Titles with Abstracts_2023-2024_Block Chain.pdf

ELYSIUMPRO TITLES WITH ABSTRACTS 2023-2024

EPRO_BC_001 A Low-Cost Cross-Border Payment SystemBased on Auditable Cryptocurrency With
Consortium Blockchain: Joint Digital Currency
Due to the involvement of a large number of intermediaries across different time zones in the correspondent
banking process, existing interbank payment systems cannot provide cost-effective cross-border transactions.
They also suffer from lack of transparency and long transaction delays. These issues can be solved by designing
a cryptocurrency in an auditable manner using a permissioned blockchain where a group of authorities can
govern the network. In this article, we propose a low-cost, seamless cross-border payment system based on an
auditable cryptocurrency that enables unspent transaction output-based transactions in a consortium blockchain
network. To manage the blockchain, participating countries execute the energy-efficient proof of authority
consensus algorithm with equal rights. Unlike conventional cryptocurrencies, dynamic decentralized identifiers
(DIDs) are used as transacting addresses so that self-manageable authentication can be performed on-chain
without any interaction with a trusted third party. The identity of transacting parties is known to respective DID
issuers only.
EPRO_BC_002 A Light Blockchain for Behind-the-Meter Peer-to-Peer Energy Transactions in Cyber-
Physical Power Systems
Household-level distributed energy sources, such as rooftop photovoltaic, microturbines, and energy storage,
become important behind-the-meter (BTM) resources. BTM resources can meet all or part of users’ demands.
Establishing a peer-to-peer (P2P) energy trading network among these users will further promote the utilization
of BTM resources. However, such a proposal faces the problem that cyber trading and physical dispatching are
difficult to coordinate. Therefore, this paper proposes the architecture of the behind-the-meter peer-to-peer
(BTM-P2P) energy trading system, including the cyber layer and physical layer. To ensure that users strictly
execute the cyber trading results in the physical layer, a trading mechanism considering credit is designed, and
the user’s credit has an impact on the bidding priority, which in turn urges the user to strictly execute in
subsequent tradings. Then, a light blockchain suitable for energy trading is developed, ensuring that both the
trading results and the actual dispatching results are not tampered with. Database technology is inserted in the
light blockchain to improve efficiency. Finally, a BTM-P2P cyber-physical testbed coupling physical
dispatching with cyber trading is built, providing technical support for the implementation of BTM-P2P.

EPRO_BC_003 Malicious Node Detection Using Machine Learning and Distributed Data Storage
Using Blockchain in WSNs
In the proposed work, blockchain is implemented on the Base Stations (BSs) and Cluster Heads (CHs) to register
the nodes using their credentials and also to tackle various security issues. Moreover, a Machine Learning (ML)
classifier, termed as Histogram Gradient Boost (HGB), is employed on the BSs to classify the nodes as
malicious or legitimate. In case, the node is found to be malicious, its registration is revoked from the network.
Whereas, if a node is found to be legitimate, then its data is stored in an Interplanetary File System (IPFS). IPFS
stores the data in the form of chunks and generates hash for the data, which is then stored in blockchain. In
addition, Verifiable Byzantine Fault Tolerance (VBFT) is used instead of Proof of Work (PoW) to perform
consensus and validate transactions. Also, extensive simulations are performed using the Wireless Sensor
Network (WSN) dataset, referred as WSN-DS. The proposed model is evaluated both on the original dataset
and the balanced dataset. Furthermore, HGB is compared with other existing classifiers, Adaptive Boost
(AdaBoost), Gradient Boost (GB), Linear Discriminant Analysis (LDA), Extreme Gradient Boost (XGB) and
ridge, using different performance metrics like accuracy, precision, recall, micro-F1 score and macro-F1 score.
EPRO_BC_004 Blockchain-Based Secure and Efficient Secret Image Sharing with Outsourcing
Computation inWireless Networks
Secret Image Sharing (SIS) is the technology that shares any given secret image by generating and distributing
n shadow images in the way that any subset of k shadow images can restore the secret image. However, in the
existing SIS schemes, the shadow images will be easily tampered and corrupted during the communication,
which will pose serious security issues. Recently, blockchain has emerged as a promising paradigm in the field
of data communication and information security. To securely communicate and effectively protect the secret
image data in wireless networks, we propose a Blockchain-based Secure and Efficient Secret Image Sharing
(BC-SESIS) scheme with outsourcing computation in wireless networks. In the proposed BC-SESIS scheme,
the shadow images are encrypted and stored in the blockchain to prevent them from being tampered and
corrupted.

