Enabling Quantum Key Distribution on a Multi-Cloud Environment to

Secure Distributed Data Lakes

Jose Luis Lo Huang

and Vincent C. Emeakaroha

Department of Computer Science, Munster Technological University, Cork, Ireland

Keywords:

Multi-Cloud, Quantum Computing, QKD Protocol, Data Lake, Quaternions.

Abstract:

Each day more and more data is produced by different sources including humans and machines, and they

are stored mainly in Cloud Service Providers (CSP) in huge data storages known as big data or data lakes.

Many situations warrant users to spread their data in a distributed way between the different CSP. When this

happens, they have to relay the inter-cloud communication security to each cloud vendor. This can cause data

leakage in the transmission channel thereby compromise information security. Quantum computing has shown

some promises to address this issue. One well known algorithm in quantum cryptography is the Quantum Key

Distribution (QKD) protocol. This enables the sender and receiver of a message to know when a third party

eavesdropped any data from the insecure quantum channel. There are studies integrating this QKD protocol

with cloud storage and data transmission inside one CSP. However, there is no research that studies the data

lake security concern for distributed multi-cloud communications taking advantage of quantum mechanisms.

This research proposal aims to address this gap in distributed data lake security by using the QKD protocol in

the multi-cloud distributed data transmission. The achieved results show over 91% detection of eavesdropping

cases and over 99% correct authorisation detection in multi-cloud environments.

1 INTRODUCTION

In the last decade, the number of cloud computing

users is increasing dramatically, and each day, more

and more data is transferred and stored in the main

cloud service providers (CSP) such as Amazon Web

Services (AWS)

, Microsoft Azure

, Google Cloud

(GCP)

or IBM Cloud

in big data storages known

as data lakes. Those CSP have to rapidly increase the

amount of data storage and services to access, man-

age and retrieve these data in the most performant

way to improve the running time of the applications

depending on the stored data. This rapid access of

multi-cloud providers can cause information leakage,

which compromises the security of the data.

Advances in quantum computing due to the re-

lease of early prototypes of quantum machines on

the cloud that can execute workloads that would take

a large amount of time in conventional computers

https://orcid.org/0000-0002-1480-0354

https://orcid.org/0000-0001-7869-2885

https://aws.amazon.com/

https://azure.microsoft.com/en-us/

https://cloud.google.com/

https://www.ibm.com/cloud

(Arute, 2019)(Wang, 2018), have shown promises to

address big data leakage issues. A quantum bit (or

qubit ) is the smallest unit of quantum information,

which is usually represented by an atom’s state, elec-

tron, photon or other elementary particle. Unlike a

classical bit, a quantum bit can exist in superposition

states (both 0 and 1 at the same time with different

probabilities) and have more features that permit dif-

ferent approaches not possible with classical compu-

tation.

Addressing data leakage in Clouds uses quantum

cryptography that is also based on quantum mechan-

ics. The Quantum Key Distribution (QKD) protocol

is a specialized quantum algorithm that permit to se-

curely send data between two entities through an inse-

cure link. With this protocol, they can detect if some-

one is trying to capture or is measuring the informa-

tion that traverse the insecure channel.

Current research efforts are focusing on integrat-

ing the QKD protocol into cloud deployments to

secure the communications between a single cloud

provider and users. However, recent trend have shown

the use of multi-cloud deployments to provision ser-

vices that access data from distributed sources. This

trend has brought the security issues around dis-

Lo Huang, J. and Emeakaroha, V.

Enabling Quantum Key Distribution on a Multi-Cloud Environment to Secure Distributed Data Lakes.

DOI: 10.5220/0011993200003488

In Proceedings of the 13th International Conference on Cloud Computing and Services Science (CLOSER 2023), pages 78-89

ISBN: 978-989-758-650-7; ISSN: 2184-5042

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

tributed data lake access to the fore. The key issues

are (i) slow multi-cloud distributed data lake transmis-

sion, (ii) insecurity due to poor parameter conﬁgura-

tions, and (iii) interruption by third party if incorrect

methods are used.

This research will take advantage of the available

quantum computers on the different CSPs to provide a

mechanisms to speed up the access, authorisation and

distribution of data in a multi-cloud distributed data

lake while improving the security using the Quantum

Key Distribution (QKD) protocol. The achieved re-

sults show over 99% correct authorisation detected in

multi-cloud setup. In addition, it detected over 91%

of eavesdropping cases.

The rest of the paper is organised as follows:

Chapter 2 discusses the background and related work.

In chapter 3, we discuss the design of the work. Im-

plementation details are presented in chapter 4. Next,

the evaluations are presented in chapter 5. Finally

chapter 6 concludes the paper.

2 BACKGROUND / RESEARCH

CONTEXT

This section highlights key background information

and discuss related work.

2.1 Background

While the cloud grows each year as the ﬁrst option be-

tween companies deploying new resources, the data

stored there for archival purposes and for data analyt-

ics is large enough to be called big data (Chang and

Grady, 2019) or data lakes. In (Zagan and Danubianu,

2021), the main types of data lakes (on-premises,

cloud, hybrid and multi-cloud) are studied and the

architectures were shown. There are different re-

searches highlighting the applications when working

with huge data lakes. For example, in (Bugbee et al.,

2020), the researchers explained how they achieved a

combined effort between different global teams from

ESA and NASA to architect and manage an open data

lake on the cloud. There are researchers that expo-

sure the speciﬁc use of multi-cloud data lakes, such

as (Imai et al., 2015), where they demonstrate the cost

reduction and the increment in throughput gained by

splitting the data analysis between multiple CSP.

