Blockchain for Artiﬁcial Intelligence: An Industry and Literature Survey

Ciprian Paduraru

, Augustin Jianu

and Alin Stefanescu

University of Bucharest, Romania

Certsign, Bucharest, Romania

Keywords:

Blockchain, AI, Frameworks, Integration, Vehicular Networks Communication, 5G/6G Wireless Networks,

Training Algorithms.

Abstract:

The requirements of today’s applications and their users set high demands and expectations. AI is a part of

these and has played an important role recently. However, the credibility of AI methods is controversial in

many cases, as is data security and user privacy. On the other hand, Blockchain is a trending technology

that offers security and privacy as required by many enterprise applications. The presentation will provide an

overview of how AI and Blockchain can be integrated for mutual beneﬁt: (a) using Blockchains to make AI

systems more trustworthy and private data secure, (b) using AI to improve Blockchain related operations and

internal algorithms. The presentation includes examples from the literature, established research in the ﬁeld,

and practical examples from industry.

1 INTRODUCTION

Artiﬁcial intelligence (AI) and Blockchains are two

of the most tending technologies used in various ar-

eas of software (Zhu et al., 2023). While AI is unde-

niably an area that can be used in almost every sec-

tor today, blockchains spread their use through many

applications that require increased security, such as

enterprise applications (Bandara et al., 2021), ﬁnance

(Jain et al., 2021), Internet of Things (IoT) (Sham-

mar et al., 2021), safety of automated vehicles (Alladi

et al., 2020), (Narbayeva et al., 2020), home secu-

rity (Ratkovic, 2022), medical systems (Abd-Alrazaq

et al., 2021), metaverse (Huynh-The et al., 2023), sup-

ply chain management (Dutta et al., 2020), and many

other areas as mentioned in the literature.

A blockchain (Shrimali and Patel, 2022) is, at its

core, an immutable ledger that stores transactions.

The main advantage of this technology is that the

ledger is not stored in a centralized node, but is repli-

cated across a group of peers and kept in sync at

all times. Transactions between users are recorded

and grouped into a linked list of blocks. The dis-

tributed and replicated ledger can store and exchange

data in a cryptographically secure manner. The va-

lidity and security of data operations is ensured by

so-called mining nodes. It has been proven both theo-

retically and empirically (Guo and Yu, 2022) that the

data stored in the blockchain ledger has a high level

of integrity and robustness and is almost impossible

to manipulate. Due to features such as immutability,

decentralization, cryptographic security, veriﬁability,

etc., it has been used by various sectors. It started

with applications for cryptocurrencies and ﬁnancial

applications in general, and then was adopted by sec-

tors such as healthcare, Internet of Things (IoT), sup-

ply chain management, agriculture, etc. Smart con-

tracts are pieces of software that can perform se-

cure, programmed, and well-controlled actions on

the blockchain. There are three main classes of

blockchains: (a) Permissioned or Private blockchain

(Vukoli

c, 2017): the platform can deﬁne and select

participants and their roles. Generally used by indus-

tries and for private personal use. (b) Permissionless

or Public (Bozic et al., 2016) blockchain: typically

open source environments where any user can par-

ticipate, e.g. Bitcoin, (c) Consortium blockchain (Li

et al., 2017): a combination of the previous ones, usu-

ally used by a group of organizations that collaborate

together on common projects or solutions. Each orga-

nization has its own access and rights attributes.

The goal of this paper is to analyze the research

and applications presented in both academia and in-

dustry to ﬁnd concrete examples of the merging of

the two technologies, AI and Blockchains. There are

three main research questions that we aim to answer

in our work:

1. Can AI operations gain more trustworthiness and

better train/query security by using blockchain as

a foundation?

2. What is the friction/interface between merging

AI and Blockchain operations in today’s applica-

712

Paduraru, C., Jianu, A. and Stefanescu, A.

Blockchain for Artiﬁcial Intelligence: An Industry and Literature Survey.

DOI: 10.5220/0012147200003538

In Proceedings of the 18th International Conference on Software Technologies (ICSOFT 2023), pages 712-719

ISBN: 978-989-758-665-1; ISSN: 2184-2833

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

tions.

3. Can the internal processes of blockchain tech-

nologies and frameworks be improved with the

help of AI?