EPRO_BC_005 Blockchain Mining with Multiple Selfish Miners
This paper studies a fundamental problem regarding the security of blockchain PoW consensus on how the
existence of multiple misbehaving miners influences the profitability of selfish mining. Each selfish miner
maintains a private chain and makes it public opportunistically for acquiring more rewards incommensurate to
his Hash power. We first establish a general Markov chain model to characterize the state transition of public
and private chains for Basic Selfish Mining (BSM), and derive the stationary profitable threshold of Hash power
in closed form. It reduces from 25% for a single attacker to below 21.48% for two symmetric attackers
theoretically, and further reduces to around 10% with eight symmetric attackers experimentally. We next
explore the profitable threshold when one of the attackers performs strategic mining based on Partially
Observable Markov Decision Process (POMDP) that only half of the attributes pertinent to a mining state are
observable to him. An online algorithm is presented to compute the nearly optimal policy efficiently despite the
large state space and high dimensional belief space.
EPRO_BC_006 Structural Identity Representation Learning forBlockchain-Enabled Metaverse Based
on Complex Network Analysis
The metaverse and its underlying blockchain technology have attracted extensive attention in the past few years.
How to mine, process, and analyze the tremendous data generated by the metaverse systems has posed a number
of challenges. Aiming to address them, we mainly focus on modeling and understanding the blockchain
transaction network from a structural identity perspective, which represents the entire network structure and
reveals the relations among multiple entities. In this article, we analyze three metaverse-related systems: non-
fungible token (NFT), Ethereum (ETH), and Bitcoin (BTC) from the structural-identity perspective. First, we
conduct the complex network analysis of the metaverse network and obtain several new insights (i.e., power-
law degree distribution, disconnection, disassortativity, preferential attachment, and non-rich-club effect).
Secondly, based on such findings, we propose a novel representation learning method named structure-to-vector
with random pace (SVRP) for learning both the latent representation and structural identity of the network.

EPRO_BC_007 Trust-Preserving Mechanism for Blockchain Assisted Mobile Crowdsensing
In today's complex Internet platform, online users need help to protect their online identity. Only sometimes,
websites are very transparent about how user data will be collected, stored and processed by them. Sometimes
Internet entities collect more online user information than required. These entities often share user identity-
related data with third parties without consent. Existing traditional identity schemes need to be improved to stop
and counter new ways of digital identity theft and fraud. Blockchain is a promising technology to strengthen
the preservation of online users' digital identity due to its decentralised nature and robust data security features.
In this paper, we proposed and implemented a generic blockchain- IoT-based self-sovereign identity
management framework called ChainDiscipline. We have demonstrated the framework's oper- ability and
functionality by implementing healthcare and smart home data management-based use cases.
EPRO_BC_008 ChainDiscipline - Towards A Blockchain-IoT-Based Self-Sovereign Identity
Management Framework
In today's complex Internet platform, online users need help to protect their online identity. Only sometimes,
websites are very transparent about how user data will be collected, stored and processed by them. Sometimes
Internet entities collect more online user information than required. These entities often share user identity-
related data with third parties without consent. Existing traditional identity schemes need to be improved to stop
and counter new ways of digital identity theft and fraud. Blockchain is a promising technology to strengthen
the preservation of online users' digital identity due to its decentralised nature and robust data security features.
In this paper, we proposed and implemented a generic blockchain- IoT-based self-sovereign identity
management framework called ChainDiscipline. We have demonstrated the framework's oper- ability and
functionality by implementing healthcare and smart home data management-based use cases.

