Blockchain-based systems open new opportunities for the IT business and society if designed and implemented properly. One of the essential challenges on their way to production readiness are severe privacy concerns and hence stringent compliance regulations. In this paper, we investigate how different privacy-preserving techniques leveraging zero-knowledge proof protocols and anonymous credentials can be used to tackle this issue on the example of a car sharing use case. Our approach considers a blockchain ecosystem enabling different parties including governmental authorities and automobile industry partners to cooperate ensuring end user privacy protection. The implemented proof-of-concept is leveraging Ethereum ZoKrates and Indy technologies respectively. This project is a joint initiative of T-Systems Multimedia Solutions GmbH and BMW Group.
The sensing-as-a-service (SaaS) model has been explored to address the challenge of intractability of managing a large number of sensors faced by future smart cities. However, how to effectively share sensor data without compromising confidentiality, privacy protection, and fair trading without third parties is one of critical issues that must be solved in the SaaS in smart cities. While blockchain shows promise in solving these issues, the existing blockchain-based data sharing (BBDS) systems are difficult to apply to SaaS in smart cities because of many unresolved issues such as requiring a customized blockchain, huge storage, communication and computation costs, and dependence on a third party to achieve fair trading. We propose a BBDS system model with its security requirements before we present a concrete construction by combining -protocol, Paillier encryption scheme, and any secure symmetrical encryption and signature schemes. To demonstrate the utility of our proposed BBDS system, we present a security analysis and compare our system with other solutions. We implement the prototype in Remix to analyze the gas cost, and we conduct experiments to evaluate the communication and computation costs of the BBDS system using symmetric encryption (advanced encryption standard (AES)) and a signature scheme (elliptic curve digital signature algorithm (ECDSA)).
Federated learning enables the development of a machine learning model among\ncollaborating agents without requiring them to share their underlying data.\nHowever, malicious agents who train on random data, or worse, on datasets with\nthe result classes inverted, can weaken the combined model. BlockFLow is an\naccountable federated learning system that is fully decentralized and\nprivacy-preserving. Its primary goal is to reward agents proportional to the\nquality of their contribution while protecting the privacy of the underlying\ndatasets and being resilient to malicious adversaries. Specifically, BlockFLow\nincorporates differential privacy, introduces a novel auditing mechanism for\nmodel contribution, and uses Ethereum smart contracts to incentivize good\nbehavior. Unlike existing auditing and accountability methods for federated\nlearning systems, our system does not require a centralized test dataset,\nsharing of datasets between the agents, or one or more trusted auditors; it is\nfully decentralized and resilient up to a 50% collusion attack in a malicious\ntrust model. When run on the public Ethereum blockchain, BlockFLow uses the\nresults from the audit to reward parties with cryptocurrency based on the\nquality of their contribution. We evaluated BlockFLow on two datasets that\noffer classification tasks solvable via logistic regression models. Our results\nshow that the resultant auditing scores reflect the quality of the honest\nagents' datasets. Moreover, the scores from dishonest agents are statistically\nlower than those from the honest agents. These results, along with the\nreasonable blockchain costs, demonstrate the effectiveness of BlockFLow as an\naccountable federated learning system.\n
Mohammad Hossein Chinaei, Hassan Habibi Gharakheili, Vijay Sivaraman
Verification of data generated by wearable sensors is increasingly becoming of concern to health service providers and insurance companies. These devices are typically vulnerable to a wide range of cybersecurity attacks, attempting to manipulate sensing data. Most of these disastrous attacks would remain undetected since neither healthcare servers nor Internet-of-Things (IoT) sensors are aware of the existence of attackers in the middle of communication. Thus, there is a need for a verification framework that various authorities can request a verification service for the local network data of a target IoT device. In this article, we leverage blockchain as a distributed platform to realize an on-demand verification scheme. This allows authorities to automatically transact with connected devices for witnessing services. A public request is made for witness statements on the data of a target IoT that is transmitted on its local network, and subsequently, devices (in close vicinity of the target IoT) offer witnessing service. Our contributions are threefold: 1) we develop a system architecture based on blockchain and smart contract that enables authorities to dynamically avail a verification service for data of a subject device from a distributed set of witnesses which are willing to provide (in a privacy-preserving manner) their local wireless measurement in exchange of monetary return; 2) we then develop a method to optimally select witnesses in such a way that the verification error is minimized subject to monetary cost constraints; and 3) finally, we evaluate the efficacy of our scheme using real Wi-Fi session traces collected from a five-storeyed building with more than thirty access points, representative of a hospital. According to the current pricing schedule of the Ethereum public blockchain, our scheme enables healthcare authorities to verify data transmitted from a typical wearable device with the verification error of the order 0.01% at cost of less than $ 2 for 1-hr witnessing service.
Proliferation of IoT devices in society demands a renewed focus on securing the use and maintenance of such systems. IoT-based systems will have a great impact on society and therefore such systems must have guaranteed resilience. We introduce cryptographic-based building blocks that strive to ensure that distributed IoT networks remain in a healthy condition throughout their lifecycle. Our presented solution utilizes deterministic and interlinked smart contracts on the Ethereum blockchain to enforce secured management and maintenance for hardened IoT devices. A key issue investigated is the protocol development for securing IoT device deployments and means for communicating securely with devices. By supporting values of openness, automation, and provenance, we can introduce novel means that reduce the threats of surveillance and theft, while also improving operator accountability and trust in IoT technology.