Big data security is an important concern, as the

amount of data increases and is more difﬁcult to en-

crypt, decrypt or verify the authenticity of the stored

data. For example, (Reddy, 2018) discusses the cur-

rent challenges to store, retrieve, process, and imple-

ment security requirements, and possible solutions for

Big Data Security in cloud environments. The au-

thor proposes a pair of algorithms that can be used

as frameworks for big data access and detect external

and internal hackers. Moreover, in (Suwansrikham

and She, 2018) the authors propose a distributed sys-

tem to split big data ﬁles in chunks and store them in

multiple CSP to enhance the security and avoid the

high risk of losing all data when a CSP fails and goes

down.

Quantum computing is a type of computation

based on quantum mechanics. It uses the character-

istics and behaviour of speciﬁc physics phenomena

to achieve results that are not possible with classi-

cal computation. Today there are a good number of

quantum computers hosted on the main cloud service

providers and other private companies. They are still

in early stages and the experts (IDC, 2022) forecast

a 8.6 billion dollar market growth in 2027 in this re-

search area. The main CSPs that offer quantum com-

puting services are IBM, AWS, GCP and Azure. Cur-

rently, IBM is the one with the biggest quantum ma-

chine in terms of qubits quantity with 433 qubits on

an Osprey chip

In classical computation, a bit is the smallest form

of information representation. In any speciﬁc time, a

bit can only be 0 or 1. Meanwhile, in quantum com-

putation, a qubit will result in 0 or 1 as well when

measured, however before the measurement process

it could be 0 and 1 at the same time with different

probabilities. This is a direct consequence of the ap-

plied quantum mechanics participation.

To mathematically represent this behaviour, the

most used representation is the Dirac notation (Dirac,

1939). With this, the 0 qubit and the 1 qubit can be

represented as vectors as follows:

|0i =





(1)

|1i =





(2)

In the zero state (1), when the result is |0i, it could

be interpreted as all the probabilities are in the posi-

tion 0 of the vector. And in the one state (2), when the

result is |1i, all the probabilities are in the position 1

of the vector.

The graphic representation of a bit could be a

switch set to on or off meaning 0 or 1. Meanwhile,

to represent a qubit the usual way is by using a bloch

sphere (Bloch, 1946) (Figure 1 (Beckers et al., 2019))

where the highest point is the |0i, the lowest point is

the |1i and every point between them is a combination

of probabilities between these two values.

https://www.ibm.com/quantum/systems

Enabling Quantum Key Distribution on a Multi-Cloud Environment to Secure Distributed Data Lakes

Figure 1: The Bloch Sphere.

One of the most known quantum protocols in

quantum cryptography is the QKD protocol deﬁned in

1984 by Bennett and Brassard in (Bennett and Bras-

sard, 2014). This protocol consists of using a quan-

tum channel to send a one pad secret message that

will be deformed and being noticed by both sender

and receiver if it is eavesdropped by a third party due

to the physical properties of photons. Using the QKD

that consists of sending a number of random qubits,

and a number of operations, the receiver will measure

those and will share parts of the results. If someone

has measured the data before, then the results from

the measures in the target endpoint will be incorrect.

2.2 Related Work

Quantum computing was recently used in combina-

tion with cloud computing services to improve al-

gorithms, security and existing protocols. For in-

stance, in (Zhang et al., 2019), the researchers pro-

posed a identity-based multiparty revocable quantum-

resistant signature inside a CSP. Also, in (Pedone

et al., 2021) the researchers develop a complete soft-

ware stack to integrate a quantum algorithm into a

cloud environment at the VM level. This provides

the quantum algorithm as a service based on user re-

quests. These efforts show the success of integrating

quantum computing into cloud environments.

There are a good number of researches on the

QKD protocol. For example, in (Diamanti, 2016), the

authors present a survey with the known QKD algo-

rithms in existence such as BB84, EE91, B92, SSP,

SAR04, COW and KMB09. Also, it shows the current

challenges for QKD, mainly Photon Number Splitting

(PNS) attack and Quantum Bit Error Rate (QBER).

Since cloud computing has reached a mature state,

researchers have started using the QKD to experiment

with cloud data storage, data access and data trans-

mission inside the same CSP. In (Murali and Prasad,

2017), the researchers proposed a framework to se-

cure cloud authentication using the QKD protocol.

They experimented using a simulation of 500 qubits

and the BB84 algorithm. Also, in (Lakshmi and Mu-

rali, 2017) there is a comparison analysis between

classical and quantum computation using a QKD sim-

ulator to identify the best algorithms to use in each

case. The conclusion was that the BB84 with TDES

gives the best performance compared to DES, AES

and blowﬁsh algorithms. Both research work focus

on single CSP as compared to our approach consider-

ing multi-cloud.

In (Murali and Prasad, 2016), the authors pro-

vided a framework for QDK in the cloud (private and

public) to secure access from clients to cloud stor-

age. They tested it using the NOT and SWAP quan-

tum gates. While in the private cloud it works ﬁne

with 100% success rate, in the public cloud it didn’t

work due to long distances. They intend to carry

out future investigations to address this issue in pub-

lic clouds. However, our proposed work addresses

this issue in public cloud domain. In the same di-

rection, in (Thangapandiyan et al., 2018), the authors

used a modiﬁed version of the Difﬁe Helman algo-

rithm with quantum generated keys to securely access

personal health data records stored in the cloud. Ad-

ditionally, they proposed to use a QKD based on non-

abelian encryption techniques. When increasing the

ﬁle size, the quantum key generation time was pro-

gressively increased too, which caused a considerable

delay. Even worse, the decryption time increases ex-

ponentially. Our approach showed good performance

in key generation and decryption.

