Prof. Afsha Akkalot, Parth Vijaykumar Lashkare, Hemant Patel, Dharmendrasingh Rajpurohit
Abstract: The emergence of blockchain technology has paved the way for the development of secured e-voting systems that are transparent, immutable, and tamper-proof. In this survey report, we examine the current trends and future prospects of using blockchain technology for e-voting. We begin by exploring the key features of blockchain technology, including distributed ledger, cryptographic security, and decentralized consensus mechanisms. Next, we discuss the benefits of using blockchain technology for e-voting, including voter anonymity, transparent voting process, and immutable voting records. To gain a better understanding of the current state of the field, we conducted a survey of recent literature on blockchain-based evoting systems. The survey covers research articles, white papers, and conference proceedings published in the last five years. We analysed the data using statistical software and present our findings on the most commonly used consensus algorithms, cryptographic techniques, and blockchain architectures in e-voting systems. Our survey also highlights the challenges facing the adoption of blockchain-based e-voting systems, including scalability, usability, and regulatory issues. We conclude by discussing the future prospects of blockchain-based e-voting systems and identifying the areas that require further research and development. Overall, this survey report provides valuable insights into the current trends and future prospects of using blockchain technology for e-voting, and will be useful for researchers, policymakers, and practitioners working in this field.
Open access
Internet Traffic Analysis and Secure E-voting
Privacy-Preserving Technologies in Data
Advanced Steganography and Watermarking Techniques
A. Rossi, Andrea Natalini, Lorenzo Cristofori, Marzia Mammina
This paper will describe a trustworthy blockchain-based framework to ensure secure, immutable and pseudo anonymized data collection at field level. This paper will describe the context in which the framework is being conceived and developed, starting from the analysis of the state of the art of DLT (Distributed Ledger Technology) and Blockchain, to highlight the potential uses cases in terms of data management, in particular. A specific focus will be given to the FIWARE framework, since the final objective is the creation of a tool for data gathering, as a FIWARE context broker extension. The core of the tool will be the two smart contracts implementing the PoE (Proof of Existence) and the RT-MDN (Reat Time-Monitoring Data Notarization). The smart contracts will be used for the certification of single documents and bulk of monitoring data, respectively. The second case is characterized by a periodicity of production and represent the most innovative part of the work. A first PoC (Proof of Concept) will be implemented, and first early results will be presented as well. The work is being developed in the framework of the EU co-funded “DigiBUILD” project.
Permissionless blockchains promise to be resilient against censorship by a single entity. This suggests that deterministic rules, and not third-party actors, are responsible for deciding if a transaction is appended to the blockchain or not. In 2022, the U.S. Office of Foreign Assets Control (OFAC) sanctioned a Bitcoin mixer and an Ethereum application, putting the neutrality of permissionless blockchains to the test. In this paper, we formalize quantify and analyze the security impact of blockchain censorship. We start by defining censorship, followed by a quantitative assessment of current censorship practices. We find that 46% of Ethereum blocks were made by censoring actors that intend to comply with OFAC sanctions, indicating the significant impact of OFAC sanctions on the neutrality of public blockchains. We further uncover that censorship not only impacts neutrality, but also security. We show how after Ethereum's move to Proof-of-Stake (PoS) and adoption of Proposer-Builder Separation (PBS) the inclusion of censored transactions was delayed by an average of 85%. Inclusion delays compromise a transaction's security by, e.g., strengthening a sandwich adversary. Finally we prove a fundamental limitation of PoS and Proof-of-Work (PoW) protocols against censorship resilience.
Technological advancement has led to a rapid increase in the growth of IoT devices leading to a vast amount of generated data. Manufacturers of such devices utilize machine learning algorithms to extract valuable insights from user data. However, this can give rise to critical issues surrounding data leakage and privacy. To tackle these issues, utilizing blockchain as a decentralized database to securely store data and employing federated learning to extract useful insights from user data can provide a viable solution. In this paper, we propose a three-layered, decentralized architecture that uses a traditional federated learning mechanism in conjunction with the Ethereum blockchain. Moreover, for data management, we use Inter-Planetary File System (IPFS) which is a peer-to-peer network used to store data in a decentralized manner. We tested our model's feasibility by using CIFAR-10 dataset and Python as the programming language with a framework for federated learning on a general purpose computer. We used Ganache_v2.5.4 and Truffle_v5.4.22 for developing smart contracts and testing and deploying them over the Ethereum blockchain.