Activity-tracking applications and location-based services using short-range communication (SRC) techniques have been abruptly demanded in the COVID-19 pandemic, especially for automated contact tracing. The attention from both public and policy keeps raising on related practical problems, including \textit{1) how to protect data security and location privacy? 2) how to efficiently and dynamically deploy SRC Internet of Thing (IoT) witnesses to monitor large areas?} To answer these questions, in this paper, we propose a decentralized and permissionless blockchain protocol, named \textit{Bychain}. Specifically, 1) a privacy-preserving SRC protocol for activity-tracking and corresponding generalized block structure is developed, by connecting an interactive zero-knowledge proof protocol and the key escrow mechanism. As a result, connections between personal identity and the ownership of on-chain location information are decoupled. Meanwhile, the owner of the on-chain location data can still claim its ownership without revealing the private key to anyone else. 2) An artificial potential field-based incentive allocation mechanism is proposed to incentivize IoT witnesses to pursue the maximum monitoring coverage deployment. We implemented and evaluated the proposed blockchain protocol in the real-world using the Bluetooth 5.0. The storage, CPU utilization, power consumption, time delay, and security of each procedure and performance of activities are analyzed. The experiment and security analysis is shown to provide a real-world performance evaluation.
In agriculture, farmers are the most important entity. For supporting farmers in increasing productivity and efficiency, the government offers subsidies, loans, insurances, and so on. This paper explores the usage of Blockchain technology for securing farmer's data in the Indian scenario. The farmer needs to register through the multiple official registration systems for availing different schemes and information provided by the country. The personnel and crop-based details of each farmer are collected at the time of registration. The filing also helps in providing better services to farmers like connecting farmers and traders to ensure a fair price for quality crops, advice to farmers of agricultural practices and location. In this paper, a blockchain-based farmer's data securing system is proposed to provide data provenance and transparency of the information entered in the system. While registering, the data is collected, and it is verified. A single verified record of farmers accessed by various government agriculture departments were designed using the Hyperledger fabric framework.
In today's digital world, information is exchanged between systems and it is expected that each interaction / transaction between the systems is secure and reliable. Block chain technology can be applied to protect the security of healthcare data. It is important to have access control mechanism that allows only authorized persons to share the medical data. In this work, we introduce a blockchain based system for securely sharing health records. The system includes a smart contract based data protection and accessibility mechanism.
For the construction of digital government,this paper proposes a privacy protection scheme for personal credit by combining smart contract and homomorphic encryption technology in blockchain.This scheme uses Paillier homomorphic encryption algorithm to set the blind reading permission of personal credit information access,so as to enable credit access users to create an automatic condition matching contract and make reasonable decisions when users cannot obtain the clear text of personal credit information.Under this scheme,the credit system cannot infer the access requirements of credit access users,so the privacy of personal credit information is protected from multiple perspectives.Analysis results show that the proposed scheme can effectively protect personal credit privacy with reduced running overhead and improved security.
Vehicular Ad Hoc Network (VANET) is a mobile network formed by vehicles, road side units, and other in-frastructures that enable communication between the nodes to improve road safety and traffic control. While this technology promises great benefits to drivers, there are many security and privacy concerns that must be addressed before it can be fully adopted. It is essential to ensure that vehicles participating in the network are authenticated and held accountable in case of misbehaviour. On the other hand, there should be adequate mechanisms for preserving the privacy of vehicles and drivers, so they are protected against unauthorized tracking and release of private information. Many current VANET technologies also depend on a central trusted authority that becomes a single point of failure for the network. In this paper, we propose a new blockchain based decentralized authentication approach for VANET. In this scheme vehicles maintain conditional anonymity in the network and their real identities can only be revealed to authorized entities. Using the blockchain technology, we create a distributed framework and maintain an immutable record of the data, strengthening the integrity of the system. We use the Hyperledger Fabric, a permissioned blockchain technology, to implement our approach and compare its performance to the traditional PKI based method for VANET authentication.
Andrew R. Short, Helen C. Leligou, Michael Papoutsidakis, Efstathios Theocharis
The potential of Federated Learning (FL) deployment increases rapidly as the number of connected devices increases, the value of artificial intelligence is recognized and networking technologies and edge computing evolves. However, as in any distributed system, a set of security issues arise in FL systems. In this paper, we discuss the use of blockchain technology to address diverse security aspects of FL systems and focus on the model poisoning attack for which we propose a novel Blockchain-based defense scheme. An assessment using data from the MNIST database has shown that the proposed approach, which has been designed to be implemented on blockchain technology, offers significant protection against adversaries attempting model poisoning attacks. The approach adopts a novel algorithm for evaluating the model updates, by verifying each model update separately against a verification dataset, without requiring information about the training dataset size, which is often unavailable or easily falsified.
