current average time is
60 minutes, while our proposal’s average time is 5
minutes, reducing the current average time by 92%.
Nevertheless, another metric that is handled is that
of the number of genetic queries. As shown in Fig-
ure 8, the current number of consultations is 450,
while our proposal is 700, which is increased by 56%.
In conclusion, we have shown that Blockchain tech-
nology is effective and efficient, because, having an
average genetic consultation time of 60 minutes, with
Technological Model for the Protection of Genetic Information using Blockchain Technology in the Private Health Sector
this technology allows us to have it in 5 minutes and
save the time of genetic consultations by up to 92%.
In addition, it allows us to increase the number of
genetic information queries, as the number of cur-
rent average queries is 450, while with the use of
this technology a total of 700 is estimated, which im-
proves by 56% and users are much more willing to
share their genetic information if they have a plat-
form that guarantees the traceability and protection of
their data. However, it would be interesting to be able
to work with geolocalisation (Cueva-S
anchez et al.,
2020), which would allow to classify the genetic in-
formation of the patient in order to provide a better
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