With the development of artificial intelligence, more and more applications rely on a large amount of high-quality data. Due to data island and security concerns, most of data is scattered on various devices and difficult to obtain. Federated learning (FL) is a promising paradigm to allow distributed devices cooperating to train a shared model without sharing raw data. However, the traditional FL is easy to be attacked because of single-point failure and it cannot avoid devices uploading fake or low-quality model updates. To this end, blockchain is integrated into FL to establish a secure model training ecosystem by maintaining an immutably distributed ledger. However, different data quality of raw data, diverse energy resources of devices, and different trust degree of devices make it challenging for blockchain-enabled FL efficient and reliable. Therefore, in this paper, we design a fine-grained resource allocation scheme for blockchain-enabled FL with considering the credit of devices, data quality, and energy resources. We first propose a credit-based blockchain-enabled FL to jointly execute FL training and blockchain establishment. Then we formulate the resource allocation problem with considering credit, data quality, precision, latency, and energy resources. A deep-reinforcement learning based algorithm is designed to solve the problem, and BlockSim is used to build the blockchain-enabled FL platform. Simulation results demonstrate the effectiveness of our proposed scheme on precision, latency and energy consumption, compared with traditional blockchain-enabled FL.
Kadiyala Ramana, R. Madana Mohana, C. Kishor Kumar Reddy, Gautam Srivastava · 5 authors
As the Internet of Things (IoT) has advanced, data sharing has become a crucial function of cloud computing. However, data security remains a significant challenge in this field. This research proposes a blockchain-based data-sharing system that prioritizes data security and efficiency. The system includes efficient smart contracts and security gateways that record data in the cloud using blockchain. If suspicious behaviour is detected, the blockchain is checked by the centralized cloud, and the responsible party for any malicious gateway behaviour is held accountable. Authentication and data exchange algorithms are used to ensure data security. Additionally, to reduce the burden on end-users, smart contracts in blockchain use highly complex partial decryption algorithms. To satisfy data restriction safety criteria, blockchain achieves traceability of historical actions through open and transparent supervision. Experimental findings demonstrate that the proposed technique is effective in ensuring the safety and efficiency of information exchange between various clients.
The main characteristics of blockchains, such as security and traceability, have enabled their use in many distinct scenarios, such as the rise of new cryptocurrencies and decentralized applications (dApps). However, part of the information exchanged with the typical blockchains is public, which can lead to privacy issues. To avoid or mitigate these issues, some blockchains are applying mechanisms to deal with data privacy. Trusted Execution Environments, the basis of confidential computing, and secure Multi-party Computation are two technologies that can be applied in that sense. In this paper, we analyzed seven blockchain technologies that apply mechanisms to improve data privacy. We defined seven technical questions related to common requirements for decentralized applications and, to answer each question, we reviewed the available documentation and gathered information from chat channels. We briefly present each blockchain technology and the answers to each technical question. Finally, we present a table summarizing the information and showing which technologies are more prominent.
This paper introduces the Proof of Opinion (PoO) consensus algorithm as an alternative to the traditional Proof of Work (PoW) and Proof of Stake (PoS) algorithms. PoO is designed to address the issues of energy efficiency, decentralization, privacy protection, scalability, and incentivization that are associated with PoW and PoS. PoO relies on the collective opinion of network nodes to reach consensus, which reduces the computational and energy requirements compared to PoW and PoS. PoO can be customized to provide a greater degree of privacy protection, such as by enabling private transactions and hiding transaction details from other nodes. PoO is designed to be more scalable than PoW and PoS, as it relies on the collective opinion of nodes, which reduces the computational requirements and enables greater scalability. PoO’s reputation-based approach incentivizes nodes to act honestly and perform their duties diligently, which promotes network security, stability, and better consensus decision-making. Our simulation results show that PoO can achieve comparable performance to PoW and PoS while consuming significantly less energy. PoO also provides greater decentralization, privacy protection, and scalability compared to PoW and PoS.
