Blockchain Papers

Shahla Atapoor, Karim Baghery, Daniele Cozzo, Robi Pedersen

No abstract is available for this record.

Muhammad Firdaus, Harashta Tatimma Larasati, Kyung-Hyune Rhee

The enormous volume of heterogeneous data from various smart device-based applications has growingly increased a deeply interlaced cyber-physical system. In order to deliver smart cloud services that require low latency with strong computational processing capabilities, the Edge Intelligence System (EIS) idea is now being employed, which takes advantage of Artificial Intelligence (AI) and Edge Computing Technology (ECT). Thus, EIS presents a potential approach to enforcing future Intelligent Transportation Systems (ITS), particularly within a context of a Vehicular Network (VNets). However, the current EIS framework meets some issues and is conceivably vulnerable to multiple adversarial attacks because the central aggregator server handles the entire system orchestration. Hence, this paper introduces the concept of distributed edge intelligence, combining the advantages of Federated Learning (FL), Differential Privacy (DP), and blockchain to address the issues raised earlier. By performing decentralized data management and storing transactions in immutable distributed ledger networks, the blockchain-assisted FL method improves user privacy and boosts traffic prediction accuracy. Additionally, DP is utilized in defending the user’s private data from various threats and is given the authority to bolster the confidentiality of data-sharing transactions. Our model has been deployed in two strategies: First, DP-based FL to strengthen user privacy by masking the intermediate data during model uploading. Second, blockchain-based FL to effectively construct secure and decentralized traffic management in vehicular networks. The simulation results demonstrated that our framework yields several benefits for VNets privacy protection by forming a distributed EIS with privacy budget (ε) of 4.03, 1.18, and 0.522, achieving model accuracy of 95.8%, 93.78%, and 89.31%, respectively.

Dan Mitrea, Liana Toderean, Tudor Cioara, Ionuț Anghel · 5 authors

Blockchain technology offers great value in terms of decentralization, data integrity, transparency, and traceability, however the transactional data is public, and accessible raising concerns about violating privacy regulations. For example, in the peer-to-peer energy trading and demand response use cases, the data stored in blockchain may allow a third party to infer the load profiles or even identify the behind the meter assets. In this paper, we employ homomorphic techniques to encrypt the energy transactional data stored on the blockchain allowing the smart contracts functions responsible for implementing the business logic of the energy flexibility trading and settlement to perform computations on encrypted data. As computations on smart contracts and public blockchains can be expensive, we have used the lighter version of the Partial Homomorphic Encryption scheme to obfuscate the energy data. To ensure the validity of the smart contracts' functions executed on encrypted data, we leverage on the consensus mechanism of the blockchain network, thus ensuring computation correctness. The solution was validated considering a micro-grid with 12 prosumers that trade their flexibility peer-to-peer (P2P). The results demonstrate the feasibility of maintaining encrypted energy data on the blockchain, executing smart contract functions on encrypted data, and preserving the privacy of computations. As anticipated, the trade-off for better privacy is the gas consumption overhead of the smart contracts' functions which is higher compared to the non-encrypted case, depending on the length of the public-private keys pair. Nonetheless, our solution exhibits consistent execution times for smart contracts, making it suitable for private networks where gas costs are of minimal concern.

Jiewei Feng

Blockchain and other decentralized databases, known as distributed ledgers, are designed to store information online where all trusted network members can update the data with transparency. The dynamics of ledger's development can be mathematically represented by a directed acyclic graph (DAG). In the first part of the thesis, we propose a random DAG model with sequential stochastic arrivals that mimic attachment rules from the IOTA cryptocurrency and study its asymptotic behavior as time goes to infinity. Our analysis establishes that the DAG is almost surely one-ended which is a crucial indicator of security of a decentralized database. In the second part of the paper, we study a modified DAG model and analyze its property as the arrival rate goes to infinity and the inter arrival time goes to zero. We establish that the number of leaves in the DAG and various random variables characterizing the vertices in the DAG can be approximated by its fluid limit, represented as delayed partial differential equations. Furthermore, we establish the stable state of this fluid limit and validate our findings through simulations.--Author's abstract