The remainder of the paper is organized as

follows. Section 2 presents already implemented

projects from industry that combine the two men-

tioned technologies. Section 2 presents ongoing re-

search from the literature that answer our research

questions but have not yet been implemented in prac-

tice. However, they provide an isolated and trustwor-

thy evaluation. The ﬁnal section provides discussion,

conclusions, and some identiﬁed research gaps.

2 APPLICATIONS FROM

INDUSTRY THAT COMBINE AI

AND BLOCKCHAINS

Hanover

(Bohr and Memarzadeh, 2020), (Tagde

et al., 2021), is a Microsoft AI technology that uses

artiﬁcial intelligence to analyze existing health data

and make recommendations. The method used is to

process and store data collected from medical records

to identify and suggest possible remedies for patients.

The underlying method ﬁrst analyzes the patient’s

personal health history information and then relates it

to medical research publications. The output consists

of general recommendations, therapies, treatments,

etc. In this application, the blockchain acts as a plat-

form for data storage and management. The patient

data and the information obtained are then crypto-

graphically secured and can be used by distributed

systems for both consumption and storage in a secure

manner.

In the same area, the pharmaceutical industry is

using blockchain and AI to ensure that products are

not counterfeited (Balan et al., 2022). Companies

such as Sanoﬁ, Pﬁzer, and Amgen are reportedly us-

ing a combination of these technologies to test, track,

and ensure the security of their drugs’ production

chain from the research and development phase to

market. Each drug is assigned a serial number, and

any change or movement of that number is recorded

in the general ledger. In this way, each unique serial

number becomes fully traceable.

In agriculture, blockchain and AI are reported to

be used for supply chain and decision-making system,

as is the case with Heifer International

. The system

https://www.microsoft.com/en-us/research/project/

project-hanover

https://www.heifer.org/

behind this combines the predictive power of AI with

data collected from geospatial, weather, environmen-

tal, and sensors connected as an Internet of Things

(IoT) ecosystem. The AI systems are then able to

provide future weather information, optimal cropping

patterns by area, prices in different markets and re-

gions, etc. The blockchain is able to collect and store

the data in a decentralized manner, provide proof of

origin for the produce, and ensure data security.

Financial services such as banking (Cucari et al.,

2022) and cryptocurrency tracking applications in

general (Yadav et al., 2022) were the ﬁrst propo-

nents of blockchain technology to ensure the secu-

rity of their operations. In addition, the link between

AI and blockchains is also being reported by invest-

ment and trading companies such as Webull: Invest-

ing and Trading

, and Robinhood: Commision-free

Stock Trading & Investing

. In this area, the security

of data, identities, and transactions is provided by the

blockchain. On the other hand, with the development

of AI methods based on blockchain technology, there

is greater conﬁdence in the resulting automated pro-

cesses, assessments, and recommendation engines. In

this area, it is important to know that both data and

AI models are stored on the Blockchain to ensure the

security and traceability of the processes.

Another interesting use case of AI and blockchain

working together is reported by IPwe

. The com-

bination of these two technologies has helped them

build an automated, transparent global patent registry

(GPR). The purpose of this registry is to remove bar-

riers to the collection, understanding, trading, and

management of intellectual property (IP). Blockchain

helps in two ways: (a) for records, data storage and

smart contracts based automation services over the

underlying IP assets, (b) to increase trust in the auto-

mated processes, assessments, and recommendations

of AI systems.

3 RESEARCH AND FUTURE

DIRECTIONS FROM SCIENCE

This section presents several suggestions and research

from the literature that have been empirically tested

but not yet applied in practice. We believe these are

also important as they may suggest future use cases

and attract industry partners and founders.

https://www.webull.com/

https://robinhood.com/us/en/

https://ipwe.com/

Blockchain for Artiﬁcial Intelligence: An Industry and Literature Survey

713

3.1 An Architecture for the

Convergence of AI and Blockchain

In the work of (Muheidat and Tawalbeh, 2021) au-

thors deﬁne an architecture that combines AI and

Blockchain operations along with stakeholder con-

tributions and roles. A sketch of their proposals is

shown in Fig. 1. The proposed architecture consists

of three layers. In the Contributors Layer, stakehold-

ers can collaborate and share datasets and AI mod-

els, and provide validation, analysis, and prediction

tools. A concrete example from the medical domain

might be that the stakeholders are a consortium of

different hospitals. The different facilities can then

add their data and records on patients and treatment

correlations, which can be further used by predictive

models to discover similarities and patterns for better

decisions, research and recommendations in the fu-

ture. The second layer, Blockchain layer, is a service-

based layer that handles data storage, ledger manage-

ment and transaction management, communication,

connections between miners, role management, en-

cryption of data, etc. These functions typically associ-

ated with the blockchain are encapsulated (hidden) in

this layer to ensure separation of concerns from other

stakeholders. The third layer, User layer, contains

the users who are allowed to interact with the system.