Cross-silo federated learning (FL) is promising in facilitating data collaboration across various organizations, which greatly alleviates the information silo problem in industries and promotes the data intelligence of Internet of Things. With the advances of decentralized FL, the higher requirements of trust and privacy are put forward. Traditional FL heavily relies on a central coordinating server, which suffers from single points of failure and lacks trust in the correctness of aggregation results. What is more, the intrinsic privacy issues of FL have aroused public attention, such as gradient inversion attack in local gradients. However, the privacy of quantized gradients remains serious and lacks attention, especially the most extremely 1-bit quantization in sign-based FL. In this article, we demonstrate the potential privacy risk in sign-based FL by presenting a new gradient inversion attack, which successfully restores the original data from sign-based quantized gradients. And then we tackle the above two challenges via constructing a self-aggregation privacy-preserving FL atop blockchain, which takes advantage of a variant of ElGamal encryption to protect the privacy of local sign-based quantized gradients, and leverages the smart contract to achieve secure self-aggregation for participants without involving a centralized server. Moreover, we analyze that the proposed protocol achieves privacy and public verifiability. Finally, we evaluate the performance of the proposed protocol with a real deep learning model, and the results show that our protocol is resilient against gradient inversion attack in a decentralized environment without sacrificing learning accuracy.
The integration of Internet of Things (IoT) with cloud–edge computing in cyber–physical systems has revolutionized the way healthcare enterprises manage electronic health records (EHRs). With more healthcare enterprises outsourcing encrypted EHRs to the cloud, searchable encryption (SE) is utilized to retrieve encrypted data, especially attribute-based SE (ABSE) can achieve fine-grained access control. However, ABSE usually requires a lot of computation, which imposes a serious burden on resource-limited devices. Moreover, ensuring fairness in data access is crucial in the healthcare domain, where both data users and owners may have conflicting interests. In order to overcome these problems, this article proposes an SE scheme with fine-grained access control for cloud-based EHRs sharing assisted by blockchain. It transfers computing tasks to edge servers and enables users to control who has access to their EHRs. The adoption of blockchain and smart contracts guarantees data integrity and transaction fairness. Moreover, a consensus algorithm is designed for the higher efficiency of the proposed scheme. Finally, security analysis proves that the proposed scheme resists adaptive chosen keyword attacks (CKAs). Performance analysis further confirms that it has more functionalities and is efficient for smart healthcare.
Heyi Zhang, Jun Wu, Xi Lin, Ali Kashif Bashir · 5 authors
The convergence of blockchain and deep learning (DL) drives the intelligence of the Internet of Things (IoT) with security guarantees. However, the soaring resource consumption resulting from blockchain mining and DL model training has overwhelmed the extremely resource-constrained IoT. In this article, we first build a blockchain and DL-empowered cloud–edge orchestrated framework for an extremely resource-constrained IoT environment. To solve the resource bottleneck of this framework, we then propose a Zero-knowledge Proof of Learning (ZPoL) consensus approach to channel the meaningless Proof of Work (PoW) mining energy waste to valuable DL model training, while protecting the DL model privacy. Besides, to encourage resource-constrained IoT devices to perform meaningful DL model mining in our ZPoL consensus, we design a model quality-aware incentive mechanism based on a two-stage Stackelberg game. Moreover, we conduct extensive simulations and experiments to evaluate our proposed ZPoL-based framework. The numerical simulation illustrates that our proposed incentive mechanism could motivate IoT devices to actively join in DL model mining. Compared with the existing blockchain and DL-enabled IoT system, experimental results demonstrate that our proposed ZPoL-based framework could significantly reduce the communication, computation, and storage cost, which is more applicable to a resource-constrained IoT environment.
With the development of Internet of Things (IoT) applications, there is an explosion in demands for data sharing between IoT nodes, which needs the IoT network to provide a data sharing platform to enhance data privacy and security. IOTA Tangle has been considered as a promising Distributed Ledger Technology to establish such a platform because of its affinity to IoT nodes. Additionally, Tangle proposes an access control system to prevent congestion of the platform by allocating a number of tokens to each node, and limiting data sharing rate of each node based on its hold tokens. However, this poses an inconspicuous but fatal problem that high-rate-demand nodes with few tokens cannot acquire an ideal rate. In this paper, we optimize the access control system for the Tangle-based platform by setting a token transferring mechanism, where a chaebol node can sell tokens to provide IoT nodes with ideal data sharing rate. Since the chaebol node needs more profits and IoT nodes need ideal data sharing rate, we formulate the token pricing and purchasing problem as a two-stage Stackelberg game. We analyze the equilibrium of the game in a uniform pricing scheme and design an access control algorithm TTM based on the equilibrium. Finally, we verify performance of the algorithm by experiments. The results show that the proposed algorithm outperforms the state-of-art in terms of providing high-rate-demand nodes with the ideal rate.
