Konstantin D. Pandl, Scott Thiebes, Manuel Schmidt-Kraepelin, Ali Sunyaev
Developments in Artificial Intelligence (AI) and Distributed Ledger\nTechnology (DLT) currently lead to lively debates in academia and practice. AI\nprocesses data to perform tasks that were previously thought possible only for\nhumans. DLT has the potential to create consensus over data among a group of\nparticipants in uncertain environments. In recent research, both technologies\nare used in similar and even the same systems. Examples include the design of\nsecure distributed ledgers or the creation of allied learning systems\ndistributed across multiple nodes. This can lead to technological convergence,\nwhich in the past, has paved the way for major innovations in information\ntechnology. Previous work highlights several potential benefits of the\nconvergence of AI and DLT but only provides a limited theoretical framework to\ndescribe upcoming real-world integration cases of both technologies. We aim to\ncontribute by conducting a systematic literature review on previous work and\nproviding rigorously derived future research opportunities. This work helps\nresearchers active in AI or DLT to overcome current limitations in their field,\nand practitioners to develop systems along with the convergence of both\ntechnologies.\n
Yang Zhao, Jun Zhao, Jiawen Kang, Zehang Zhang · 7 authors
An increasing amount of users' sensitive information is now being collected for analytics purposes. To protect users' privacy, differential privacy has been widely studied in the literature. Specifically, a differentially private algorithm adds noise to the true answer of a query to generate a noisy response. As a result, the information about the dataset leaked by the noisy output is bounded by the privacy parameter. Oftentimes, a dataset needs to be used for answering multiple queries (e.g., for multiple analytics tasks), so the level of privacy protection may degrade as more queries are answered. Thus, it is crucial to keep track of the privacy spending which should not exceed the given privacy budget. Moreover, if a query has been answered before and is asked again on the same dataset, we may reuse the previous noisy response for the current query to save the privacy cost. In view of the above, we design and implement a blockchain-based system for tracking and saving differential-privacy cost. Blockchain provides a distributed immutable ledger that records each query's type, the noisy response used to answer each query, the associated noise level added to the true query result, and the remaining privacy budget in our system. Furthermore, since the blockchain records the noisy response used to answer each query, we also design an algorithm to reuse previous noisy response if the same query is asked repeatedly. Specifically, considering that different requests of the same query may have different privacy requirements, our algorithm (via a rigorous proof) is able to set the optimal reuse fraction of the old noisy response and add new noise (if necessary) to minimize the accumulated privacy cost. Experimental results show that the proposed algorithm can reduce the privacy cost significantly without compromising data accuracy.
Andrei Lihu, Jincheng Du, Igor Barjaktarević, Patrick Gerzanics · 5 authors
Bitcoin mining is a wasteful and resource-intensive process. To add a block of transactions to the blockchain, miners spend a considerable amount of energy. The Bitcoin protocol, named 'proof of work' (PoW), resembles a lottery and the underlying computational work is not useful otherwise. In this paper, we describe a novel 'proof of useful work' (PoUW) protocol based on training a machine learning model on the blockchain. Miners get a chance to create new coins after performing honest ML training work. Clients submit tasks and pay all training contributors. This is an extra incentive to participate in the network because the system does not rely only on the lottery procedure. Using our consensus protocol, interested parties can order, complete, and verify useful work in a distributed environment. We outline mechanisms to reward useful work and punish malicious actors. We aim to build better AI systems using the security of the blockchain.