Following the example from the medical domain, the

stakeholders in this case can be physicians, patients,

and others who can access the datasets, models, and

AI-based tools provided by the system.

3.2 Autonomous Vehicles Safety

Recent trends show a growing interest for Vehicular

ad-hoc networks (VANET) and Vehicular Social Net-

works (VSN) (Lee and Atkison, 2021), (Azam et al.,

2021). The main goal of vehicular communication

systems is to enable peer-to-peer communication be-

tween vehicles, connect them to smart cities and sen-

sors, and then use AI-based predictive algorithms to

reduce trafﬁc congestion and increase driver safety

(Figure 2).

However, this growing interest in vehicular com-

munication systems has raised several potential at-

tack methods aimed at modifying the content sent

between vehicles or from the smart city to the ve-

hicles in a way that disrupts predictive capabilities.

In this sense, recent literature has made an interest-

ing progress in this ﬁeld, as shown by the works of

(Bendiab et al., 2023), (Hammoud et al., 2020) and

(Pokhrel and Choi, 2020). The solutions used by

the authors generally combine AI prediction capabil-

ities and algorithms with blockchains to secure intel-

Figure 1: A foundation architectural proposal for the con-

vergence of AI and blockchain solutions based on the work

presented by (Muheidat and Tawalbeh, 2021).

Figure 2: An illustration of how vehicles communicate

within a smart city (V2X). They can communicate either

directly, peer to peer (P2P), or with the urban infrastructure

through the Road Side Units (RSU). All the data coming

from the vehicles and the sensors installed in the city (cam-

eras, trafﬁc lights, etc.) can be stored in the cloud, processed

and used by the authorized users.

ligent automated vehicles (AVs). Secure messaging

that combines these two technologies is also proposed

in the work of (Malik et al., 2020).

Various types of attacks can occur in these sce-

narios, and a comprehensive list can be found in the

work of (Azam et al., 2021). Common examples can

be seen in Fig. 3. Data manipulation-based attacks,

where the attacker gains authorized access to AVs to

compromise data integrity and violate their privacy,

are also very common in practice. Each AV is identi-

ﬁed with a unique identiﬁer that can help identify AV

ICSOFT 2023 - 18th International Conference on Software Technologies

714

and the ﬂow of messages sent, but false identities can

be created by attackers to inﬂuence the prediction re-

sults of the AI-based method (Bendiab et al., 2023),

(Pokhrel and Choi, 2020).

From the existing research on these AV commu-

nication problems, we conclude that the intersection

of blockchain and AI technologies can offer two main

features:

• The ability to protect users’ private data from cy-

berattacks.

• Decentralized access to data as it might be needed

for the efﬁciency of AI systems.

An architecture in this sense is presented in (Ham-

moud et al., 2020), and outlined in Fig. 4. Vehi-

cles can communicate directly or through a master

node. Road data and sensors are generated and ag-

gregated by the nearest RSUs (data generation layer).

Finally, all communication at this layer is stored, pro-

cessed, and validated by the nearest cluster where

a blockchain ledger infrastructure is deployed (edge

layer). This data is passed to the cloud layer, where

predictive AI models are stored and continuously

trained with new data. These models are sent back

through the hierarchy to the data generation layer so

that vehicles, trafﬁc lights, and other entities can work

together to reduce trafﬁc congestion and ensure road

safety.

As mentioned in the literature review, for the con-

sortium of blockchain architecture deployed in the

edge layer it is recommended to use the Byzantine

Fault Tolerance protocol (Li et al., 2021) for consen-

sus. The smart contracts that operate between the two

lower layers are classiﬁed into three categories:

• Participant Authentication.

• Data Storing: collecting and storing data.

• AI models management between entities.

