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

Keywords:

Genetic Information, Blockchain, Patient, Healthcare.

Abstract:

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

1 INTRODUCTION

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.

https://orcid.org/0000-0002-7510-618X

Chief Resolution (In Spanish) N 490-2019-J -

https://bit.ly/2RQ0k5X

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-

tion

. 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 -

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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

171

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.

2 BACKGROUND

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

172

(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 - https://bit.ly/3kvgwG2

3 MAIN CONTRIBUTION

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

173

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.

4 RELATED WORKS

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

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Table 1: Expert Survey Data.

Validation

Date

Expert

Recording

Link

Survey

Link

Answers

Blockchain

(

06/10/2020 Expert 1 https://bit.ly/3kVQFYr https://bit.ly/31uVArq https://bit.ly/3mqyoCV

07/10/2020 Expert 2 https://bit.ly/34sRTEL https://bit.ly/31uVArq https://bit.ly/3mqyoCV

Laboratory

(

06/10/2020 Expert 1 https://bit.ly/3dYeYCs https://bit.ly/3dOrwMv https://bit.ly/3kTXeuB

09/10/2020 Expert 2 https://bit.ly/37CukLH https://bit.ly/3dOrwMv https://bit.ly/3kTXeuB

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.

5 EXPERIMENTS

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”

• Ethereum

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:

https://bit.ly/37HAcDk

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

user.

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: https://bit.ly/2TRl5iA

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 -

https://bit.ly/2RQ0k5X

Technological Model for the Protection of Genetic Information using Blockchain Technology in the Private Health Sector

175

Figure 4: Final Proposal for the Technology Model.

Table 2: Average genetic consultation time report

Medical Genetics

Consultation

Average Time

(minutes)

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

176

(a) Security importance level of genetic information (b) Level of sharing of genetic information with other

people

ternet

(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%.

6 CONCLUSIONS AND

PERSPECTIVES

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

177

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

diagnosis according to a region or using a chatbot to

detect the interact with the users (Solis-Quispe et al.,

2021).

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