Crowdsensing is an emerging paradigm of data aggregation, which has a pivotal role in data-driven applications. By leveraging the recruitment, a crowdsensing system collects a large amount of data from mobile devices at a low cost. The critical issues in the development of crowdsensing are platform security, privacy protection, and incentive. However, the existing centralized, platform-based approaches suffer from the single point of failure which may result in data leakage. Besides, few previous studies have addressed the considerations of both the economic incentive and data quality. In this paper, we propose a decentralized crowdsensing architecture based on blockchain technology which will help improve the attack resistance. Furthermore, we present a hybrid incentive mechanism, which integrates the data quality, reputation, and monetary factors to encourage participants to contribute their sensing data while discouraging malicious behaviors. The effectiveness our of proposed incentive model is verified through a combination of the theory of mechanism design. The performance analysis and simulation results illustrate that the proposed hybrid incentive model is a reliable and efficient mean to promote data security and incentivizing positive conduct on the crowdsensing application.
The aim of this paper is to understand whether Distributed Ledger\nTechnologies (DLTs) are ready to support complex services, such as those\nrelated to Intelligent Transportation Systems (ITS). In smart transportation\nservices, a huge amount of sensed data is generated by a multitude of vehicles.\nWhile DLTs provide very interesting features, such as immutability,\ntraceability and verifiability of data, some doubts on the scalability and\nresponsiveness of these technologies appear to be well-founded. We propose an\narchitecture for ITS that resorts to DLT features. Moreover, we provide\nexperimental results of a real test-bed over IOTA, a promising DLT for IoT.\nResults clearly show that, while the viability of the proposal cannot be\nrejected, further work is needed on the responsiveness of DLT infrastructures.\n
Katarina Preikschat, Moritz Böhmecke‐Schwafert, Jan‐Paul Buchwald, Carolin Stickel
Summary Blockchain technology has the potential to bring transparency and trust to a multitude of use cases. Our research demonstrates that the technology can reduce asymmetric information in markets by bridging trust gaps. The combination of blockchain and Internet of Things technology that automatically collects sensor data, provides a feasible, decentralized technological solution for such an inefficient “Market of Lemons” coined by nobel laureate Georg Akerlof. In this paper, we develop a system prototype to reduce mileage fraud on the used car markets. Our work demonstrates the feasibility of a trusted system of records for (vehicle) data such as mileage data using a distributed database based on the public Ethereum network and smart contracts. We have identified eight requirements that are fulfilled by the prototype and the functional logic and design of thesolution can be reproduced to any other application area characterized by a lack of trust between actors or by the absence of a trusted central authority. However, the developed prototype suffers from similar limitations and challenges as the technology itself. Low throughput causes limitations in scalability and transaction costs are unpredictable. Further development of the blockchain technology and considering more cost‐efficient consensus mechanisms will address these issues.
Md. Ashraf Uddin, Andrew Stranieri, Iqbal Gondal, Venki Balasubramanian
Blockchain technologies recently emerging for eHealth, can facilitate a secure, decentralized and patient-driven, record management system. However, Blockchain technologies cannot accommodate the storage of data generated from IoT devices in remote patient management (RPM) settings as this application requires a fast consensus mechanism, careful management of keys and enhanced protocols for privacy. In this paper, we propose a Blockchain leveraged decentralized eHealth architecture which comprises three layers: (1) The Sensing layer – Body Area Sensor Networks include medical sensors typically on or in a patient body transmitting data to a smartphone. (2) The NEAR processing layer – Edge Networks consist of devices at one hop from data sensing IoT devices. (3) The FAR processing layer – Core Networks comprise Cloud or other high computing servers). A Patient Agent (PA) software replicated on the three layers processes medical data to ensure reliable, secure and private communication. The PA executes a lightweight Blockchain consensus mechanism and utilizes a Blockchain leveraged task-offloading algorithm to ensure patient’s privacy while outsourcing tasks. Performance analysis of the decentralized eHealth architecture has been conducted to demonstrate the feasibility of the system in the processing and storage of RPM data.
