1. Introduction
When COVID-19 outbreak, the epidemic swept the
world, the impact can not be estimated. In particular,
this new coronavirus is different from the coronavirus
in the past. When people face this situation for the f rst
time, they are helpless. It’s not just the slow development
and diff culty of vaccine production; the existing medical
information system is also limited effect. If an additional
information system is developed and designed for the
new coronavirus epidemic, it will be time-consuming,
labor-intensive, and ineff cient. Based on this viewpoint,
our study intends to propose a simple and eff cient new
coronavirus epidemic monitoring information system, the
system uses cryptography and blockchain technology, and
has anonymity function. Digital health is an interdisci-
plinary subject integrating medical treatment, information
technology and medical service administration. The major
medical units in the world are actively studying how to
improve the system function and service eff ciency. At
present, the technology is mainly used in mobile medicine,
telemedicine, and health analytics. The application of
digital health care can be combined with the business
model of health care, and the real-time supervision mech-
anism of COVID-19 is urgent. The anonymity system
needs to have a high degree of security, supervision and
operability design. The identity of patients is anonymous
to the hospital and the health bureau. To a certain extent,
the identity is prevented from being leaked in this link;
the health bureau has the function of supervision and
inspection for the hospitals or doctors, and to prevent fraud
between patients-doctors or doctors-hospitals dealers. If
the transaction content is verif ed to be untrue, the system
center has the right to decode the patient’s identity and
hold it accountable. Our research uses this as the design
framework, combined with the ElGamal [1] and RSA [2]
algorithm as the cores, introduces digital signature tech-
nology, and adjusts the algorithm to enhance the overall
security. The algorithm includes 8 stages, namely: reg-
istration stage, account issuance stage, order placement
stage, order conf rmation stage, transaction return stage,
report business stage, data inquiry and supervision and
inspection stage. The last 7 stages are the main content
of discussion. , This solution meets the requirements of
computational security and theoretical security.
2. Literature Review
In 2013, Chiang [3] began to study the medical
research and personal data protection issues related to
Japanese epidemiology. In 2016, Bouslimi and Coa-
trieux [4] proposed a medical image research with cryp-
tography digital watermarking system, which has the char-
acteristics of reliability and traceability. Abdmouleh et
al. [5] discussed the encryption of medical images using
JPEG algorithm in 2017. Anand and Singh [6] proposed
an improved DWT-SVD secure watermarking algorithm
in 2020. In 2021, Zermi et al. [7] also proposed a DWT-
SVD based robust watermarking algorithm for medical
image security in the Forensic Science International. On
April of the same year, Fares et al. [8] also proposed
a medical information security watermarking scheme by
on DCT and DWT. Chen et al. [9] used mobile phone
positioning signal to track the whereabouts of passengers
on the Diamond Princess Cruise, Park et al. [10] also
used information technology tracking technology to detect
the epidemic of cowid-19 in South Korea, Liu et al. [11]
proposed a tripartite anonymous information system for
patients-doctors-hospitals to reduce the leakage risk of
personal data privacy. For the research on the application
of blockchain technology in medical treatment and health,
please refer to [12]–[29]. In this paper, the author lists
some literatures which discuss the application of other
f elds in health or medicine , such as blockchain, image
processing, watermarking, information security and so on.
Due to limited conditions, this study lists parts of good
contributions, but is a little different then what is discussed
in this article, please see Table 1.
Study of COVID-19 Monitoring System Based on Block Chain and
Anonymity Techniques
1CHENGLIAN LIU, 2SONIA C-I CHEN
1School of Computing, Neusoft Institute of Guangdong, Foshan 528225, CHINA
2School of Economics, Qingdao University, Qingdao 266061, CHINA
Abstract: Since the COVID-19 epidemic has been raging around the world, it has a great impact on people’s life
and work; in addition to monitoring and controlling the epidemic situation, government departments are also
facing the impact of economic downturn. The investigation of epidemic situation and the privacy of personal
information often contradict each other. It is a headache for countries all over the world how to make both sides
perfect. This study uses blockchain technology, combined with cryptography theory, proposes a set of epidemic
monitoring information system, which has the function of anonymity. When hospitals or doctors release patient
information, they do not need to worry about personal information leakage.