EPRO_BC_009 Block chain-based Secure Storage and Access Scheme For Electronic Medical Records
in IPFS
Electronic medical records can help people prevent diseases, improve cure rates, provide a significant basis for
medical institutions and pharmaceutical companies, and provide legal evidence for medical negligence and
medical disputes. However, the integrity and security problems of electronic medical data still intractable. In
this paper, based on the ciphertext policy attribute-based encryption system and IPFS storage environment,
combined with blockchain technology, we constructed an attribute-based encryption scheme for secure storage
and efficient sharing of electronic medical records in IPFS storage environment. Our scheme is based on
ciphertext policy attribute encryption, which effectively controls the access of electronic medical data without
affecting efficient retrieval. Meanwhile, we store the encrypted electronic medical data in the decentralized
InterPlanetary File System (IPFS), which not only ensures the security of the storage platform but also solves
the problem of the single point of failure. Besides, we leverage the non-tamperable and traceable nature of
blockchain technology to achieve secure storage and search for medical data. The security proof shows that our
scheme achieves selective security for the choose keyword attacks. Performance analysis and real data set
simulation experiments shows that our scheme is efficient and feasible.
EPRO_BC_010 Privacy-preserving Blockchain based IoT Ecosystem using Attribute-based Encryption
The Internet of Things (IoT) has penetrated deeply into our lives and the number of IoT devices per person is
expected to increase substantially over the next few years. Due to the characteristics of IoT devices (i.e., low
power and low battery), usage of these devices in critical applications requires sophisticated security measures.
Researchers from academia and industry now increasingly exploit the concept of blockchains to achieve security
in IoT applications. The basic idea of the blockchain is that the data generated by users or devices in the past
are verified for correctness and cannot be tampered once it is updated on the blockchain. Even though the
blockchain supports integrity and non-repudiation to some extent, confidentiality and privacy of the data or the
devices are not preserved. The content of the data can be seen by anyone in the network for verification and
mining purposes. In order to address these privacy issues, we propose a new privacy-preserving blockchain
architecture for IoT applications based on attribute-based encryption (ABE) techniques. Security, privacy, and
numerical analyses are presented to validate the proposed model.

EPRO_BC_011 Blockchain technology for cybersecurity applications in the food supply chain
The food supply chain is a complex system responsible for the circulation of food products, and managing it
requires IT infrastructures and technologies that are free of cyber-risk and that are used to connect, build and
share information. Blockchain technology is a distributed ledger that can play an important role in providing
data transparency, trust, immutability, integrity, and traceability to all food supply chain members. The purpose
of this review is to depict a landscape of the scientific literature enriched by an author's keywords analysis to
develop and test blockchain’s capabilities for cyber-risks prevention in international food supply chains. This
paper combines a systematic literature review (SLR) process with the analysis of bibliographic networks.
EPRO_BC_012 A Blockchain-Based Machine Learning Framework for Edge Services in IIoT
Edge services provide an effective and superior means of real-time transmissions and rapid processing of
information in the Industrial Internet of Things (IIoT). However, the continuous increase of the number of smart
devices results in privacy leakage and insufficient model accuracy of edge services. To tackle these challenges,
in this article, we propose a blockchain-based machine learning framework for edge services (BML-ES) in IIoT.
Specifically, we construct novel smart contracts to encourage multiparty participation of edge services to
improve the efficiency of data processing. Moreover, we propose an aggregation strategy to verify and aggregate
model parameters to ensure the accuracy of decision tree models. Finally, based on the SM2 public key
cryptosystem, we protect data security and prevent data privacy leakage in edge services. Theoretical analysis
and simulation experiments indicate that the BML-ES framework is secure, effective, and efficient, and is better
suitable to improve the accuracy of edge services in IIoT.

EPRO_BC_013 A distributed computing framework based on lightweight variance reduction method
to accelerate machine learning training on blockchain
To security support large-scale intelligent applications, distributed machine learning based on blockchain is an
intuitive solution scheme. However, the distributed machine learning is difficult to train due to that the
corresponding optimization solver algorithms converge slowly, which highly demand on computing and
memory resources. To overcome the challenges, we propose a distributed computing framework for L-BFGS
optimization algorithm based on variance reduction method, which is a lightweight, few additional cost and
parallelized scheme for the model training process. To validate the claims, we have conducted several
experiments on multiple classical datasets. Results show that our proposed computing framework can steadily
accelerate the training process of solver in either local mode or distributed mode.
EPRO_BC_014 Efficient Privacy-Preserving Machine Learning for Blockchain Network
A blockchain as a trustworthy and secure decentralized and distributed network has been emerged for many
applications such as in banking, finance, insurance, healthcare and business. Recently, many communities in
blockchain networks want to deploy machine learning models to get meaningful knowledge from
geographically distributed large-scale data owned by each participant. To run a learning model without data
centralization, distributed machine learning (DML) for blockchain networks has been studied. While several
works have been proposed, privacy and security have not been sufficiently addressed, and as we show later,
there are vulnerabilities in the architecture and limitations in terms of efficiency. In this paper, we propose a
privacy-preserving DML model for a permissioned blockchain to resolve the privacy, security, and performance
issues in a systematic way. We develop a differentially private stochastic gradient descent method and an error-
based aggregation rule as core primitives. Our model can treat any type of differentially private learning
algorithm where non-deterministic functions should be defined.