In this paper, a blockchain-based data sharing and access control system is proposed, for communication between the Internet of Things (IoT) devices. The proposed system is intended to overcome the issues related to trust and authentication for access control in IoT networks. Moreover, the objectives of the system are to achieve trustfulness, authorization, and authentication for data sharing in IoT networks. Multiple smart contracts such as Access Control Contract (ACC), Register Contract (RC), and Judge Contract (JC) are used to provide efficient access control management. Where ACC manages overall access control of the system, and RC is used to authenticate users in the system, JC implements the behavior judging method for detecting misbehavior of a subject (i.e., user). After the misbehavior detection, a penalty is defined for that subject. Several permission levels are set for IoT devices’ users to share services with others. In the end, performance of the proposed system is analyzed by calculating cost consumption rate of smart contracts and their functions. A comparison is made between existing and proposed systems. Results show that the proposed system is efficient in terms of cost. The overall execution cost of the system is 6,900,000 gas units and the transaction cost is 5,200,000 gas units.
Muhammad Ajmal Azad, Samiran Bag, Feng Hao, Andrii Shalaginov
The Internet of Things (IoT) is the network of connected computing devices that have the ability to transfer valued data between each other via the Internet without requiring human intervention. In such a connected environment, the social IoT (SIoT) has become an emerging trend where multiple IoT devices owned by users support communication within a social circle. Trust management in the SIoT network is imperative as trusting the information from compromised devices could lead to serious compromises within the network. It is important to have a mechanism where the devices and their users evaluate the trustworthiness of other devices and users before trusting the information sent by them. The privacy preservation, decentralization, and self-enforcing management without involving trusted third parties are the fundamental challenges in designing a trust management system for SIoT. To fulfill these challenges, this article presents a novel framework for computing and updating the trustworthiness of participants in the SIoT network in a self-enforcing manner without relying on any trusted third party. The privacy of the participants in the SIoT is protected by using homomorphic encryption in the decentralized setting. To achieve the properties of self-enforcement, the trust score of each device is automatically updated based on its previous trust score and the up-to-date tally of the votes by its peers in the network with zero-knowledge proofs (ZKPs) to enforce that every participant follows the protocol honestly. We evaluate the performance of the proposed scheme and present evaluation benchmarks by prototyping the main functionality of the system. The performance results show that the system has a linear increase in computation and communication overheads with more participants in the network. Furthermore, we prove the correctness, privacy, and security of the proposed system under a malicious adversarial model.
Transgender community face serious socio-economic predicaments due to the discrepancy between their current gender expression and assigned gender identity at birth. Even though, a considerable amount work have been done to protect their basic human rights such as security, equality and social acceptance; trans people are still large victims of hate related crimes. With GDPR and other data protection laws and policies in place, now it is ever more important to protect the confidentiality of gender change information as well as to establish technical solutions that can prevent from inferring any sense of gender change from historical data. In this context, distributed ledger technologies such as blockchain present great opportunities for information integrity, security, privacy and access. However, at the same time provenance information extracted from immutable blockchain can be exploited to infer gender change. Addressing this paradox here we propose recommendations for managing gender change information in the blockchain environment in context of present sociopolitical, legislative and technical challenges associated with gender change.
Blockchain serves as an immutable ledger which allows transactions to take place in a decentralized manner. The proposed solution incorporates the use of Blockchain for Supply Chain Management of Relief Materials after a Disaster. The proposed solution also provides many additional features when compared to the current system like being tamper-proof, providing anonymity and accountability to the users. Our solution incorporates a box-in-box approach whilst keeping tabs via tamper-proof barcodes. Barcodes will be frequently scanned at designated locations. Users also have the facility to track the donated items online through a dedicated website. For blockchain infrastructure, we have used Hyperledger Fabric.