Regarding cloud data transmission, in (Sud-

hakar Reddy et al., 2018), the authors created a hybrid

quantum protocol using 3DES and QKD to demon-

strate the viability of secure dual party communica-

tion between two resources in the cloud. They did

experiments using a simulator for the BB84 part that

must run in a quantum channel. Similarly, in (Zhao

et al., 2020), the authors do a performance analysis

of QKD network structures suitable for power busi-

ness scenarios. They proposed and did simulations

to test a Quantum VPN to enhance the network se-

curity. They conﬁrmed that for shorter quantum sig-

nal state correction time, the quantum key rate efﬁ-

ciency was higher. In (Srivastava et al., 2020), the

researchers uses a modiﬁed version of the BB84 com-

bined with the elliptic curve digital signature to secure

cloud data transmission. However, comparing these

works to ours, they provided no methods to improve

the data access, authorisation and transmission secu-

rity on a multi-cloud environment taking advantage

of the current quantum technologies, speciﬁcally the

CLOSER 2023 - 13th International Conference on Cloud Computing and Services Science

QKD protocol.

To the best of our knowledge, non of the afore

mentioned research work investigated data access, au-

thorisation and transmission security in a multi-cloud

environment considering distributed data lakes and

QKD protocol.

3 DESIGN

This section discusses the design of our proposed so-

lution. Before going deep into it, we ﬁrst highlight

the key challenges to be addressed in this paper for

distributed data lakes, which are:

• The multi-cloud data access and authorization

process is insecure.

The data transmission between the CSP pass

through the Internet and other unreliable net-

works, and can be eavesdropped.

• The multi-cloud distributed data lakes are slow.

When the data reach the CSP, it goes through an

encryption/decryption process that includes all the

bytes.

The QKD protocol is well known as a one-pad secu-

rity process, as discussed previously. Figure 2 shows

an example of the components of the protocol and the

working steps.

Figure 2: QKD protocol example.

The steps are as follows:

• Step 1: Alice chooses a string (the message) and

a random choice of basis or operations. Those are

private to Alice. For example:

string = 100101;

basis = XZZXX Z;

where X means a bit and a Z means an operation.

• Step 2: Alice encodes each bit onto a string of

qubits using the chosen basis and send it to Bob.

This means that each qubit is in one of the follow-

ing states |0i, |1i, |+i or |−i chosen at random.

For example:

message = |1i|−i|+i|0i |0i|+i

• Step 3: Bob measures each qubit randomly using

a chosen basis. For example:

basis = ZZXXZZ

• Step 4: Alice and Bob publicly share the basis

they used for each qubit. If Bob measured a qubit

on the same basis that Alice has prepared it, then

they use this to form part of the shared secret key,

if not, this qubit is discarded. For this example,

the basis are equal in the second, fourth and sixth

position ( Z X Z), then the value on those posi-

tions should be the same for both ( 0 1 1) and will

be part of the key to share.

• Step 5: Finally, both share a random sample

of their keys (which after the previous process

should be 011), and if the samples match, they can

be in a highly probability sure that their transmis-

sion was not eavesdropped by a third party (Eva

in the ﬁgure).

By extracting the ﬁrst bytes of each ﬁle, instead of us-

ing all the ﬁle, to validate the transmission, the speed

should improve and the overall process of data distri-

bution could be faster. The security will prevail with

the QKD protocol and even, would be enhanced as the

algorithm has been researched and tested previously

in several situations. It will detect any eavesdropping

on the network if it happens.

In this research, we aim to use the QKD proto-

col to enhance the data authorisation and transmis-

sion security of data lakes that are distributed in mul-

tiple clouds (multi-cloud). To accomplish this, the use

of the different quantum computers from each CSP

could help by running the quantum sections of the

QKD protocol.

We divide this research into four main layers: the

user layer, the cloud layer, the quantum layer and the

data layer as shown in Figure 3.

In Figure 3, we show how the main layers should

communicate between themselves and therefore how

the data will ﬂow among them. In the next step, we

discuss the responsibilities of each layer.

• User Layer.

This layer is composed by the user of the system

and the programs and hardware that are executed

locally on the user computer.

• Cloud Layer.

All the resources that belongs to any of the CSPs

belong to this layer. For example, the identity,

Enabling Quantum Key Distribution on a Multi-Cloud Environment to Secure Distributed Data Lakes

Figure 3: The main layers of the project.

compute and networking resources that will be

used.

• Quantum Layer.

This layer includes all the quantum computers

(QC), simulators and any software that is pure

quantum or hybrid.

• Data Layer.

Although the resources on this layer also are on

the cloud, the data layer can be taken as a different

section, as it is the ﬁnal destination of the data

ﬁles.

The distributed data ﬁles to be used are expected to

be composed of uncommon elements that will be split

between the CSPs. The quaternions are an extension

of the complex numbers and were ﬁrst deﬁned by the

Irish mathematician sir William Hamilton (Hamilton,

1850). The data to be used for the distributed data

lake will be Hamilton’s quaternions calculations and

huge matrices based on those.