The flexibility and low cost of unmanned aerial vehicles (UAVs) offer great potential for them in areas such as disaster relief, energy line inspection, and traffic monitoring. Multiple UAVs form an airborne UAV network to share geo-tagged observation data for better collaborative missions. Blockchain can solve the security threats caused by the environment’s untrustworthiness and the UAV networks’ openness. However, the key to sharing data in blockchain-assisted UAV networks is identifying and understanding observational data and providing authentication query services in free boundary spatial. This paper proposes a blockchain-assisted UAV network data-sharing framework based on Non-Fungible Token (NFT). First, we design a marking and describing data method based on NFT to help geo-tagged data be effectively understood. Moreover, we propose a free-boundary spatial index tree to manage data and provide efficient queries. Furthermore, combined with the consensus mechanism and the blockchain transaction tree, the proposed sharing framework can provide query results authentication. Compared with the existing schemes, analysis and experiments demonstrate that our scheme could support the arbitrary expansion of UAV flight range and the random distribution of observation data in space, save at least 22% of storage overhead and reduce more than 36% of query time overhead.
H. F. Zhang, Lubin Lin, Guipeng Zhang, Zhenguo Yang · 5 authors
With the spread of information on the Internet and the explosive growth of intellectual property information, the traditional intellectual property management model relying on third-party institutions cannot meet the demand for intellectual property protection, which has a cumbersome process, low efficiency, and insufficient evidence of rights protection. To address the issues of information falsification and leakage, we present an anonymous and traceable intellectual property management system based on blockchain, namely ATIPM, which employs the non-interactive zero knowledge proof to realize user unlinkability and anonymous transactions to protect the users’ intellectual property information. To avoid a single point of accountability, the ATIPM introduces a threshold ramp secret sharing scheme to achieve the traceability of intellectual property for all users and greatly improve the users’ privacy security and autonomy by preventing information leakage from malicious third-party institutions. Furthermore, the ATIPM can improve the management efficiency of intellectual property by utilizing smart contracts to realize efficient retrieval and verification of intellectual property. The evaluation results demonstrate the effectiveness of our proposed system.
Federated learning is an efficient technology that implements distributed model training among multiple data sources with local data, and can realize data privacy protection and data sharing computing .However, existing federated learning models may involve a large number of external attacks that can reconstruct the original training data using the acquired model, resulting in possible global model or user privacy data attacks. To address the above problem, we propose a new decentralized multiconsensus federated learning model by combining blockchain and interplanetary file system (IPFS), named as BIFLC. To be specific, we firstly design an on-chain consensus process based on a blockchain hybrid consensus mechanism by introducing a proof- of-work (PoW) and a proof-of-stake (PoS) mechanism, which can ensure the integrity of the on-chain consensus process and provide a chained data hash index for data. Furthermore, we introduce the interplanetary file system to reduce the cost of storing data on the chain and employ its distributed content delivery mechanism to save bandwidth. Extensive experiments demonstrate that our proposed scheme has higher accuracy and lower IPFS transmission time.
C. U. Om Kumar, Sudhakaran Gajendran, Viswaksena Balaji, A. Nhaveen · 5 authors
Transferring of data in machine learning from one party to another party is one of the issues that has been in existence since the development of technology. Health care data collection using machine learning techniques can lead to privacy issues which cause disturbances among the parties and reduces the possibility to work with either of the parties. Since centralized way of information transfer between two parties can be limited and risky as they are connected using machine learning, this factor motivated us to use the decentralized way where there is no connection but model transfer between both parties will be in process through a federated way. The purpose of this research is to investigate a model transfer between a user and the client(s) in an organization using federated learning techniques and reward the client(s) for their efforts with tokens accordingly using blockchain technology. In this research, the user shares a model to organizations that are willing to volunteer their service to provide help to the user. The model is trained and transferred among the user and the clients in the organizations in a privacy preserving way. In this research, we found that the process of model transfer between user and the volunteered organizations works completely fine with the help of federated learning techniques and the client(s) is/are rewarded with tokens for their efforts. We used the COVID-19 dataset to test the federation process, which yielded individual results of 88% for contributor a, 85% for contributor b, and 74% for contributor c. When using the FedAvg algorithm, we were able to achieve a total accuracy of 82%.