Permission delegation in access control provides the subject with a second method to obtain object permissions in addition to permission granting. It is especially applicable when the owner and manager of the object are inconsistent. With the development of the Internet of Things, there are more and more scenes where object owners and managers are inconsistent, but the research on permission delegation in access control based on blockchain is not perfect. Therefore, once implemented in these blockchain-based access control algorithms, the permission delegation tends to have an unauthorized access. Based on the analysis of the causes for the unauthorized access vulnerability, this paper proposes a token-constrained permission delegation algorithm (TCPDA), which converts the access control policy corresponding to permissions into constraints for permission use, embeds the constraints in the permission token, and forms constraints on the transfer of tokens. Only subjects that meet the constraint conditions can receive tokens, thereby solving unauthorized access vulnerability caused by permission delegation. Since not all access control models can transform strategies into constraints and integrate them into blockchain tokens, this paper also proposes a permission delegation algorithm for decision-making entities to make desirable decisions. Finally, the security analysis shows that the two proposed schemes can overcome the unauthorized access vulnerability caused by permission delegation, and the algorithm performance is analyzed through experiments.
Rustam Pirmagomedov, Aleksandr Ometov, Dmitri Moltchanov, Xi Lu · 9 authors
Wireless traffic produced by modern mobile devices displays high temporal and spatial dynamics as users spontaneously engage in collective applications where a significant portion of generated data remains localized. As a result, conventional service provisioning approaches may no longer be sufficient in beyond fifth generation (B5G) systems. The challenge of increased dynamics on the access networks can be mitigated with moving cells. However, the deployment time of these temporary serving entities may lag behind the service demand lifetime. Another viable solution to offload excessive cellular traffic is to rely upon locally available radio resources offered by user devices via direct mmWave-based mesh interworking. An important challenge in such systems is related to the incentivization of users to partake in collaborative resource sharing. To leverage multi-hop mesh capabilities, we propose the use of emerging blockchain technology that offers cryptographically-strong accounting while maintaining the anonymity of the participants. With system-level evaluations, we demonstrate that the utilization of mobile blockchain methods allows for a non-incremental improvement in the offloading gains. This demonstrates the potential of the outlined proposal for becoming a successful mechanism in the emerging B5G systems.
When a user registers a digital service, the service provider often asks for the user's personal information, such as name, phone number and so on. With the digital services gradually becoming an indispensable part of our lives, the abuse and misappropriation of personal information have caused people's attention to the protection of it. At present, The technical schemes for personal information protection mainly focus on preventing information leakage. We provide protection in a different way: propose a traceable method for personal information registration based on blockchain, which can distinguish the service provider obtained the information legally or illegally, by storing the personal information transaction records on the blockchain. The proposed method includes both direct and third-party personal information transaction scenarios. In different scenarios, the user will send the encrypted personal information data or authorization file to the service provider. After the transaction record is confirmed to be on the blockchain, the certification center will assist the service provider to decrypt the personal information. With this method, users can clearly understand which personal information has been delivered to which service providers. Moreover, the user's personal information data involving privacy are not stored on or transmitted through the blockchain. Hence, there is no additional risk of information disclosure, so as to achieve the purpose of personal information protection. Additionally, we analyze the proposed method using Kailar logic, and conduct a transaction performance simulation using NS-3. It shows that our method has properties of privacy, reliability, and accountability, and can meet the transaction performance requirements under the practical application scene.