Feifei Guo, Guohua Shen, Zhiqiu Huang, Yang Yang · 6 authors

With the advent of IoT technology, the dynamic nature of IoT devices has introduced new obstacles to access control. It is essential to consider the security requirements of the actual physical environment, rendering the traditional access control approach centered on the information space. In the IoT ecosystem, there are several issues such as the dynamics of devices frequently entering and leaving, the lack of computing and storage capacity, and distributed deployment. To address these challenges, this paper proposes the Domain Attribute Based Access Control(DABAC) that incorporates domain elements to implement the physical location limitation of dynamic devices. Moreover, an intelligent gateway is utilized to divide the physical area and act as a proxy to achieve regional device management, automatic networking of devices in the domain, and the dynamic expansion of the sensor network resulting from device entry or exit. Then, given the distributed deployment of devices, smart contracts are employed to deploy access control mechanisms and construct a trusted environment to mitigate threats such as single points of failure. Finally, the DABAC is implemented on the Ethereum platform, simulating a smart medical situation. The experimental results demonstrate that the proposed solution effectively addresses the problem of access control of device dynamics in an untrusted IoT environment while maintaining system security.

Muhammad Firdaus, Siwan Noh, Zhuohao Qian, Harashta Tatimma Larasati · 5 authors

Federated learning (FL) is a distributed machine learning technique that allows multiple devices (e.g., smartphones and IoT devices) to collaborate in the training of a shared model with each device preserving the privacy of its local data. However, the highly heterogeneous distribution of data among clients in FL can result in poor convergence. In addressing this issue, the concept of personalized federated learning (PFL) has emerged. PFL aims to tackle the effects of non-independent and identically distributed data and statistical heterogeneity and to achieve personalized models with rapid model convergence. One approach is clustering-based PFL, which utilizes group-level client relationships to achieve personalization. However, this method still relies on a centralized approach, whereby the server coordinates all processes. To address these shortcomings, this study introduces a blockchain-enabled distributed edge cluster for PFL (BPFL) that combines the benefits of blockchain and edge computing. Blockchain technology can be used to enhance client privacy and security by recording transactions on immutable distributed ledger networks, thereby improving client selection and clustering. The edge computing system offers reliable storage and computation such that computational processing is locally performed in the edge infrastructure to be closer to clients. Thus, the real-time services and low-latency communication of PFL are improved. However, further work is required to develop a representative dataset for the examination of related types of attacks and defenses for a robust BPFL protocol.

AoXuan Li, Gabriele D’Angelo, Su-Kit Tang

No abstract is available for this record.

A. Sasikumar, Logesh Ravi, Malathi Devarajan, V. Subramaniyaswamy · 7 authors

The expansion of Internet of Things (IoT) devices and their integration into a variety of vital sectors has created serious concerns regarding data protection, privacy, and resource management. As a promising model, edge computing has the ability to overcome these difficulties by putting the computing power closer to IoT devices. This article presents a novel approach for decentralized resource allocation in edge computing settings, with the goal of improving the security and efficiency of IoT systems. Edge nodes are critical in our proposed framework for managing and assigning computing resources to IoT devices, minimizing latency, and optimizing network traffic. The decentralization of resource distribution promotes resilience in the event of network outages or cyberattacks and provides robustness against single points of failure. We created a proof-of-importance (PoI) consensus mechanism for creating new blocks in the blockchain integrated edge-computing IoT devices. Therefore, the consensus mechanism will ensure the trust and security of IoT devices by authentication of each user in the network. We performed a series of experiments in a simulated edge-computing setting to assess the feasibility of our proposed method. We analyze the proposed system model based on the operation of three different file delivery and transactions. The simulation outcomes show that the blockchain system efficiently delivers the files and increases the transmission rate. We also compared our file delivery and transmission rate with existing techniques, and our proposed model provides a better result. Finally, we compared the power consumption of creating IoT nodes based on proof-of-work (PoW), proof-of-stake (PoS), and PoI. The proposed PoI consensus mechanism consumes less power than the other two methods.