The quaternions can be represented as a linear

combination as shown in the formula 3.

H = α + βi + γ j + δk (3)

To represent quaternions in software, the inputs

needed are the values for α, β, γ and δ . This data

should be able to be generated on the user layer or on

the cloud layer.

In Figure 4, we show the general multi-cloud ar-

chitecture to be used to determine the data transmis-

sion between the different CSPs when splitting the

distributed data. Similar approaches will be used for

data access and authorisation. But instead of the data

in the data lakes, the messages to be delivered as

quantum blocks will be the user data.

In general, the proposed solution is designed to

use the following steps, which will be discussed in

details later on:

• Step 1:

The quaternion data will be generated from a pro-

gram developed during the research, then this data

will be stored on ﬁles with size up to 2GB.

• Step 2:

Those ﬁles will be split and distributed onto the

different CSP by a second program. Once there,

the data will be processed by the quantum com-

puters to execute the QKD tests and the valida-

tions before being stored on the native data lake.

• Step 3:

In parallel, another program will take the times

and evaluate the outputs from each CSP and check

the times. In summary, the following are the main

evaluation outputs from each test:

– The duration of each ﬁle upload, distribution

and storage process.

– The QKD validation output.

– The hash of each ﬁle fragment.

it is also expected to evaluate the complexity of

the algorithms developed during the investigation.

One objective is to tune the algorithms to achieve

a upper bound of logarithmic or linear time. The

latter may or may not be possible.

• Step 4:

In this ﬁnal step, we will review the achieved

results and plot some graphics to conﬁrm and

demonstrate the preposition.

Next, we provide some details to the design steps.

3.1 Data Generation

The ﬁrst step is the data generation. This step is ex-

pected to be able to run both locally and in a cloud

VM. This cloud VM should be a hub between the

users and the multi-cloud quantum data lake system.

It is expected to have an autoscaling group (ASG) for

this cloud hub VM components. This is to increase

the compute power if needed by the program when

generating the ﬁles. Moreover, the architecture in-

cludes two Availability Zones (AZ) for high availabil-

ity purposes.

3.2 Data Distribution

Once the data was generated (if it was generated on

the local machine, it must be transmitted to the cloud

hub VM before this step), the next step is to distribute

the data ﬁles. For this the cloud hub ASG must have

proper connections to all the CSPs. The cloud hub

ASG must have proper connections to the 3 CSPs that

CLOSER 2023 - 13th International Conference on Cloud Computing and Services Science

Figure 4: Multi-cloud distributed data lake architecture.

will be used for the implementation. Additionally,

a cloud database (DB) must be running in the same

VPC as the cloud hub ASG. On this cloud DB, all

the results will be written and also it will be queried

by the researcher to get information related with times

and correctness. Therefore, the cloud DB should have

one instance in each AZ.

3.3 Multi-Cloud Quantum System

Once the data ﬁles enters on this stage, they will be

processed by each CSP in parallel. Each one running

its native quantum code with the different quantum

API (Braket for AWS, Qiskit for IBM and Cirq for

GCP).

3.4 Full System Design

The full system includes all the previously mentioned

components. The data ﬁles are generated locally or

in the cloud VM hub, then they are distributed to each

CSP server using the internal network from each CSP.

Ideally it should use a quantum network as the chan-

nel. Once the data reach this stage, the header of each

data ﬁle is veriﬁed using the QKD protocol and com-

pare it to the one in the hub, if the veriﬁcation success,

then the data ﬁle is stored on the local data lake ser-

vice.

Figure 5, presents the complete system design. All

the previous components are added on this ﬁgure. It

can be clearly seen how the data ﬂows throughout the

system. It will go from the user to the cloud hub ASG

to the server on each CSP, and from this server the

calculations are made using the QKD protocol on the

quantum machines and simulators before authorising

the data ﬁles to be stored on the local data lake on

each CSP.

4 IMPLEMENTATION

This section describes the implementation details of

our proposed solution. We called the ﬁrst full version

of this work QLake, as it is the ﬁrst known Quantum

technology based Data Lake.

For this work, the use of GitHub was important to

maintain the code on the cloud, and to get all the ben-

eﬁts from this platform, such as change management,

code history, bug reports, etc. Also, it will provide the

open source code to the different quantum and cloud

developer communities, who can take advantage of it

and extend the purpose and use cases. All the code of

this research is on GitHub (GIT, 2022).

4.1 Cloud Hub VM

The VM used in AWS as the hub node is an Ubuntu

Server 20.04 LTS (HVM) with SSD Volume Type and

AMI ID ami-04505e74c0741db8d (64-bit x86). The

version 20.04 is the latest LTS and this was the rea-

son for the choice. The instance type is m5.large (2

vCPUs and 8 GB memory), which is a general use in-

stance type with standard size. The packages that are

installed on this VM for the data generation phase are:

Python 3.10.2, Anaconda 3, NumPy, SciPy, Numba,

quaternion.

Enabling Quantum Key Distribution on a Multi-Cloud Environment to Secure Distributed Data Lakes

Figure 5: Full system design.

4.2 Multi-Cloud Environment

To implement the solution for this research, the CSPs

with the most mature quantum services will be used.

These CSPs are AWS, IBM Cloud and GCP.