EPRO_BC_015 Evolved PoW: Integrating the Matrix Computation in Machine Learning into
Blockchain Mining
Machine learning is an essential technology providing ubiquitous intelligence in Internet of Things (IoT).
However, the model training in machine learning demands tremendous computing resource, bringing heavy
burden to the IoT devices. Meanwhile, in the Proof-of-Work (PoW)-based blockchains, miners have to devote
large amount of computing resource to compete for generating valid blocks, which is frequently disputed for
tremendous computing resource waste. To address this dilemma, we propose an Evolved-PoW (E-PoW)
consensus that can integrate the matrix computations in machine learning into the process of blockchain mining.
The integrated architecture, the elaborated schemes of transferring matrix computations from machine learning
to blockchain mining, and the reward adjustment scheme to affect the activity of the miners are, respectively,
designed for E-PoW in detail. E-PoW can keep the advantages of PoW in blockchain and simultaneously
salvage the computing power of the miners for the model training in machine learning. We conduct experiments
to verify the availability and effect of E-PoW. The experimental results show that E-PoW can salvage by up to
80% computing power from pure blockchain mining for parallel model training in machine learning.
EPRO_BC_016 Networking Integrated Cloud–Edge–End in IoT: A Blockchain-Assisted Collective Q-
Learning Approach
Recently, the term “Internet of Things” (IoT) has elicited escalating attention. The flexibility, agility, and
ubiquitous accessibility have encouraged the integration between machine learning (ML) with IoT. However,
there are many challenges that present the key inhibitors in moving ML to the public solution, such as
centralized training, poor training efficiency, and heavy computing capabilities requirements. Therefore,
bringing learning intelligence to edge IoT nodes has been spotlighted for some researches. Meanwhile, how to
govern the use of learning results efficiently, reliably, scalably, and safely is hampered by the heterogeneity and
nonconfidence among IoT nodes. In this article, we propose a blockchain-based collective Q-learning (CQL)
approach to address the above issues, where lightweight IoT nodes are used to train parts of learning layers,
then employing blockchain to share learning results in a verifiable and permanent manner. We further improve
the traditional Proof of Work (PoW). Instead of solving a meaningless puzzle, we regard the learning process
in the IoT node as a piece of work.

EPRO_BC_017 Blockchain-based Federated Learning with Secure Aggregation in Trusted Execution
Environment for Internet-of-Things
This article proposes a blockchain-based federated learning (FL) framework with Intel Software Guard
Extension (SGX)-based trusted execution environment (TEE) to securely aggregate local models in Industrial
Internet-of-Things (IIoTs). In FL, local models can be tampered with by attackers. Hence, a global model
generated from the tampered local models can be erroneous. Therefore, the proposed framework leverages a
blockchain network for secure model aggregation. Each blockchain node hosts an SGX-enabled processor that
securely performs the FL-based aggregation tasks to generate a global model. Blockchain nodes can verify the
authenticity of the aggregated model, run a blockchain consensus mechanism to ensure the integrity of the
model, and add it to the distributed ledger for tamper-proof storage. Each cluster can obtain the aggregated
model from the blockchain and verify its integrity before using it. We conducted several experiments with
different CNN models and datasets to evaluate the performance of the proposed framework.
EPRO_BC_018 Blockchain-Based Event Detection and Trust Verification Using Natural Language
Processing and Machine Learning
Information sharing is one of the huge topics in social media platform regarding the daily news related to events
or disasters happens in nature or its human-made. The automatic urgent need identification and sharing posts
and information delivery with a short response are essential tasks in this area. The key goal of this research is
developing a solution for management of disasters and emergency response using social media platforms as a
core component. This process focuses on text analysis techniques to improve the process of authorities in terms
of emergency response and filter the information using the automatically gathered information to support the
relief efforts. Specifically, we used state-of-art Machine Learning (ML), Deep Learning (DL), and Natural
Language Processing (NLP) based on supervised and unsupervised learning using social media datasets to
extract real-time content related to the emergency events to comfort the fast response in a critical situation.
Similarly, the blockchain framework used in this process for trust verification of the detected events and
eliminating the single authority on the system. The main reason of using the integrated system is to improve the
system security and transparency to avoid sharing the wrong information related to an event in social media.