Keywords: ElGamal Algorithm, Anonymity, Double-Blind Mechanism
Received: July 25, 2021. Revised: June 27, 2022. Accepted: August 8, 2022. Published: September 14, 2022.
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Table 1. RELATEDLIT ERATURES
Year Blockchain Image Watermarking Others
2013 Chiang [3]
2014
2015
2016 Bouslimi & Coatrieux [4] Bouslimi & Coatrieux [4]
2016 Yue et al. [12]
2017 Abdmouleh et al. [5]
2017 Linn & Koo [13]
2017 Kuo et al. [14]
2018 Bayle et al. [15]
2018 Dasaklis et al. [16]
2018 Sadiku et al. [17]
2019 Dimitrov [18]
2019 Manset et al. [19]
2019 Agbo et al. [20]
2019 Khezr et al. [21]
2019 McGhin et al. [22]
2020 Fang et al. [23]
2020 Liu et al. [11]
2020 Capece & Lorenzi [24]
2020 Park et al. [10]
2020 Chen et al. [9]
2020 Anand & Singh [6]
2020 Farouk et al. [25]
2020 Hasselgren et al. [26]
2020 Bell et al. [27]
2021 Miyachi & Mackey [28]
2021 Zermi et al. [7] Zermi et al. [7]
2021 Hussien et al. [29]
2021 Fares et al. [8]
3. Our Research Methodology
This paper extends the concept of information secu-
rity technology and management, specif cally introducing
cryptography and information security mechanisms into
the COVID-19 monitoring system, combining the two
cryptographic algorithms of ElGamal [1] and RSA [2]
to meet the requirements of the digitalization process of
electronic medical records. In the process of patients using
the medical insurance card, the information center can set
the identity of the person who knows or does not know
(double blind mechanism). Based on this design concept,
the medical staff passively know or does not know the
patient’s identity. In this paper, we propose a conditional
anonymity scheme. In the process of submission, patients
and the system center have registered and issued account
numbers, and patients, hospitals, doctors and the health
insurance bureau are anonymous. In the process of the
system, the patient has no direct contact with the health
insurance bureau, so the health insurance bureau can not
know the real identity of the patient at the initial stage;
the role of the health insurance bureau has the right
to supervise and inspect the doctor’s visit content and
inquire about the hospital information; the hospital has the
responsibility to report the business to the health insurance
bureau; the doctor has to report the visit situation to the
hospital. This scheme of the algorithm consists of eight
phases: registering phase, account issuing phase, medical
treatment phase, diagnosis phase, data verif cation phase,
data update phase, data response and f nal result return
phase.
Step 1. Patient opens an account and register to hospital’s
system center.
Step 2. The system center issues an accounts to patient
who applied an ID previously.
Step 3. Patient goes to hospital or clinic to meet a doctor
when he feel ill.
Step 4. The doctor diagnoses patient and sent the diag-
nostic record to system center.
Step 5. The system center received the record from doctor
before returned the verif cation.
Step 6. The doctor updates record with health bureau.
Step 7. The health bureau responds the updating action
to doctor.
Step 8. The doctor feeds back the result to patient.
The detailed information f ow is shown in Figure 1.
Notation and Signif cant:
pi: denote a large prime of RSA.
qi: denote a large prime of RSA.
nidenote a modulo number of RSA.
eidenote the public key of RSA.
didenote the secret key of RSA.
p1: denote an other prime number of ElGamal, it different
with pi.
g: is the primitive root of prime number p1.
xi: is a private key in ElGamal like algorithm.
yi: is a public key in ElGamal like algorithm.
m: digitized message.
Health Bureau: The Health Bureau (HB) means
Ministry of Health and Welfare (MHL) or National
Health Insurance Administration (NHI) in Taiwan. The
names of medical institutions in different countries, it
may be varieties.
Hospital: We usually means the hospital (or clinic)
information system center. Here, we use abbreviation
‘hospital’ or ‘system center’.