Training Automated Vehicles. In the same area, we

identiﬁed the work in (Gandhi and Salvi, 2019) dis-

cussing a possible method for safely training auto-

mated vehicles. As noted in the paper, autonomous

vehicles typically learn to drive using a variety of

reinforcement learning methods. Vehicles could be

connected to a shared public ledger to collect and

share experiences (e.g., pairs of observations, re-

wards, and/or actions performed by a human expert)

in a safe and reliable manner.

The conclusion of this study on vehicle communi-

cation is that the use of a blockchain layer would bring

two main beneﬁts to the AI systems built on top of it:

(a) increasing the trust and reliability of the collected

experience data, (b) better explaining the decisions of

the AI algorithms, since the data can be stored and

tracked in the blockchain.

3.3 5G/6G Wireless Networks and IoT

The architectural proposals presented in the previ-

ous section are continued in the work of (Li et al.,

2020), (Dai et al., 2019), which further extends the

concepts, architecture, and algorithms to combine AI

and blockchain in the context of 5G and future 6G

networks. The key foundation of their work is the ob-

servations that: (a) Blockchain is capable of provid-

ing a secure and decentralized resource sharing envi-

ronment for different participating entities, (b) AI can

solve speciﬁc problems in this domain involving un-

certainty and time-varying characteristics, (c) ﬁnally,

the integration of both can improve the performance

of wireless networks.

An important architectural change from the pre-

vious one, shown in Fig. 4, is the caching mech-

anism, where the edge layer servers not only pro-

vide AI-based distributed intelligent wireless com-

putation and routing, but also provide a caching

mechanism to store computationally intensive and

delay-sensitive applications (e.g., emergency situa-

tions, news, weather reports, etc.). The motivation for

the caching mechanism is that the content generated

by sensors, multimedia applications, trafﬁc lights,

etc., grows exponentially and challenges the transmis-

sion and processing capacity of the networks. In this

sense, two roles are assigned: (a) a caching requester

- e.g., a personal mobile device that needs to be no-

tiﬁed from time to time in order for the owner to in-

teract with a smart city environment, (b) a caching

provider - a device capable of processing caching re-

sults or even receiving computing power. The edge

layer servers have an AI algorithm (based on rein-

forcement learning) that is able to predict the com-

munication patterns between the cache requestors and

providers to optimize the overall process.

The blockchain is tasked with recording and val-

idating all transactions generated in the wireless net-

work, which in turn can enhance the security and pri-

vacy of the wireless ecosystem. As with the other

examples shown previously, it also enables trustwor-

thiness in AI data mining processes and decisions.

For example, in the caching mechanisms presented

above, each caching request or response generates

a transaction that is then written to the blockchain.

The blockchain receives these transactions and uses

them to create blocks that are stored in the immutable

ledger data structure. This would also allow providers

to securely market different services in a smart city.

Indoor Positioning With 5/6G Wireless Networks.

An interesting application of wireless networking is

indoor positioning and pathﬁnding, such as a person

in a shopping mall. Ideally, you would like to see

Blockchain for Artiﬁcial Intelligence: An Industry and Literature Survey

715

Figure 3: Common attacks in V2X scenarios, where the black vehicle is the attacker while the victims are colored green.

The left ﬁgure shows a Denial of Service attack (DoS), where the attacker blocks wireless communications through message

ﬂooding and radio jamming. In the middle ﬁgure, a fake identity is created to send fake messages (Sybil attack). The ﬁgure

on the right contains an example of creating fake entities such as vehicles, cameras, or pedestrians to send false or disrupted

information to the victim.

Figure 4: A vehicle communication network as a typical

IoT system, with three layers: the data generation, edge and

cloud layers.

where you are on the map of the mall and how to

get to speciﬁc locations inside (Wang et al., 2020).

There are many known algorithms for indoor posi-

tioning and pathﬁnding using AI methods. However,

in general, AI methods operate in a centralized man-

ner, while in this case the required operations are de-

centralized. The main research question in this con-

text is how a system can solve the queries from differ-

ent users in a safe and decentralized world.

The chosen solution is to integrate AI and

blockchains. There are two roles in this system:

(a) The requester: a set of people who request lo-

cation information, e.g., by sending locations with

their phones and trying to ﬁnd speciﬁc areas, (b) The

worker: can provide localization and pathﬁnding ser-

vices to requesters. An important note is that re-

questers should be able to provide feedback on the

services they receive.