EPRO_BC_019 BCOOL: A Novel Blockchain Congestion Control Architecture Using Dynamic Service
Function Chaining and Machine Learning for Next Generation Vehicular Networks
This paper presents the first, novel, dynamic, resilient, and consistent Blockchain COngestion ContrOL
(BCOOL) system for vehicular networks that fills the gap of trustworthy Blockchain congestion prediction
systems. BCOOL relies on the heterogeneity of Machine Learning, Software-Defined Networks and Network
Function Virtualization that is customized in three hybrid cloud/edge-based On/Offchain smart contract
modules and ruled by an efficient and reliable communication protocol. BCOOL's first novel module aims at
managing message and vehicle trustworthiness using a novel, dynamic and hybrid Blockchain Fog-based
Distributed Trust Contract Strategy (FDTCS). The second novel module accurately and proactively predicts the
occurrence of congestion, ahead of time, using a novel Hybrid On/Off-Chain Multiple Linear Regression
Software-defined Contract Strategy (HOMLRCS). This module presents a virtualization facility layer to the
third novel K-means/Random Forest-based On/Off-Chain Dynamic Service Function Chaining Contract
Strategy (KRF-ODSFCS) that dynamically, securely and proactively predicts VNF placements and their
chaining order in the context of SFCs w.r.t users' dynamic QoS priority demands. BCOOL exhibits a linear
complexity and a strong resilience to failures.
EPRO_BC_020 Machine Learning Enhanced Blockchain Consensus with Transaction Prioritization
for Smart Cities
In the given technology-driven era, smart cities are the next frontier of technology, and these smart cities aim
to improve the quality of people’s lives. In this article, we introduce such future Internet of Things (IoT)-based
smart cities that leverage blockchain technology. Particularly, when there are multiple parties involved,
blockchain helps in improving the security and transparency of the system in an efficient manner. However, if
a current fee-based or first-come–first-serve-based processing is used, emergency events may get delayed and
even threaten people’s lives. Thus, there is a need for transaction prioritization based on the priority of
information and a dynamic block creation mechanism for efficient data recording and faster event response.
Also, our system focuses on the consortium blockchain maintained by a group of members working across
different organizations to provide more efficiency. The leader election procedure in such a consortium
blockchain becomes more important for the transaction prioritization process to take place honestly. Hence, in
our proposed consensus protocol, we deploy a machine-learning (ML) algorithm to achieve efficient leader
election, based on which a novel dynamic block creation algorithm is designed.

EPRO_BC_021 Cyber-Attack on P2P Energy Transaction Between Connected Electric Vehicles: A
False Data Injection Detection Based Machine Learning Model
When cybersecurity is neglected, any network system loses its efficiency, reliability, and resilience. With the
huge integration of the Information, Communication and Technology capabilities, the Connected Electric
Vehicle (CEV) as a transportation form in cities is becoming more and more efficient and able to reply to citizen
and environmental expectations which improve the quality of citizens’ life. However, this CEV technological
improvement increases the CEV vulnerabilities to cyber-attacks resulting to serious risks for citizens. Thus,
they can intensify their negative impact on societies and cause unexpected physical damage and economic
losses. This paper targets the cybersecurity issues for CEVs in parking lots where a peer-to-peer(P2P) energy
transaction system based on blockchain, and smart contract scheme is launched. A False Data Injection Attack
(FDIA) on the electricity price and power signal is proposed and a Machine Learning/SVM classification
protocol is used to detect and extract the right values. Simulation results are conducted to prove the effectiveness
of this proposed model.
EPRO_BC_022 A Decentralized Electricity Trading Framework (DETF) for Connected EVs: A
Blockchain and Machine Learning for Profit Margin Optimization
Connected electric vehicles (CEVs) can help cities to reduce road congestion and increase road safety. With the
technical improvement made to the battery system in terms of capacity and flexibility, CEVs, as mobile power
plants can be an important actor for the electricity markets. Especially, they can trade electricity between each
other when supply stations are full or temporarily not available. In this article, we propose an advanced
decentralized electricity trading framework between CEVs in parking lots based on consortium blockchain,
machine learning, and Game theoretic model. We design a distributed smart contract solution based on a
stochastic bidding process, which helps CEVs to sell and buy electricity with their maximum profitability.
Finally, numerical simulations with MATLAB and Solidity are conducted to prove the effectiveness of our
proposed solution. Also, a comparison with another method in terms of CEVs' profitability improvement and
energy trading management is provided.