Open access
Privacy-Preserving Technologies in Data
Blockchain Technology Applications and Security
Artificial Intelligence in Healthcare and Education
Stepping into the era of big data, with more resources shared, the machine learning algorithms are more likely to derive a better solution, and those complicated computations can be finished in a shorter time. The existing works about multiparty computing mainly focus on how to perform the computation when the involved partners are given, but hardly consider the process during which the partners find each other. In this work, we proposed a framework of the multiparty computing network (MPCNet) for the agents propose and collaborate, where R3 Corda is harnessed to establish a blockchain platform where the convener is able to look for some other partners, and a crowdsourcing process is performed to verify the validity of the conveners proposal and the partners applications. Furthermore, a reward mechanism is proposed in order to motivate the verifiers to participate. Once all the agents joining the computing task are confirmed, they communicate with each other to perform the computing task, following the plan that is mentioned in the proposed smart contract. Experimental results demonstrated the feasibility, usability, and scalability of our proposed approach.
Zhuotao Lian, Weizheng Wang, Zhaoyang Han, Chunhua Su
The rapid growth of artificial intelligence (AI), blockchain technology, and edge computing services have enabled the Internet of Medical Things (IoMT) to provide various healthcare services to patients, including neural network-based disease diagnosis, heart rate monitoring, and fall detection. Generally, end devices should transmit the collected patient data to a centralized server for further model training, but at the same time, the patient's privacy may be at risk. In addition, due to the diversity of patient conditions, a one-size-fits-all model cannot meet personalized healthcare needs. To address the above challenges, we propose a blockchain-based personalized federated learning (FL) system that enables clients to participate in personalized model training without directly uploading private data. We further realize the decentralized FL by combining blockchain technology, which improves the security level of the system. Finally, we verify the reliable performance of our system on different datasets through simulation experiments.
Liuling Qi, Junfeng Tian, Mengjia Chai, Hongyun Cai
More and more works use blockchain to improve the data security of integrated edge computing and Internet of Things (IoT) system. However, there are some problems of Proof-of-Work (PoW) that hinder the application, such as high energy consumption, low resource utilization efficiency, and insufficient incentive. In this article, we present a cooperative PoW named relay mining-based PoW (Relay-PoW) to reduce energy consumption and improve resource utilization efficiency, where the nodes can mine blocks together under the management of edge server. We further propose parallel relay mining method to increase the throughput, where the nodes can mine blocks with multiple heights in a pipeline manner. In addition, we design supervision group mechanism to ensure the security, where the edge server evaluates the trust values of the nodes according to the capability and quality, and eliminates abnormal nodes timely. Finally, we propose a Shapley-based reward allocation strategy (SRAS) to encourage node to participate in Relay-PoW. Experimental results show that Relay-PoW can effectively decrease the energy consumption, improve the throughput and resource utilization efficiency, SRAS can motivate nodes to cooperate, and they all have a better performance than other methods.
Access control data will continue to be exposed to the threat of privacy leakage even if blockchain technology currently offers a new solution for the security and privacy of the internet of things (IoT). However, its usability and privacy are not completely leveraged. This paper first discusses the IoT and blockchain technology and then examines each technology's structural models in order to address the issue of information security and privacy protection for the global organization IoT based on blockchain. Second, the information security and privacy guarantee system based on blockchain is built with ZKP and TEE at its heart after problems with zero-knowledge proof (ZKP) and trusted execution environment (TEE) in information security guarantee based on blockchain are investigated. By comparing the simulation trials, the proposed system's viability is finally confirmed. The results demonstrate that the suggested algorithm's evidence generation time is 352 ms when it reaches the experiment's highest node 28, which is clearly faster than previous techniques.
When a blockchain application runs on data from the real world, it relies on an oracle mechanism that transports data from external sources to the blockchain. The blockchain oracle problem arises around the need to procure trustworthy data from external sources. Previous works have addressed data authenticity/integrity by building a secure channel between blockchain and external sources while employing a decentralized oracle network to avoid a single point of failure. However, the truthful data challenge, which emerges when legitimate external sources submit fraudulent or deceitful data, remains unsolved. In this paper, we introduce a new decentralized truth-discovering oracle architecture called DecenTruth to address the truthful data challenge using a data-centric approach. DecenTruth aims to elevate the "truthfulness" of external data input by enabling decentralized oracle nodes to discover and reach consensus on truthful values of common data objects from multi-sourced inputs in an off-chain manner. It harmonizes techniques in both the data plane and consensus plane—truth discovery (TD) and asynchronous BFT consensus—and enables nodes to finalize the same estimated truths on data objects with high accuracy, amid the harsh asynchronous network condition and presence of Byzantine sources and nodes. We implemented DecenTruth and evaluated its performance in a simulated oracle service scenario. The results demonstrate significantly higher Byzantine resilience and long-term data feed accuracy of DecenTruth, compared to existing median-based aggregation methods.