In the implementation of the quantum layer, it

is important to note that a global quantum network

doesn’t exists at the moment due to the lack of ad-

vanced mechanisms. This subject is in experimental

analysis by many researchers and hopefully a global

quantum network will exists very soon. For this rea-

son, the quantum communication between the cloud

hub VM and the quantum machines was done as ex-

pected (directly), but the communication between the

different quantum machines was made by using the

cloud hub VM as intermediary (relayed).

On AWS, the services used are Identity Access

Management (IAM), Virtual Private Cloud (VPC)

from the networking area, Elastic Compute Cloud

(EC2) from the compute area, Simple Storage Service

(S3) from the storage area and Braket from the quan-

tum computing offerings. Additionally, the cloud

database with the test results is an Aurora cluster on

AWS.

For the IBM Cloud, the services used are Virtual

Servers from the compute offerings, IBM Cloud Ob-

ject Storage for the data lake and the IBM Quantum

set (Composer, Lab and Qiskit SDK) from the quan-

tum computing area.

From GCP, the services used are compute engine

virtual machines, object storage and the IonQ Quan-

tum machine provided by their partner IonQ. More-

over, the Sycamore23 was also tested.

The multi-cloud quantum system is a distributed

platform that must be running on all the CSP at the

moment of the data distribution. It is composed by a

Linux server, speciﬁcally with Ubuntu 20.04 LTS, in

each CSP listening on port 22. Each time it receives a

communication request, it creates a client/server pri-

vate connection with SSH and exchange the required

ﬁles with the client. Once the connection is estab-

lished, it starts receiving data packets from the client

and send the data to the data lake passing through a

quantum validation using the simulators or the quan-

tum computer on the local CSP with the QKD proto-

col.

4.3 Quaternions Generation

The component to generate the complex data to be

stored on ﬁles that will be split to test the multi-cloud

distributed data lake was developed with the latest sta-

ble version of the Python programming language at

the moment, which is 3.10.2. This component gener-

ates a number of ﬁles with quaternions represented by

their associated matrices.

There are several modules, package and libraries

to represent quaternions on Python. For example

“quaternion”, “pyquaternion” and “rowan”. In this re-

search, the “quaternion” module is used, as it has been

conﬁrmed, that it has better performance than the oth-

ers (Ramasubramani and Glotzer, 2018), despite not

fully supporting Euler angles.

4.4 Data Distribution

The data distribution component is in charge of dis-

tributing the ﬁles using the different API calls from

each cloud storage data lake. This component is the

CLOSER 2023 - 13th International Conference on Cloud Computing and Services Science

client of the Multi-Cloud Quantum system server.

4.5 QKD Authorisation

The user authorisation component is capable of au-

thorising a ﬁle transmission using QKD protocol to

validate the credentials and accept the user data. This

must be executed from the same node where the dis-

tribution component will run. It takes each local data

ﬁle served from the distribution, then it will get the

ﬁrst 2 quaternion data bytes (16 bits) and transform

them to an initial state on qubits. With this string of

bits, it executes the QKD protocol with both simu-

lators and real quantum machines, depending on the

version used. It takes only 2 bytes (16 bits) due to the

limitations on the current quantum simulators. For the

quantum computers the limitation is lower, with 4 bits

only. Based on this authorisation phase, it decides if

it will send the ﬁle to the data lake or not.

4.5.1 AWS

The AWS QKD component was developed in 3 ver-

sions. One with the Braket local simulator, one with

the cloud simulator and other using real quantum

computers.

The cloud simulator used was the SV1, which is

a universal state vector simulator. For this paper, the

state vector is the best choice, as the experiments did

not include added noise (density matrix or DM1) or

graph needs (tensor network or TN1). Moreover, the

SV1 is proven faster than the DM1 for circuits with

less than 28 qubits.

In the following code snippet, the lines used to

connect to the AWS QC and run the program are

listed, as an example.

. . .

import boto3

from braket.aws import AwsDevice

from braket.circuits import Circuit

. . .

aws_account_id = boto3.client

("sts").get_caller_identity()["Account"]

my_bucket = "amazon-braket-afbfc6532108"

my_prefix = "simulation-output"

s3_folder = (my_bucket, my_prefix)

. . .

# Set the quantum computer as device

device = AwsDevice("

arn:aws:braket:::device/qpu/ionq/ionQdevice")

# run circuit

m_shots = 1

result = device.run(alice_eve_circuit,

shots = m_shots).result()

. . .

4.5.2 IBM

The IBM QKD component was developed in 2 ver-

sions. One with the Qiskit simulator and the other us-

ing real quantum computers. During the initial tests,

some limitations were detected on the available quan-

tum computers, such as:

• Low number of available qubits.

Although the simulator can use up to 32 qubits,

the available qubits on the real quantum comput-

ers (IBMQ) for public is 5. Then, the experiments

were ran using 16 qubits with the simulator and 4

qubits with the IBMQ machine.

• Duration in queue.

During the initial tests, sometimes the duration in

the IBMQ queue was for several hours. Therefore,

the use of the simulator version is more efﬁcient.

For this reason, some of the experiments were ran in

both versions to compare them, but some were only

ran using simulators to use more qubits, which is only

offered by using the Qiskit simulator.

4.5.3 GCP

The GCP QKD component was developed in 2 ver-

sions. One with the Cirq local simulator and other

using real quantum computers.

4.6 Eavesdropping Simulation

The eavesdropping simulation was made using an in-

terception inside the hybrid algorithm. It is the same

code as the execution without interception, but be-

fore the last mile, VM receives the qubits, Eve will

try to extract some information from them. As previ-

ously mentioned, an actual quantum network doesn’t

properly exists at the moment, and for this reason, the

eavesdropping was simulated on the code directly.