In recent years, cryptocurrencies implemented on top of Blockchains became very popular, with Bitcoin as the most prominent example. However, novel Blockchain-based platforms such as Ethereum also support distributed applications beyond cryptocurrencies through so-called smart contracts. Technically, smart contracts are programs, whose code and execution state is stored in the Blockchain, inherently featuring the ability to transfer (electronic) money during their execution. In this Bachelor thesis, we investigate how smart contracts can be used to implement a distributed crowdsensing application for tracking mobile objects by a crowd of privately owned mobile devices. Such a system could be used, for instance, to nd lost or stolen objects, such as keys, vehicles (cars, bicycles, . . . ), or pets tagged with short-range radio transmitters implemented using readily available Bluetooth or RFID technology. These objects can then be detected by smartphones of private users in the vicinity of the object, effectively implementing a huge sensor network covering many parts of the world without any upfront investments by a central entity. Although highly attractive, implementing a crowdsensing application on top of a Blockchain platform such as Ethereum comes with several challenges. First of all, users need incentives to participate in searching for mobile objects. A natural incentive is a monetary reward that participants automatically receive through the smart contract when reporting sightings (timestamped positions) of wanted objects. However, this directly brings up the problem of malicious participants (attackers) who try to get the reward without actually executing the work of searching for the object by simply reporting fake positions. Therefore, one major goal of this Bachelor thesis is to counter such attacks by proposing effective counter-measures, and implementing and evaluating them for the Ethereum platform. In detail, we propose a basic reputation-based approach for detecting fake positions which judges each sighting made by a mobile devices according to the reputation of that device, implemented by a smart contract. Furthermore, advanced attacks are identified compromising the basic reputation-based approach and effective counter-measures to these advanced attacks are proposed. Identified advanced attacks include reputation farming, where the attacker tries to aggregate reputation first before launching the attack, and the so-called copycat attack, where the attacker simply copies already submitted valid sightings form honest participants, making his fake positions indistinguishable from valid positions. Our evaluations analyses the monetary cost of executing smart contracts with and without our security mechanisms. The results show that the overhead included by our reputation-based approach is at maximum 45% of the cost of a smart contract without implemented security mechanisms.
Big data sharing in Cyber-Physical-Social Systems (CPSSs) relies on wireless transmission between numerous devices, causing a serious scarcity of radio spectrum resources. Although license-free spectrum access has great potential to alleviate the growing scarcity of spectrum resources, spectrum competition is more intense due to lower access requirements. A blockchain technology may solve this competition problem by introducing a dynamic cycle of “competition-verification-synchronization-competition”. In this paper, we propose a general framework for license-free spectrum resource management in CPSSs based on blockchain technologies and smart contracts. The management framework is mainly used for edge computing of non-real-time data. In particular, we divide spectrum of a local cell into multiple channels and each channel corresponds to a blockchain. Then, we propose a blockchain-KM protocol that may improve transaction processing speed without losing typical attributes of a general blockchain. For the proposed Blockchain-KM protocol, the entire private chain becomes a multi-ring blockchain and users rely on mining or leasing to access wireless spectrum. Different from the traditional mining process, the reward in our mining process is not only virtual currency but also a spectrum access license. Once a miner obtain a spectrum access license, it will exploit the license to transmit its messages over wireless links. Also, the miner may sell its license by an auction when it does not want to transmit messages. In the auction, we introduce a virtual currency, called as Xcoin, for spectrums or other trading (e.g., paid edge computing services).
Tao Feng, Hongmei Pei, Rong Ma, Youliang Tian · 5 authors
Data privacy is important to the security of our society, and enabling authorized users to query this data efficiently is facing more challenge. Recently, blockchain has gained extensive attention with its prominent characteristics as public, distributed, decentration and chronological characteristics. However, the transaction information on the blockchain is open to all nodes, the transaction information update operation is even more transparent. And the leakage of transaction information will cause huge losses to the transaction party. In response to these problems, this paper combines hierarchical attribute encryption with linear secret sharing, and proposes a blockchain data privacy protection control scheme based on searchable attribute encryption, which solves the privacy exposure problem in traditional blockchain transactions. The user’s access control is implemented by the verification nodes, which avoids the security risks of submitting private keys and access structures to the blockchain network. Associating the private key component with the random identity of the user node in the blockchain can solve the collusion problem. In addition, authorized users can quickly search and supervise transaction information through searchable encryption. The improved algorithm ensures the security of keywords. Finally, based on the DBDH hypothesis, the security of the scheme is proved in the random prediction model.