Doctor: We denote the staff who works in hospital.
There are including nurse and doctor. We preferred mean
to doctor who is qualif ed in medicine and treats people.
Patient: A common person or user who is ill.
Patient
Hospital
Doctor
1.Register
2. Pass
3. See the doctor
4. Diagnose
5. Verify
6. Update Health
7. Respond
9. Take
8. Feedback result Bureau
account
medicine
Figure 1. The concept of this system.
3.1. Initializing System Phase
3.1 In the system initialization phase, all users such as
patients, doctors, system center and health bureau set their
own account numbers and passwords, and share primitive
parameters gand a large prime numbers p1through the
system. The patient randomly selects a number xa, as its
private key and satisf es gcd(xa, p−1), then calculates his
public key
ya≡gxa(mod p)(1)
The hospital (or system center) randomly selects its own
private key xbto calculates its own public key yb, and
then announces
yb≡gxb(mod p)(2)
The doctor randomly selects its own private key xcto
calculates its own public key yc, and publishes it
yc≡gxc(mod p)(3)
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The health bureau will randomly select its own private key
xdto calculate his public key yd, and then publishes
yd≡gxd(mod p).(4)
Please see Figure 2. Every Users (Patients) randomly
Patient Hospital Doctor Health Bureau
Compute:
ya≡gxamod p1yb≡gxbmod p1yd≡gxdmod p1
yc≡gxcmodp1
ra≡gkamod p1rb≡gkbmod p1rd≡gkdmod p1
rc≡gkcmod p1
Figure 2. The System Initializing Phase.
select two primes piand qito f nd:
ni=pi·qi,(5)
since
φ(n) = (p1−1) ·(qq−1).(6)
Compute the public key eiwhere it satisf ed
gcd(ei, ni) = 1 (7)
and
ei·di≡1 (mod ni).(8)
The public key pairs are (ei, ni), although the secret key
is di; we have destroyed some parameters such as (pi, qi
and φ(ni)) based on security issue. From Equation (5) to
(8), it is well-known RSA algorithm [2].
3.2. Registering Phase
3.2 The Patient uses his ElGamal private key xaand
the RSA secret key dato calculate a temporary account
from Equation (9) to (11),
ya≡gxa(mod p1),(9)
ra≡gka(mod p1),(10)
Ra≡(ya·ra)da(mod na),(11)
and register this account to system center (hospital), see
Figure 3.
Patient Hospital
1. {Ra}
Ra≡(ya·ra)da(mod na)
Figure 3. The Registration Phase.
3.3. Issuing Account Phase
3.3 When the hospital receives Rafrom patient, hos-
pital approved and returned Pasince
Pa≡(Ra)eaxb·yxb
c(mod na),(12)
see Figure 4.
Patient Hospital
Pa≡(Ra)eaxb·yxb
c(mod na)
2. {Pa}
Figure 4. The Account Issuing Phase.
3.4. Meeting Doctor Phase
3.4 The patient obtains a valid account and he then
uses Waand mbefore he went to hospital to meet a
doctor. This operation has an anonymous feature:
Wa≡(Pa·m)da(mod na),(13)
see Figure 5.
Patient Hospital
3. {Wa,m}
Wa≡(Pa·m)da(mod na)
Figure 5. The Watch Doctor Phase.
3.5. Filling Record Phase
3.5 When the doctor receives the patient’s requirement,
he will diagnose patient and sent the diagnostic record to
system center for processing. The process is shown in
Equation (14) and Figure 6.
Ca≡yWa
c·Wxc
a(mod p1).(14)
Hospital
Doctor
4. {Wa,Ca}
Ca≡yWa
c·Wxc
a(mod p1)
Figure 6. The Fill Record Phase.
3.6. Returning Transaction Phase
3.6 The hospital received the diagnostics record by a
doctor, he would check this identif er Waf rstly; if it is
hold, and then verif ed this message before returned to
doctor. See Equation (15)-(16) and Figure 7.
Wea
a
?
≡(Pa·m) (mod na).(15)
If holds, to calculate the Equation (16).