4.7 Data Lake

The distributed data lake was created using the CLI

commands for each CSP. The create-bucket command

on AWS, the bucket-create command on IBM and the

mb command on GCP.

4.8 Results Gathering

We developed a program to gather the results. It

runs on the hub machine, which will collect the re-

sults from each block uploaded and store it on a cloud

database. This cloud database is located on AWS and

based on Amazon Aurora.

Enabling Quantum Key Distribution on a Multi-Cloud Environment to Secure Distributed Data Lakes

5 TESTING AND EVALUATION

In this section, we discuss the evaluation of the imple-

mented solution.

5.1 Test Conﬁgurations

The evaluations were executed using the following

conﬁgurations:

• Local Machine.

The local machine was used to generate the data

ﬁles for the experiments. Operating System: ma-

cOS Monterey 12.2.1, Processor: 2.3 GHz Dual-

Core Intel Core i5, Memory: 8 GB 2133 MHz

LPDDR3.

• Cloud Hub VM and CSP Servers.

The cloud hub VM was used as the pivot to dis-

tribute the data ﬁles and as the communication

bridge between the local machine and the other

cloud components. The CSP servers are the ma-

chines that receives the data ﬁles and run the

quantum experiments. Operating System: Ubuntu

Linux 20.04 Focal Fossa, Processor: Intel(R)

Xeon(R) 8259CL, Memory: 8 GB. Note: The

same Ubuntu version and memory size was used

for all the 3 CSP.

• Quantum Computers and Simulators.

The quantum computers and simulators used in

the experiment to run the QKD section of the hy-

brid codes are presented in Table 1.

Table 1: Quantum Computers and Simulators speciﬁca-

tions.

Item Value

AWS QC IonQ Aria device (gate based QPU)

IBM QC IBM Quantum System One

GCP QC Google IonQ and Sycamore23

AWS Simulator SV1 and the Braket local simulator

IBM Simulator IBM Qiskit simulator

GCP Simulator Cirq simulator

• Data Lake.

The data lake is the place in each CSP where the

data resides after the QKD process. AWS Data

Lake: Simple Storage Service (S3), IBM Data

Lake: Cloud Object Storage, GCP Data Lake:

Cloud Storage.

All the duration times on the experiments were col-

lected using the Linux time tool.

5.2 Percentage of Correct

Authorisations

These experiments show what percentage of the ex-

ecutions between Alice and Bob were successful

and what percentage failed due to quantum machine

noise.

As shown in Figure 6, both the percentage of cor-

rectness results were achieved when using the simu-

lators and when using quantum hardware with each

vendor.

Figure 6: Percentage of Correctness: Simulator vs Quantum

Hardware.

The experiment was ran one hundred times with

16 qubits when using simulators and 4 qubits when

using quantum hardware. This should provide less ad-

vantage to the simulators. However, as they are noise

free, they resulted in correct outputs most of the time

(99% as per the experiments). Meanwhile, the results

on hardware were 91% on AWS Braket, 98% on IBM

Q and 92% on GCP.

5.3 Percentage of Detected

Eavesdropping

This experiment shows the percentage of tests where

an external agent exists and tried to measure the data

sent from Alice to Bob, and was detected by the sys-

tem. Sometimes there are corner cases where the

measure from Eve sent to Bob are incorrectly set as

valid because the keys between Alice and Bob co-

incide. This could happen when after Eve measure-

ment, then Alice and Bob exchanged keys still re-

mains the same because different bit and gate com-

bination results in the same key.

According to theory, this could happen with 25%

probability. This is because the probability that Eve

chooses the incorrect basis is 50%, and then when

Bob measures these intercepted photons generated by

Eve, it will also chooses the incorrect basis 50% of

the time. Then, 50%x50%=25%.

CLOSER 2023 - 13th International Conference on Cloud Computing and Services Science

The eavesdropping was detected 91% of the time,

while with quantum hardware, only 86% of the time

was detected, probably due to the noise errors. This

conﬁrms the close results to theory. This is a good

result, as it can be improved using any of the error

correction existing today.

5.4 Data Upload Duration Tests

In this subsection, the whole system is tested upload-

ing the quaternion ﬁles to quantify the speciﬁc time

duration needed to upload a number n of ﬁles to the

different CSP after split the load, authorise the header

using the QKD protocol and send the ﬁle to each CSP

data lake.

In Figure 7, the duration time of the complete sys-

tem for n ﬁles is shown, with n equal to 1, 100, 200,

1.000 and 2.000.

Figure 7: Duration Time of the complete system for n ﬁles.

As can be seen in Figure 7, the total time increases

as the number of ﬁles grows. But it is not a linear

growth, which shows performance in the face of large

number of ﬁles to be uploaded. In addition, it can be

observed that the QKD authorisation remains perfor-

mant even with large number of ﬁles.

5.5 Comparison Between Existing

Distributed Data Lakes and

Multi-Cloud QLake

For this set of experiments, the following existing

multi-cloud alternatives were used to compare both

the performance and the upload duration:

• Dell Apex Multi-Cloud Data Services.

This option was tested with help of the hardware

team on Dell Ireland, because the actual service

request a minimum of 26 TB to be allocated.

The experiments were ran with a demo version of

the service with minimal storage (up to 10 GB).