Siqi He, Xiaofei Xing, Guojun Wang, Zeyu Sun

Currently, many companies and institutions use centralized or distributed databases to store massive amounts of data. However, the use of untrusted centralized third-party auditors can result in security issues because these auditors may be malicious and tamper with or delete user data. This poses a significant challenge for ensuring the reliability of the data verification results. Although introducing a third-party auditor can help address this issue, it may also be untrustworthy and collude with the database service provider to forge false data verification results. In this study, we propose a data integrity verification scheme using smart contracts (DIV-SC) to address this challenge in a centralized database environment. Our approach utilizes blockchain technology as a decentralized third-party auditor, ensuring that the information stored on the blockchain is immutable and cannot be tampered with maliciously. In addition, smart contracts deployed on the blockchain can ensure that the verification procedures are correct and are not affected by any malicious party. We also leverage game theory to improve the reliability of multiple verifications, reduce verification time and improve overall performance. Our proposed scheme reduces the total verification time consumption by up to 53.44% while increasing the number of verifiable times by nearly 3.8 times, compared to conventional data integrity verification schemes.

Theodoros Constantinides, John Cartlidge

We present a general purpose, privacy-preserving framework for verifying user attributes. The framework is designed for users (e.g., a job candidate) to allow a challenger (e.g., a prospective employer) to verify whether the usermeets a particular requirement (e.g., does the candidate hold a valid driving license?), without leaking any other information about the user. Importantly, the user is an active part of the challenge-verification process, which ensures that challenges cannot be made without the user’s full knowledge and participation. The framework is decentralized and requires a public blockchain. A smart contract is used to manage the challenge-verification process, and zero-knowledge proofs are used to verify challenges in a privacy-preserving manner. We implement a simplified version of the framework using smart contracts deployed on the Ethereum blockchain, and we simulate some simple use cases. All simulation code is available open-source (https://github.com/lifeisbeer/BlockVerify).

Umer Majeed, Latif U. Khan, Sheikh Salman Hassan, Zhu Han · 5 authors

Federated learning (FL) is an on-device distributed learning scheme that does not require training devices to transfer their data to a centralized facility. The goal of federated learning is to learn a global model over several iterations. It is challenging to claim ownership rights and commercialize the global model efficiently and transparently. Additionally, incentives need to be provided to ensure that devices participate in the FL process. In this paper, we propose a smart contract-based framework called FL-Incentivizer, which relies on custom smart contracts to maintain flow governance of the FL process in a transparent and immutable manner. FL-Incentivizer commercializes and tokenizes the global model using FL-NFT (FL Non-Fungible Token) based on the ERC-721 standard. FL-Incentivizer uses ERC-20 compliant FL-Tokens to incentivize devices participating in FL. We present the system design and operational sequence of the FL-Incentivizer. We provide implementation and deployment details, complete smart contract codes, and qualitative evaluation of the FL-Incentivizer. After implementing FL-Incentivizer for a global iteration of a Federated learning task, we showed the FL-NFT on OpenSea and an FL-Token for a learner on MetaMask. FL-NFTs can be traded on markets such as OpenSea like other NFTs. While FL-Tokens can be transferred in the same manner as other ERC-20-based tokens.