EPRO_BC_023 Blockchain-Based Federated Learning for Device Failure Detection in Industrial IoT
Device failure detection is one of most essential problems in Industrial Internet of Things (IIoT). However, in
conventional IIoT device failure detection, client devices need to upload raw data to the central server for model
training, which might lead to disclosure of sensitive business data. Therefore, in this article, to ensure client
data privacy, we propose a blockchain-based federated learning approach for device failure detection in IIoT.
First, we present a platform architecture of blockchain-based federated learning systems for failure detection in
IIoT, which enables verifiable integrity of client data. In the architecture, each client periodically creates a
Merkle tree in which each leaf node represents a client data record, and stores the tree root on a blockchain.
Furthermore, to address the data heterogeneity issue in IIoT failure detection, we propose a novel centroid
distance weighted federated averaging (CDW_FedAvg) algorithm taking into account the distance between
positive class and negative class of each client data set.
EPRO_BC_024 A Blockchain Dynamic Sharding Scheme Based on Hidden Markov Model in
Collaborative IoT
Sharded blockchain offers scalability, decentralization, immutability, and linear improvement, making it a
promising solution for addressing the trust problem in large-scale collaborative IoT. However, a high proportion
of cross-shard transactions (CSTs) can severely limit the performance of decentralized blockchain. Furthermore,
the dynamic assemblage characteristic of collaborative sensing in sharded blockchain is often ignored. To
overcome these limitations, we propose HMMDShard, a dynamic blockchain sharding scheme based on the
hidden Markov model (HMM). HMMDShard leverages fine-grained blockchain sharding and fully embraces
the dynamic assemblage characteristic of IoT collaborative sensing. By integrating the HMM, we achieve
adaptive dynamic incremental updating of blockchain shards, effectively reducing CSTs across all shards. We
conduct a comprehensive analysis of the security issues and properties of HMMDShard, and evaluate its
performance through the implementation of a system prototype. The results demonstrate that HMMDShard
significantly reduces the proportion of CSTs and outperforms other baselines in terms of system throughput and
transaction confirmation latency.

EPRO_BC_025 A Blockchain-based Decentralized, Fair and Authenticated Information Sharing
Scheme in Zero Trust Internet-of-Things
Internet-of-Things (IoT) are increasingly operating in the zero-trust environments where any devices and
systems may be compromised and hence untrusted. In addition, data collected by and sent from IoT devices
may be shared with and processed by edge computing systems, in order to reduce the reliance on centralized
(cloud) servers, leading to further security and privacy issues. To cope with these challenges, this paper proposes
an innovative blockchain-enabled information sharing solution in zero-trust context to guarantee anonymity yet
entity authentication, data privacy yet data trustworthiness, and participant stimulation yet fairness. This new
solution is able to support filtering of fabricated information through smart contracts, effective voting, and
consensus mechanisms, which can prevent unauthenticated participants from sharing garbage information. We
also prove that the proposed solution is secure in the universal composability framework, and further evaluate
its performance over an Ethereum-based blockchain platform to demonstrate its utility.
EPRO_BC_026 SCEI: A Smart-Contract Driven Edge Intelligence Framework for IoT Systems
Federated learning (FL) enables collaborative training of a shared model on edge devices while maintaining
data privacy. FL is effective when dealing with independent and identically distributed (iid) datasets, but
struggles with non-iid datasets. Various personalized approaches have been proposed, but such approaches fail
to handle underlying shifts in data distribution, such as data distribution skew commonly observed in real-world
scenarios (e.g., driver behavior in smart transportation systems changing across time and location).
Additionally, trust concerns among unacquainted devices and security concerns with the centralized aggregator
pose additional challenges. To address these challenges, this paper presents a dynamically optimized personal
deep learning scheme based on blockchain and federated learning. Specifically, the innovative smart contract
implemented in the blockchain allows distributed edge devices to reach a consensus on the optimal weights of
personalized models. Experimental evaluations using multiple models and real-world datasets demonstrate that
the proposed scheme achieves higher accuracy and faster convergence compared to traditional federated and
personalized learning approaches