It works with a distributed data lake using any

combination from AWS, Azure, GCP and Oracle

Cloud. In the experiments, the AWS and GCP

were used. One of the advantages of this prod-

uct is the private connection from Dell to all the

partner CSPs. This improve the data ﬁle upload

speed.

• Dremio Multi-Cloud.

For this experiment, the standard (free) version

was used. The CSP used were AWS and Azure,

as those are more documented on this tool web-

site. Some beneﬁts of using this tool are the tool

is free and also it is easy to use.

• SnowFlake.

Snowﬂake is one of the most used Multi-Cloud

Data Lake. This is a pay-as-you-go service and it

supports AWS, Azure and GCP. For this experi-

ment, AWS and GCP were used.

• Terraform + Apache Spark.

This option prepare an environment using AWS

and GCP as CSPs and Apache Spark to upload

the data.

In all cases only two CSPs were used because of the

limitation in some of the products to support other

CSPs. Therefore, for this section, the tool of this re-

search was modiﬁed to use only two of the CSPs as

well. The chosen CSPs were AWS and GCP. All the

experiments used the same source machine and the

same cloud servers.

In Figure 8, the duration time comparison for

these 4 alternatives and the implemented tool on this

project were shown. The experiment uploaded 100,

200, 1.000 and 2.000 quaternion ﬁles using each prod-

uct/tool.

Figure 8: Comparison between QLake and existing classi-

cal alternatives.

As shown in the ﬁgure, the QLake tool using

quantum simulators was the best with less amount of

ﬁles, however with large number of ﬁles, the Dell so-

lution was the best. This means that the quantum al-

ternative works better than existing alternatives, ex-

cept just one. With some improvements, the quantum

Enabling Quantum Key Distribution on a Multi-Cloud Environment to Secure Distributed Data Lakes

alternative could approach further the duration times

of the best option and reduce it in the future.

5.6 Percentage of Correct Distributed

Calculations

This section shows the results of using the distributed

quantum system to achieve a distributed simple math

calculation. This was an extra experiment that was

thought during the execution of the planned experi-

ments. The idea is to use the built distributed quantum

environment to perform a distributed calculation. In

the experiments, each quantum machine calculated a

sum and then, the results were obtained from the hub

VM and multiplied. Finally, a program checked how

many results were correctly calculated.

Figure 9: Quantum adder circuit.

The experiment executed 100 simple math cal-

culation between two single digit binary number on

the quantum machines (using the adder circuit (Qis,

2022) shown in Figure 9). The results were 98% cor-

rect and 2% incorrect.

These tests conﬁrms that the distributed calcula-

tions using heterogeneous quantum machines from

different CSPs is possible.

6 DISCUSSION AND

CONCLUSIONS

Based on our experimentations, the overall QLake

system is working as expected, and the output of the

program is consistent as planned. The main conclu-

sion of this research is the conﬁrmation of the pos-

sibility to distribute quantum computation between

multiple CSPs. Moreover, the distribution of data ﬁles

on a multi-cloud distributed data lake can be achieved

using quantum security mechanisms. However, with

the current quantum computers there are a good num-

ber of limitations that leads to the current impossibil-

ity to execute big workloads on these. For experimen-

tation, the use of quantum simulators is a good op-

tion, but although they use real quantum procedures

and theory, in the backend it uses classical machines,

which affects the fact of using real quantum technolo-

gies.

Additionally to the main conclusions, there are

other secondary, but no less important, aspects to take

in consideration. As the quantum computing area is

still quite new, the lack of documentation is still an is-

sue for the researchers in the area. Also, some of the

CSP public documentation are outdated or not work-

ing as expected. During the implementation a good

number of feature requests were sent to the different

CSPs for further improvement. Finally, as the quan-

tum computation is a subject in the middle of quan-

tum physics and computer science, this adds a layer

of difﬁculty and need the proper time to read and un-

derstand complex topics of one area and relate them

to the other before start developing over it.

6.1 Future Perspectives

Perhaps the current quantum computers have limita-

tions, but there are extensions that can be developed

over this project. Firstly, the use of quantum com-

puters and data lakes from other CSPs. Also, by

continuously experiment with new quantum machines

that are delivered each year to reach new best upload

times, and once a quantum global network is created,

test the eavesdropping section without a simulation,

but with an external proper measurement.

If more time were available, it would be useful to

complete tests and experiments including error cor-

rection methods to reduce the errors on the results.

Moreover, other quantum gates different than the ones

used on the experiments could be used.

As a result, for testing purposes and to extend the

quantum hybrid and cloud development community,

this project code can be used to study further consid-

erations. Of course, the need of more powerful quan-

tum computers with more qubits would be crucial to

achieve better results, improved performance and new

areas of research. Hopefully, in the near future, these

quantum machines with more compute power will be

available.

REFERENCES

(2022). Github platform - jose lo huang.

https://github.com/artneutro/MTU. Accessed:

2022-03-11.

(2022). Idc forecasts worldwide quantum comput-

ing market to grow to $8.6 billion in 2027.

https://www.idc.com/getdoc.jsp?containerId=prUS48

414121. Accessed: 2022-04-05.

(2022). Qiskit - the atoms of computation.

CLOSER 2023 - 13th International Conference on Cloud Computing and Services Science

https://qiskit.org/textbook/ch-states/atoms-

computation.html. Accessed: 2022-04-12.

Arute (2019). Quantum supremacy using a programmable

superconducting processor. Nature, (574):505–510.