Carmit Hazay, Muthuramakrishnan Venkitasubramaniam, Mor Weiss

Abstract Distributed zero-knowledge (dZK) proofs, recently introduced by Boneh et al. (CRYPTO‘19), allow a prover $$\mathcal{P}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>P</mml:mi> </mml:math> to prove NP statements on an input x , which is distributed between k verifiers $$\mathcal{V}_1,\ldots ,\mathcal{V}_k$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msub> <mml:mi>V</mml:mi> <mml:mn>1</mml:mn> </mml:msub> <mml:mo>,</mml:mo> <mml:mo>…</mml:mo> <mml:mo>,</mml:mo> <mml:msub> <mml:mi>V</mml:mi> <mml:mi>k</mml:mi> </mml:msub> </mml:mrow> </mml:math> , where each $$\mathcal{V}_i$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>V</mml:mi> <mml:mi>i</mml:mi> </mml:msub> </mml:math> holds only a piece of x . As in standard ZK proofs, dZK proofs guarantee Completeness when all parties are honest; Soundness against a malicious prover colluding with t verifiers; and Zero Knowledge against a subset of t malicious verifiers, in the sense that they learn nothing about the NP witness and the input pieces of the honest verifiers. Unfortunately, dZK proofs provide no correctness guarantee for an honest prover against a subset of maliciously corrupted verifiers. In particular, such verifiers might be able to “frame” the prover, causing honest verifiers to reject a true claim. This is a significant limitation, since such scenarios arise naturally in dZK applications, e.g., for proving honest behavior, and such attacks are indeed possible in existing dZKs (Boneh et al., CRYPTO‘19). We put forth and study the notion of strong completeness for dZKs, guaranteeing that true claims are accepted even when t verifiers are maliciously corrupted. We then design strongly-complete dZK proofs in the honest-majority setting using the “MPC-in-the-head” paradigm of Ishai et al. (STOC‘07), providing a novel analysis that exploits the unique properties of the distributed setting. To demonstrate the usefulness of strong completeness, we present several applications in which it is instrumental in obtaining security. First, we construct a certifiable version of Verifiable Secret Sharing (VSS), which is a VSS in which the dealer additionally proves that the shared secret satisfies a given NP relation. Our construction withstands a constant fraction of corruptions, whereas a previous construction of Ishai et al. (TCC‘14) required $$k={\textsf{poly}}\left( t\right) $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>k</mml:mi> <mml:mo>=</mml:mo> <mml:mi>poly</mml:mi> <mml:mfenced> <mml:mi>t</mml:mi> </mml:mfenced> </mml:mrow> </mml:math> . We also design a reusable version of certifiable VSS that we introduce, in which the dealer can prove an unlimited number of predicates on the same shared secret. Finally, we extend a compiler of Boneh et al. (CRYPTO‘19), who used dZKs to transform a class of “natural” semi-honest protocols in the honest-majority setting into maliciously secure ones with abort. Our compiler uses strongly-complete dZKs to obtain identifiable abort.

R. Fernández

This paper delves into two legal models for zero-knowledge proof protocols in the context of the eIDAS 2.0 Regulation: a trust service or a software product. The ARIES: reliAble euRopean Identity EcoSystem EU project highlighted the need for a legal framework for stakeholders to accept proof of the existence of user data with legal certainty, while Hyperledger Indy shows that ZKP solutions are currently commercialized, stressing deficiencies in the eIDAS 2.0. An overview of ZKP applied to identity, its relationship to the European Digital Identity Wallet and the electronic attestations of attributes, both introduced by the eIDAS 2.0, and Self-Sovereign Identity systems, leads to the central question of proof of the existence of user-held data as a trust service or as a software product and its data privacy implications for each approach. Finally, we outline a possible solution based on the product approach for future work. Our findings reveal that ZKP technology must have legal value and a presumption system to be effective. However, the path we take could lead us either to develop a system of surveillance and control in electronic environments or to build an environment where we share not the data itself but proof of its existence.

Ye Tao, Ehsan Javanmardi, Pengfei Lin, Jin Nakazato · 7 authors

Cooperative perception is crucial for connected automated vehicles in intelligent transportation systems (ITSs); however, ensuring the authenticity of perception data remains a challenge as the vehicles cannot verify events that they do not witness independently. Various studies have been conducted on establishing the authenticity of data, such as trust-based statistical methods and plausibility-based methods. However, these methods are limited as they require prior knowledge such as previous sender behaviors or predefined rules to evaluate the authenticity. To overcome this limitation, this study proposes a novel approach called zero-knowledge Proof of Traffic (zk-PoT), which involves generating cryptographic proofs to the traffic observations. Multiple independent proofs regarding the same vehicle can be deterministically cross-verified by any receivers without relying on ground truth, probabilistic, or plausibility evaluations. Additionally, no private information is compromised during the entire procedure. A full on-board unit software stack that reflects the behavior of zk-PoT is implemented within a specifically designed simulator called Flowsim. A comprehensive experimental analysis is then conducted using synthesized city-scale simulations, which demonstrates that zk-PoT’s cross-verification ratio ranges between 80 % to 96 %, and 90 % of the verification is achieved in 5 s, with a protocol overhead of approximately 25 %. Furthermore, the analyses of various attacks indicate that most of the attacks could be prevented, and some, such as collusion attacks, can be mitigated. The proposed approach can be incorporated into existing works, including the European Telecommunications Standards Institute (ETSI) and the International Organization for Standardization (ISO) ITS standards, without disrupting the backward compatibility.