The key principles in the implementations are as

follows:

• Deﬁne requests for a set of workers that can per-

form localization and pathﬁnding services.

• For each request, have an algorithm that selects a

set of trusted workers to perform the operations.

• Use the blockchain to prevent malicious attackers

(e.g., workers who try to create false identities or

provide false information).

As shown in the architecture of the system in Fig.

5, the blockchain platform works as an intermediate

layer between requesters and workers. It ﬁrst solves

the problem of decentralized data storage. Then,

both entities can agree through transactions on the

blockchain, which are added to blocks and stored in

the ledger for traceability and auditability. The trusted

authority (TA) designed for the blockchain platform is

responsible for authorizing and validating the regis-

tration identities of participants without compromis-

ing privacy. This component would then allow the

Blockchain to store data and transactions in a secure

manner. The Fog server in this architecture, is play-

ing the role of the computational node for blockchain

operations such as consensus algorithms (i.e., it man-

ages the group of miners), process feedbacks, and se-

lects trusted workers based on their current reputation

value (AI can help in this direction through regression

or recommendation systems).

Figure 5: An architectural system of blockchain and AI

working together to provide indoor localization and navi-

gation services.

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3.4 Using AI to Optimize Internal

Blockchain Operations

As mentioned in Section 4, we believe that there

are important research opportunities to leverage AI

in the internal algorithms and mechanisms of the

Blockchain. At the moment, we have noted two

works in this area. In (Wang et al., 2021), the au-

thors use reinforcement learning (RL) methods to op-

timize the mining strategy for a particular Bitcoin-

like blockchain. The mining problem is modeled as

a Markov decision process (MDP) and the RL agent

trains on it. However, blockchain networks and pa-

rameters can change rapidly over time, which in gen-

eral breaks the assumption that an MDP can be de-

ﬁned. The agent would then use policies learned

based on incorrect/old data. The work in (Chen et al.,

2018) proposes a neural network to select a set of

nodes to participate in the consensus mechanism for

each query. However, the proposal has not yet been

formally proven or demonstrated by robust empirical

experiments.

4 DISCUSSION AND

CONCLUSIONS

AI algorithms and methods must use data or infor-

mation to learn, interpret, and then draw conclusions.

When this data comes from secure and trusted chan-

nels, machine learning algorithms can perform better

and present their decisions with more credibility. This

result is achieved by using blockchains as the under-

lying technology for data storage and management,

which creates a secure, immutable, and decentralized

system needed to improve AI methods that typically

need to collect, store, and use sensitive data (IBM,

2023). Without blockchain support, it will be difﬁ-

cult to trust the data and decisions that come from AI-

powered methods. Generally speaking, from a high-

level architecture perspective, by building AI methods

on top of blockchain technologies architecturally, the

design and speciﬁcation are moving from the tradition

ones, as shown in Figure 6, to the speciﬁcation given

in Figure 7.

Smart contracts can enable both automated data

retrieval and analysis using machine learning algo-

rithms. The security of the underlying data and min-

ers makes the results of their operations highly trust-

worthy.

As shown in the wireless network and vehicular

communication studies in Section 2, the integration

of the two technologies can enable intelligent decen-

tralized autonomous agents (DAOs) to validate data

Figure 6: The traditional, centralized way of using AI meth-

ods for training, queries, and user interactions.

Figure 7: Combining AI and Blockchain technologies at

a high level, in a departure from the traditional methods,

as shown in Figure 6. The potential beneﬁts would be de-

centralized AI services, more data security, trustworthiness,

easier explanation and traceability of the results of AI meth-

ods.

and asset transfers, as well as AI-based predictive ca-

pabilities in IoT environments.

In this sense, we believe it may be a matter of time

before the current research is applied in practice to

obtain a better and more secure communication envi-

ronment between people, smart cities, and vendors in

general. In the gaps and research opportunities iden-

tiﬁed, we found that there is a lack of AI methods be-

ing used to improve operations within the Blockchain.

We believe AI can also be used for optimized con-

sensus mechanisms, predictive methods for selecting

miners, and other internal entities involved in lower-

level blockchain operations.

ACKNOWLEDGEMENTS

This research was supported by the European Re-

gional Development Fund, Competitiveness Oper-

ational Program 2014-2020 through project IDBC

(code SMIS 2014+: 121512).

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717

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