Va≡Cxb
a·(Wxc
a)−xb(mod p1).(16)
Doctor Hospital
Va≡Cxb
a·(Wxc
a)−xb(mod p1)
5. {Va}
Figure 7. The Transaction Return Phase.
3.7. Reporting Business Phase
3.7 The doctor updates diagnostic record such as
calculation Equation (17), and the results to the health
bureau, see Figure 8.
Ua≡(Va)·ykc
d(mod p1),(17)
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Doctor Health Bureau
6. {Ua}
Ua≡(Va)·ykc
d(mod p1)
Figure 8. The Report Business Phase.
3.8. Inquiring Data Phase
3.8 When the health bureau received the doctor of
updating with diagnostic, he can compute Equation (18)
and (19), see Figure 9. The health bureau calculates
Ap0≡(Ua)·r−xd
c(mod p1),(18)
and
Apa≡(Ap0)·yxd
c·m′(mod p1).(19)
Doctor Health Bureau
7. {Apa}Apa≡(Ap0)·yxd
c·m′(mod p1)
Ap0≡(Ua)·r−xd
c(mod p1)
Figure 9. The Information Inquiry Phase.
3.9. Supervising and Inspecting Phase
3.9 In addition to supervising and inquiring the con-
tents of the reports of the stock exchange, the SFC can
also supervise and inspect securities companies, see Equa-
tion (14) and Figure 10.
Fa≡(Apa)·y−xc
d·m−1·y−xc·Wa
b(mod p1).(20)
Patient Doctor
8. {Fa}Fa≡(Apa)·yxc
d·m−1·y−xc·Wa
b(mod p1)
Figure 10. The Supervision and Inspection Phase.
Proof.
m′?
≡Fa·m(mod p1)
≡(Apa)·y−xc
d·m−1·y−xc·Wa
b·m(mod p1)
≡m−1·m′·m(mod p1)
≡m′(mod p1)(21)
123243
Patient
Hospital
Doctor
1. Ra
2. Pa
3. Wa, m
4. Wa,Ca
5. Va
6. Ua
Health
7. Apa
9. Take
8. FaBureau
account
medicine
Figure 11. The protocol of this scheme.
4. Security Analysis
1312321
Def nition 1. Discrete Logarithm Problem (DLP)
As known parameters {p, g, yi}where the formula
yi≡gxi(mod p), it is very hard to f nd the private key xi
while prime approaching inf nite. Based on this assump-
tion of computation and condition, it is called solving
the discrete logarithm problem (Solving Discrete Loga-
rithm Problem) [30]. The current public key cryptosystem
based on discrete logarithm has value parameters that are
greater than 1024 bit length or 2048 bit length.
Def nition 2. Computation Diff e-Hellman Problem
(CDHP)
The Computation Diff e-Hellman Problem [31]is derived
on the Diff e-Hellman key exchange principle (Diff e Hell-
man Key Exchange) [32]. The main ideas are described as
follows: Given {g, gx, gy}to f nd gxy . Here, gis known
parameter, the xand yare unknown parameters.
Def nition 3. Decisional Diff e-Hellman Problem (DDHP)
The Decisional Diff e-Hellman Problem [33] is a vari-
ant of the Diff e-Hellman computation problem. Given
{g, gx, gy, gz}, to f nd the Zpis satisf ed z=xy. Given
{g, gx, gy}, to f nd gxy. Here the parameter gis known,
and the parameters {x, y, z}are all unknown.
4.1. Theoretical Security Level Analysis
Theoretical Security Level Analysis Analysis security
of theoretical level
Lemma 1. If patient is honest, then the Equation (6)
holds, that is, the hospital verif ed the patient.
Proof. The patient registers Rathrough the system center
by Equation (5). The system system center uses its RSA’s
public key eato check Wea
a
?
≡(Pa·m) (mod na),
if it does not equal, it is determined that the patient
deception. Otherwise, patient honestly use his private key
xain Equation (1), the patient naturally can not deny
his behavior, the program has a “user-repudiation” in this
scheme.
Lemma 2. If doctor is honest, the Equation (7) holds,
that is to say, the system center and doctor verif ed each
other.