The evolution of Artificial Intelligence (AI) from centralized models toward decentralized architectures has fundamentally reshaped the paradigms of data management, ownership, and governance. In traditional AI ecosystems, data is consolidated within centralized repositories for model training and analytics, resulting in challenges related to privacy, latency, and compliance with regulatory frameworks. Decentralized AI architectures encompassing federated learning, edge AI, swarm intelligence, and blockchain-based frameworks offer a transformative alternative that aligns technological innovation with distributed data governance principles. These architectures enable AI systems to learn collaboratively across multiple nodes or organizations without transferring raw data, ensuring data sovereignty and compliance with global data protection mandates such as GDPR and CCPA. This review examines how decentralized AI architectures influence distributed data governance by promoting transparency, trust, and accountability in multi-party data ecosystems. The integration of AI with blockchain and distributed ledger technologies provides immutable audit trails and decentralized identity management, enabling verifiable governance across federated networks. Moreover, privacy-preserving techniques such as differential privacy, homomorphic encryption, and secure multiparty computation empower organizations to perform analytics on encrypted datasets while maintaining compliance with ethical and legal data-handling standards. Through a synthesis of academic research and real-world applications, the review highlights the significant advantages of decentralized AI, including enhanced privacy assurance, reduced systemic risks, and improved collaboration among data stakeholders. However, the transition toward decentralized intelligence introduces new challenges related to interoperability, communication overhead, and model convergence in distributed environments. Ensuring fairness, accountability, and explainability within federated systems remains a critical governance issue, as decentralized decision-making increases complexity in auditing and oversight. Furthermore, the governance of AI models themselves rather than just data poses emerging regulatory and ethical questions in globally interconnected ecosystems.
The insurance business plays a quite significant role in people's lives, but in the process of claim settlement, there are still various frauds such that the insurance companies' refusal to compensate or customers' malicious fraud to obtain compensation. Therefore, it is very important to ensure fair and just claims. In this paper, by combining the blockchain technology and the ciphertext-policy attribute-based encryption system, we build a scheme for secure storage and update for insurance records under the InterPlanetary File System (IPFS) storage environment in the insurance system. In this scheme, we use the fog node to outsource encryption of insurance records to improve the efficiency of the staff; In addition, we store encrypted insurance records on IPFS to ensure the security of the storage platform and avoid the single point failure of the centralized mechanism. In addition, we use the immutability of the blockchain to achieve the non-repudiation of both insurance companies and the client. The security proof shows that the proposed scheme can achieve selective security against selected keyword attacks. Our scheme is efficient and feasible under performance analysis and real data set experiments.
Aisha Zahid Junejo, Manzoor Ahmed, Abdullah Abdulrehman
Blockchain decentralization not only ensures transparency of transactions to eliminate need of trusting third party, but also makes the transactions of the network to be publicly accessible to all the participating peers in the network. As a result, data anonymity and confidentiality are compromised making several business enterprises and industrialists hesitant to adopt the technology. Although research community has proposed various privacy-preserving solutions for blockchain, however, they still lack in efficiency resulting in distrust of industries in opting for the technology. This study is conducted for contributing to the existing body of knowledge corresponding to privacy in blockchains. The fundamental goal of this study is to delve into privacy vulnerabilities of the blockchain network in a permissionless setting by identifying non-trivial roots of factors causing privacy breach in blockchain and presenting limitation of existing privacy preserving mechanisms. Studies with superficial comparison of privacy preserving techniques are available in literature but a detailed and in-depth analysis of their limitations and causes of privacy breach in blockchain is yet not done. Therefore, in this paper we first present comprehensive analysis of various privacy breaching factors of the blockchain networks. Next, we discuss existing cryptographic and non-cryptographic solutions in literature. We found out that these existing privacy preserving mechanisms have their own set of limitations and hence are inefficient at current point of time. The existing privacy preserving mechanisms need further consideration of the research community before they’re widely adopted and benchmarked. Therefore, in the end, we identified some future directions that need to be addressed to model an efficient privacy preserving mechanism for wider adoption of the blockchain technology.