EPRO_BC_027 Privacy-Preserving Electricity Theft Detection based on Blockchain
In most electricity theft detection schemes, consumers’ power consumption data is directly input into the
detection center. Although it is valid in detecting the theft of consumers, the privacy of all consumers is at risk
unless the detection center is assumed to be trusted. In fact, it is impractical. Moreover, existing schemes may
result in some security problems, such as the collusion attack due to the presence of a trusted third party, and
malicious data tampering caused by the system operator (SO) being attacked. Aiming at the problems above,
we propose a blockchain-based privacy-preserving electricity theft detection scheme without a third party.
Specifically, the proposed scheme uses an improved functional encryption scheme to enable electricity theft
detection and load monitoring while preserving consumers’ privacy; distributed storage of consumers’ data with
blockchain to resolve security problems such as data tampering, etc. Meanwhile, we build a long short-term
memory network (LSTM) model to perform higher accuracy for electricity theft detection. The proposed
scheme is evaluated in a real environment, and the results show that it is more accurate in electricity theft
detection within acceptable communication and computational overhead. Our system analysis demonstrates that
the proposed scheme can resist various security attacks and preserve consumers’ privacy.
EPRO_BC_028 Blockchain-based Federated Learning with SMPC Model Verification Against
Poisoning Attack for Healthcare Systems
Due to the rising awareness of privacy and security in machine learning applications, federated learning (FL)
has received widespread attention and applied to several areas, e.g., intelligence healthcare systems, IoT-based
industries, and smart cities. FL enables clients to train a global model collaboratively without accessing their
local training data. However, the current FL schemes are vulnerable to adversarial attacks. Its architecture makes
detecting and defending against malicious model updates difficult. In addition, most recent studies to detect FL
from malicious updates while maintaining the model’s privacy have not been sufficiently explored. This paper
proposed blockchainbased federated learning with SMPC model verification against poisoning attacks for
healthcare systems. First, we check the machine learning model from the FL participants through an encrypted
inference process and remove the compromised model. Once the participants’ local models have been verified,
the models are sent to the blockchain node to be securely aggregated. We conducted several experiments with
different medical datasets to evaluate our proposed framework.

EPRO_BC_029 Self-Healing Secure Blockchain Framework in Microgrids
Blockchain has recently been depicted as a secure protocol for information exchange in cyber-physical
microgrids. However, it is still found vulnerable to consensus manipulation attacks. These stealth attacks are
often difficult to detect as they use kernel-level access to mask their actions. In this paper, we firstly build a
trusted and secured peer-to-peer network mechanism for physical DC microgrids’ validation of transactions
over Distributed Ledger. Secondly, we leverage from a physics-informed approach for detecting malware-
infected nodes and then recovering from stealth attacks using a self-healing recovery scheme augmented into
the microgrid Blockchain network. This scheme allows compromised nodes to adapt to a reconstructed
trustworthy signal in a multi-hop manner using corresponding measurements from the reliable nodes in the
network. Additionally, recognizing the possible threat of denial-of-service attacks and random time delays
(where information sharing via communication channels is blocked), we also integrate a model-free predictive
controller with the proposed system that can locally reconstruct an expected version of the attacked/delayed
signals.
EPRO_BC_030 A Fast and Secured Vehicle-to-Vehicle Energy Trading Based on Blockchain
Consensus in the Internet of Electric Vehicles
The organization and management of electricity markets worldwide are rapidly evolving, moving towards
decentralized, distributed, and renewable energy-based generation with solutions based on real-time data
exchange. A Vehicle-to-Vehicle (V2V) energy trading has emerged as one of the most promising alternatives
for relieving the load imposed on the traditional grid enabling two individuals to buy and sell energy directly
without intermediaries. However, the Internet of Electric Vehicles (IoE) environment is trustless, and such P2P
energy trading is prone to different kinds of cyber attacks. Blockchain technology has lately been proposed to
implement V2V energy trading to securely and fairly share energy. The consensus mechanism is one of the
most important modules of blockchain applied to the V2V network. It determines the efficiency and security
among untrustworthy EVs of the energy trading blockchain (ETB). Nevertheless, most works on ETB have
currently adopted traditional consensus mechanisms. Due to high computing power and communication
overhead, these consensus algorithms are unsuitable for applications requiring real-time services such as energy
trading.

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