Carsten Baum, Chiang, James Hsin-yu, Bernardo David, Tore Kasper Frederiksen

Recent years have seen the emergence of practical advanced cryptographic tools that not only protect data privacy and authenticity, but also allow for jointly processing data from different institutions without sacrificing privacy. The ability to do so has enabled implementations of a number of traditional and decentralized financial applications that would have required sacrificing privacy or trusting a third party. The main catalyst of this revolution was the advent of decentralized cryptocurrencies that use public ledgers to register financial transactions, which must be verifiable by any third party, while keeping sensitive data private. Zero Knowledge (ZK) proofs rose to prominence as a solution to this challenge, allowing for the owner of sensitive data (e.g. the identities of users involved in an operation) to convince a third party verifier that a certain operation has been correctly executed without revealing said data. It quickly became clear that performing arbitrary computation on private data from multiple sources by means of secure Multiparty Computation (MPC) and related techniques allows for more powerful financial applications, also in traditional finance. &#13;\nIn this SoK, we categorize the main traditional and decentralized financial applications that can benefit from state-of-the-art Privacy-Enhancing Technologies (PETs) and identify design patterns commonly used when applying PETs in the context of these applications. In particular, we consider the following classes of applications: 1. Identity Management, KYC &amp; AML; 2. Markets &amp; Settlement; 3. Legal; and 4. Digital Asset Custody. We examine how ZK proofs, MPC and related PETs have been used to tackle the main security challenges in each of these applications. Moreover, we provide an assessment of the technological readiness of each PET in the context of different financial applications according to the availability of: theoretical feasibility results, preliminary benchmarks (in scientific papers) or benchmarks achieving real-world performance (in commercially deployed solutions). Finally, we propose future applications of PETs as Fintech solutions to currently unsolved issues. While we systematize financial applications of PETs at large, we focus mainly on those applications that require privacy preserving computation on data from multiple parties.

T. Haritha, A. Anitha

Access control to patient information has become increasingly important in healthcare systems. It is vital to enhance the security of healthcare systems to avoid data loss despite the various security policies imposed by healthcare management. The issue needs to be resolved with a comprehensive secure framework, which allows users to access data according to their level of confidentiality. This article presents a solution by imposing multi-level security in e-health systems by integrating the Lattice-Based Access Control (LBAC) model and blockchain-based smart contract mechanisms. These mechanisms provide security levels in compliance with data access restrictions among users and resources while maintaining compliance security levels. By using LBAC, you can provide multilevel protection for access control restrictions, whereas smart contracts are used to ensure the transaction process in a decentralized system via an agreement between the parties. A smart contract validates every user and performs the authentication process in the envisioned model, which uses the Ethereum Virtual Machine (EVM). In the blockchain network, the patient’s e-health details are accessed and stored as immutable blocks. Comparing the proposed scheme with existing benchmarking methods reveals that the proposed scheme preserves privacy, maintains transparency, provides an authentication process, maintains data integrity, and provides multilevel access control security. The proposed model performs better than other existing models. As a result, lattice-based access control enhances the security of e-health records.