Beckers, A., Tajalli, A., and Sallese, J.-M. (2019). A review

on quantum computing: Qubits, cryogenic electronics

and cryogenic mosfet physics.

Bennett, C. H. and Brassard, G. (2014). Quantum cryp-

tography: Public key distribution and coin tossing.

Theoretical Computer Science, 560:7–11. Theoreti-

cal Aspects of Quantum Cryptography – celebrating

30 years of BB84.

Bloch, F. (1946). Nuclear induction. Phys. Rev., 70:460–

474.

Bugbee, K., Ramachandran, R., Maskey, M., Barciauskas,

A., Kaulfus, A., That, D.-H. T., Virts, K., Markert,

K., and Lynnes, C. (2020). Advancing open sci-

ence through innovative data system solutions: The

joint esa-nasa multi-mission algorithm and analysis

platform (maap)’s data ecosystem. In IGARSS 2020

- 2020 IEEE International Geoscience and Remote

Sensing Symposium, pages 3097–3100.

Chang, W. and Grady, N. (2019). Nist big data interop-

erability framework: Volume 1, deﬁnitions. Special

Publication (NIST SP), National Institute of Standards

and Technology, Gaithersburg.

Diamanti (2016). Practical challenges in quantum key dis-

tribution. Quantum Inf, (2).

Dirac, P. A. M. (1939). A new notation for quantum me-

chanics. Mathematical Proceedings of the Cambridge

Philosophical Society, 35(3):416–418.

Hamilton, W. R. (1844-1850). On quaternions or on a new

system of imaginaries in algebra. Philosophical Mag-

azine. Trinity College Dublin.

Imai, S., Patterson, S., and Varela, C. A. (2015). Cost-

efﬁcient high-performance internet-scale data analyt-

ics over multi-cloud environments. In Proceedings

of the 15th IEEE/ACM International Symposium on

Cluster, Cloud, and Grid Computing, CCGRID ’15,

page 793–796. IEEE Press.

Lakshmi, P. S. and Murali, G. (2017). Comparison of

classical and quantum cryptography using qkd sim-

ulator. In 2017 International Conference on Energy,

Communication, Data Analytics and Soft Computing

(ICECDS), pages 3543–3547.

Murali, G. and Prasad, R. S. (2016). Cloudqkdp: Quan-

tum key distribution protocol for cloud computing. In

2016 International Conference on Information Com-

munication and Embedded Systems (ICICES), pages

1–6.

Murali, G. and Prasad, R. S. (2017). Secured cloud au-

thentication using quantum cryptography. In 2017

International Conference on Energy, Communication,

Data Analytics and Soft Computing (ICECDS), pages

3753–3756.

Pedone, I., Atzeni, A., Canavese, D., and Lioy, A. (2021).

Toward a complete software stack to integrate quan-

tum key distribution in a cloud environment. IEEE

Access, 9:115270–115291.

Ramasubramani, V. and Glotzer, S. C. (2018). rowan: A

python package for working with quaternions. Jour-

nal of Open Source Software, 3(27):787.

Reddy, Y. (2018). Big data security in cloud environ-

ment. In 2018 IEEE 4th International Conference on

Big Data Security on Cloud (BigDataSecurity), IEEE

International Conference on High Performance and

Smart Computing, (HPSC) and IEEE International

Conference on Intelligent Data and Security (IDS),

pages 100–106.

Srivastava, V., Pathak, R. K., Kumar, A., and Prakash, S.

(2020). Using a blend of brassard and benett 84 amp;

elliptic curve digital signature for secure cloud data

communication. In 2020 International Conference on

Electronics and Sustainable Communication Systems

(ICESC), pages 738–743.

Sudhakar Reddy, N., Padmalatha, V. L., and Sujith, A. V.

L. N. (2018). A novel hybrid quantum protocol to

enhance secured dual party computation over cloud

networks. In 2018 IEEE 8th International Advance

Computing Conference (IACC), pages 142–149.

Suwansrikham, P. and She, K. (2018). Asymmetric se-

cure storage scheme for big data on multiple cloud

providers. In 2018 IEEE 4th International Confer-

ence on Big Data Security on Cloud (BigDataSecu-

rity), IEEE International Conference on High Perfor-

mance and Smart Computing, (HPSC) and IEEE In-

ternational Conference on Intelligent Data and Secu-

rity (IDS), pages 121–125.

Thangapandiyan, M., Rubesh Anand, P. M., and Sankaran,

K. S. (2018). Quantum key distribution and cryptog-

raphy mechanisms for cloud data security. In 2018

International Conference on Communication and Sig-

nal Processing (ICCSP), pages 1031–1035.

Wang (2018). 16-qubit ibm universal quantum computer

can be fully entangled. Quantum Inf, (4):46.

Zagan, E. and Danubianu, M. (2021). Cloud data lake: The

new trend of data storage. In 2021 3rd International

Congress on Human-Computer Interaction, Optimiza-

tion and Robotic Applications (HORA), pages 1–4.

Zhang, Y., Wu, F., and Wang, C. (2019). Identity-

based multi-party revocable quantum-resistant signa-

ture with csp. In 2019 5th International Conference

on Big Data Computing and Communications (BIG-

COM), pages 133–141.

Zhao, B., Zha, X., Chen, Z., Shi, R., Wang, D., Peng, T., and

Yan, L. (2020). Performance analysis of quantum key

distribution technology for power business. Applied

Sciences, 10(8).

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