Proof. As known from Equation (13), doctor sent two
parameters Caand Wato system center. System center
calculates Equation (15) by Equation (14); in this mean
time, if system center honestly use his private key xbto
compute Vabefore he returned to doctor. The doctor can
verify this Equation (22) if it holds. Otherwise, it is not
equal, the system center cheated doctor.
Va
?
≡ywa·xc
b(mod p1).(22)
If doctor cheated in Equation (13), namely diagnosing
phase, it produced a wrong parameter Caand then trans-
mitted to system center, the system center naturally can
not f nd the correct value Va. Wrong input, of course get
wrong output. The Vawas computed by system center
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who can self-checking if doctor honest or not, namely
Equation (23)
Va
?
≡ywa·xb
c(mod p1),(23)
we rewrite Equation (22) and (23), get
Va
?
≡ywa·xc
b
?
≡ywa·xb
c(mod p1).(24)
Till now, it means “the system center and doctor verif ed
each other”.
Lemma 3. If doctor and Health Bureau are both interac-
tion honestly, the Equation (16) and (17) holds, the doctor
and health bureau can verify each other.
Proof. As known the health bureau received Uafrom
doctor, the health bureau honestly use his private key xd
to calculate Ap0with Apaby Equation (18) and (19),
and then returned Apato doctor, the doctor recoveries the
message through Equation (19). If doctor want to decode
m′, he should calculate Equation (25)
m′≡Apa·V−1
a·y−xc
d(mod p1).(25)
Since doctor obtained Vaby system center, and get the
Apafrom health bureau. We rewrite the Equation (25)
into
m′?
≡Apa·V−1
a·y−xd
c(mod p1)
≡yWa·xb
c·yxd
c·m′·V−1
a·y−xc
d(mod p1)
≡m′(mod p1).(26)
4.2. Analysis of practical safety levels
Analysis security of practical levels
Doubts about cracking RSA and ElGamal cryptosys-
tems: If the attacker intends to disguise the identity of
the patient, the attacker must have the patient’s key da to
be able to calculate the corresponding pairing public key
ea. In addition to being unable to disguise the patient,
the attacker cannot disguise the system center, unless
the attacker can crack the RSA cryptosystem. Obviously
cracking the RSA cryptosystem is not realistic at the
moment [34].
Key Compromise Impersonation attacks: The patient,
system center, doctor and Health Bureau keep their own
keys. Although their public keys are published, the hackers
can not calculate the corresponding key through known
public parameters. The discrete logarithm problem of the
Def nition 1 is def ned and fully described. This study
does not consider this assumption unless any party who
owns the key divulges the key.
5. Conclusion
This research is mainly about the four-party supervi-
sion and management plan of patients, hospitals, doctors,
and the National Health Bureau. The improved ElGamal
and RSA algorithm are used in the application of the
COVID-19 monitoring system. This information system
is anonymous and the identity of any patient is strictly
controlled. Keep it secret. If the hospital (system) is
invaded, hackers cannot obtain patient health record or
content through the hospital. If the hacker colludes with
any of the regulatory agencies to deceive, he still does not
have to worry about identity exposure. If the patient has
breached the contract, the doctor and the system center can
track the anonymous identity under certain conditions, and
f nally restore the anonymous identity to the real-name
user identity. In this way, the patient’s security can be
protected. The identity is protected from exposure, and
on the other hand, it can deter patient from maliciously
defaulting on transactions. This program has the best of
both sides. This research plan puts forward 3lemmas,
3def nitions and 26 equations to run through the full
text, provide a strong theoretical support for the thesis,
and f nally put this idea into reality. This idea shows a
patient-hospital monitoring system with anonymity, non-
modif cation, security, and double-blind mechanism to
achieve a combination of theory and practice.
Acknowledgments
The authors would like to thank the anonymous re-
viewers for their useful comments.
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WSEAS TRANSACTIONS on MATHEMATICS
DOI: 10.37394/23206.2022.21.74
Chenglian Liu, Sonia C-I Chen
E-ISSN: 2224-2880
640
Volume 21, 2022