Mohameden Dieye, Pierre Valiorgue, Jean-Patrick Gelas, El-hacen Diallo · 7 authors

Systems for generating and managing digital identities are in the process of being transformed to improve data sharing security and increase decentralization. Addressing both issues, a theoretical solution to create and manage Self-Sovereign Identities (SSI) is proposed using two Zero-Knowledge Proof (ZKP) protocols based on the discrete logarithm difficulty. Automorphism group properties are introduced to link several identities, their identifiers and attributes to produce a proof. The proposed SSI protocol does not encounter the problem of reusing the same secret key as in the case of the initial ZKP Schnorr protocol. The designed protocol ensures minimal disclosure of information to a single trusted third party. In addition, it allows zero disclosure of information to service providers requiring proof of authentication or identification. Such a SSI protocol is compliant with Electronic IDentification And Trust Services (eIDAS) as well as General Data Protection Regulation (GDPR) regulations.

Dana Alsagheer, Lei Xu, Weidong Shi

Researchers have started to recognize the necessity for a well-defined ML governance framework based on the principle of decentralization and comprehensively defining its scope of research and practice due to the growth of machine learning (ML) research and applications in the real world and the success of blockchain-based technology. In this paper, we study decentralized ML governance, which includes ML value chain management, decentralized identity for the ML community, decentralized ownership and rights management of ML assets, community-based decision-making for the ML process, decentralized ML finance, and risk management.

Nima Afraz, Francesc Wilhelmi, Hamed Ahmadi, Marco Ruffini

Blockchain technology offers solutions to numerous network problems by leveraging distributed record-keeping and collaborative decision-making features. However, deployment considerations such as blockchain infrastructure cost, performance requirements, and scalability are often overlooked. This paper provides an in-depth perspective on deploying blockchain-based solutions for telecommunications networks, estimating costs, comparing infrastructure options (on-premises, IaaS, BaaS), and choosing a suitable blockchain platform. We have analyzed prominent use cases and investigated deployment options, highlighting the pros and cons of each. Finally, we present two case studies, one proposing a distributed marketplace solution for 5G slice brokering and another one on the decentralization of federated learning (FL) through blockchain. Experiments are conducted to identify the performance limitations of the proposed solution under various deployment infrastructures. For the slice brokering use case, we studied the achievable transaction throughput and average latency under various systems under test with different resource specifications. Our experiments showed that while use cases that required maximum transaction throughputs in the range of 10 to 200 could be carried out with sub-second latency, use cases that require higher transaction throughputs (300 to 400) would need more computational resources to maintain such low latency. The federated learning use case provided insights into the achievable accuracy of distributed learning under various blockchain settings (public, consortium, and private). This led to the understanding that private and consortium blockchains can achieve acceptable accuracy in significantly lower training times compared to public blockchains.

Sumit Kumar Rana, Arun Kumar Rana, Sanjeev Rana, Vishnu Sharma · 7 authors

Modern legal proceedings heavily rely on digital evidence as a basis for decisions in a variety of contexts, including criminal investigations and civil lawsuits. However, factors like data alteration, unauthorised access, or flaws in centralised storage can threaten the security and integrity of digital evidence. We suggest a decentralised methodology for using smart contracts to safeguard digital evidence in order to overcome these issues. The decentralised model makes use of smart contracts and blockchain technology to guarantee the integrity, transparency, and immutability of digital evidence. The approach does not require a centralised authority because it makes use of a distributed ledger, which lowers the possibility of data loss or manipulation. Multiple parties participating in the evidence lifecycle can build confidence and accountability thanks to smart contracts’ programmable rules and automated enforcement mechanisms. In our study, we show the decentralised model’s architecture and describe its essential elements, such as the blockchain network, smart contracts, and decentralised storage. We go over the advantages of employing this architecture, including enhanced auditability, decreased dependency on centralised institutions, and increased data security. Additionally, we discuss potential difficulties and constraints, like scalability and interoperability. We run a few simulations and experiments to test the suggested model’s viability and effectiveness while comparing it to conventional centralised methods. The outcomes show that our decentralised paradigm offers improved security for digital evidence, guaranteeing its reliability, usability, and tamper-proofness. We also go through how the model is used in actual legal systems, law enforcement organisations, and digital forensics investigations.

Dhruv Patel, Ritesh Tandon

Cryptography is one of the most important approaches to keep digital communication in lock and key and therefore guarantees the privacy, integrity and authenticity of the data by means of complex coding. Cryptographic techniques have arisen centuries ago and the old techniques have continued to evolve as today’s challenges are cloud computing, data storage and retrieval as well as authenticated users. With the widespread spread of cloud environments, trust-enabling systems with cutting-edge technologies such as Zero Knowledge Proofs and blockchain emerged to increase privacy and security. By examining the notion of trust localization, this study draws attention to a use of Gateways as access control points to sensitive information reducing the dependence on the central cloud infrastructures. Additionally, source authentication and authorization provided by Acaras and PKI is studied regarding their integration and effectiveness. The research examines the current challenges in cryptographic security and current findings of cryptographic security and investigates what the future of cryptographic security to build trust and security in modern digital systems..

Junjian Yan, Zixuan Fang, Qizhi He, Yanfei Lu

As an important link of the Internet of Things, wireless sensor network has great potential in industry, agriculture and daily use of residents. To ensure cloud data security, data is encrypted before being uploaded to the cloud. However, how to ensure the searchability of ciphertext in cloud is challenging. To solve the above problems, a searchable encryption scheme for sensor networks is proposed. The sensor node collects the data, encrypts it and uploads it to the smart contract after collecting the data from the local gateway. In this scheme, the concept of version information is introduced to authenticate ciphertext version so as to realize access control of trap gates of different versions. In order to avoid data leakage caused by the information storage center, the searchable encryption technology is combined with the block chain technology, and the smart contract is used as the data storage center, so as to ensure efficient ciphertext retrieval and eliminate malicious behavior on the server side. Through security analysis, it is proved that the scheme satisfies ciphertext indistinguishability and forward security based on DBDH hard problems. Experimental efficiency analysis shows that the scheme has certain advantages in computational efficiency.

Komal Farooq, Hassan Jamil Syed, Samar Othman Alqahtani, Wamda Nagmeldin · 6 authors

This research combines two emerging technologies, the IoT and blockchain, and investigates their potential and use in the healthcare sector. In healthcare, IoT technology can be utilized for purposes such as remotely monitoring patients’ health. This paper details ongoing research towards individualized health monitoring using wearable gadgets. The goal of improving healthcare facilities and improvement of the quality of life of citizens naturally brings up Internet of Things (IoT) technologies for consideration. Health observation is exceptionally critical in terms of avoidance, especially since the early determination of illnesses can minimize trouble and treatment costs. The cornerstones of intelligent, integrated, and individualized healthcare are continuous monitoring of physical signs and evaluation of medical data. To build a more reliable and robust IoMT model, the study will monitor the application of blockchain technology in federated learning (FL). A viable way to address the heterogeneity problem in federated learning is to design the system, data, and model tiers to lessen heterogeneity and produce a high-quality, tailored model for each endpoint. Blockchain-based federated learning allows for smarter simulations, lower latency, and lower power consumption while maintaining privacy at the same time. This solution provides another immediate benefit: in addition to having a shared model upgrade, the updated model on phones will now be used automatically, giving personalized knowledge about the phone is used.

Lirui Bi, Tasiu Muazu, Omaji Samuel

We propose a decentralized medical trust management system using blockchain-based federated learning for large-scale Internet of Things (IoT) systems. The proposed system enables health institutions to share data without revealing the privacy of data owners. Health institutions form coalitions and the leader of each coalition is elected based on the proposed proof-of-trust collaboration (PoTC) consensus protocol. The PoTC consensus protocol is based on a weight difference game where trust scores, trust consistency value, and trust deviation are factors used for evaluating nodes in the blockchain. The trust of a node is obtained either through direct trust or recommended trust evaluations. Each leader elects an aggregator who has the most credibility to manage the proposed federated learning system. The leaders become the federated clients as well as validators while the aggregator is the federated server. To ensure the decentralization of nodes, a consortium blockchain is employed. Extensive simulations are performed, which show that the proposed system not only demonstrates scalability and credibility without compromising the accuracy, convergence, and resilience properties against malicious attackers but also outperforms existing trust management systems. A security analysis is also conducted, which shows that the proposed system is robust against trust-related attacks.