Vehicular Ad Hoc networks (VANETs) is a new emerging technology that aims at connecting vehicles using wireless communication technologies. With the emergence of VANETs, new advanced applications have emerged away which aim at enhancing driving safety and traffic management. These applications exploit the huge amount of data, shared between vehicles and infrastructure, through advanced data analysis. Due to resources limitation of vehicles, this increasing volume of data is stored on powerful edge computing servers spread over the VANETs infrastructure. However, these edge servers are not fully trusted, which rise new serious security and privacy challenges regarding the shared data between vehicles. In this work, we propose a new data-sharing scheme that protects the privacy of vehicles and drivers. We base our construction on consortium blockchain, smart contracts and Zero-Knowledge Proofs (ZKP) to propose a decentralized and anonymous data-sharing scheme. In addition, we propose a fine-grained data storage scheme on the top of blockchain, based on publish-subscribe model to enhance the data management. We confirm the efficiency of our scheme through extensive simulations and experiments. The numerical results showed that our protocol achieves a reasonable efficiency while guaranteeing a high level of security.
Nowadays, data is an extremely important asset. Governments around the world encourage big data sharing and trading to flourish the big data economics. However, existing data trading platforms are not fully trusted. Data providers lack control over data trading rights. Meanwhile, the data price model needs further justification. These issues hinder the development of big data trading. In this paper, we propose a blockchain-based decentralized data trading platform, on which data providers can better control data trading. Accordingly, we design smart contracts for distributed data trading and set rules to assign data rewards in trading. The contract guarantees the efficiency of data matching and fair reward distribution. We also adopt proxy re-encryption to ensure the security of data transmission. We verify the security, availability, and efficiency of the proposed big data trading platform.
The rapid advancements in autonomous technologies have paved way for vehicular networks. In particular, Vehicular Ad-hoc Network (VANET) forms the basis of the future of Intelligent Transportation System (ITS). ITS represents the communication among vehicles by acquiring and sharing the data. Though congestion control is enhanced by Internet of Vehicles (IoV), there are various security criteria where entire communication can lead to many security and privacy challenges. A blockchain can be deployed to provide the IoV devices with the necessary authentication and security feature for the transfer of data. Blockchain based IoV mechanism eliminates the single source of failure and remains secure at base despite having strong security, the higher level layers and applications are susceptible to attacks. Artificial Intelligence (AI) has the potential to overcome several vulnerabilities of current blockchain technology. In this paper, we propose an AI-Powered Blockchain which provides auto coding feature for the smart contracts making it an intelligent contract. Moreover, it speeds up the transaction verification and optimises energy consumption. The results show that intelligent contracts provide higher security compared to smart contracts considering range of different scenarios.
Dawid Połap, Gautam Srivastava, Alireza Jolfaei, Reza M. Parizi
In today's technological climate, users require fast automation and digitization of results for large amounts of data at record speeds. Especially in the field of medicine, where each patient is often asked to undergo many different examinations within one diagnosis or treatment. Each examination can help in the diagnosis or prediction of further disease progression. Furthermore, all produced data from these examinations must be stored somewhere and available to various medical practitioners for analysis who may be in geographically diverse locations. The current medical climate leans towards remote patient monitoring and AI-assisted diagnosis. To make this possible, medical data should ideally be secured and made accessible to many medical practitioners, which makes them prone to malicious entities. Medical information has inherent value to malicious entities due to its privacy-sensitive nature in a variety of ways. Furthermore, if access to data is distributively made available to AI algorithms (particularly neural networks) for further analysis/diagnosis, the danger to the data may increase (e.g., model poisoning with fake data introduction). In this paper, we propose a federated learning approach that uses decentralized learning with blockchain-based security and a proposition that accompanies that training intelligent systems using distributed and locally-stored data for the use of all patients. Our work in progress hopes to contribute to the latest trend of the Internet of Medical Things security and privacy.
Siyuan Wang, Xuehan Zhang, Wei Yu, Kai Hu · 5 authors
A smart contract is a computable protocol that automatically enforces contract terms in a computer, transforming real-world contract terms into digital promises of the virtual world. Early smart contracts have been stuck in the theoretical phase due to the lack of a credible execution environment and the means to control digital assets. With the emergence of blockchain technology, it has solved the problems mentioned above. Smart contracts are stored on blockchain, ensuring the credibility of contract execution through the joint execution of contracts by the various nodes in the blockchain network. However, the current technology of blockchain-based smart contracts is still not mature enough and faces many major challenges. Among them, the extensibility and performance of smart contracts are the most important and most concerned ones. This paper studies the extensibility and performance of smart contracts by combining blockchain-based smart contracts with cloud technologies to address the extensibility and performance issues of smart contracts. Combined with micro-service technology, a new type of smart contract architecture is proposed, and then the key technologies in each layer of the architecture are further studied.
Muhammad Habib ur Rehman, Khaled Salah, Ernesto Damiani, Davor Svetinović
Federated learning (FL) is the collaborative machine learning (ML) technique whereby the devices collectively train and update a shared ML model while preserving their personal datasets. FL systems solve the problems of communication-efficiency, bandwidth-optimization, and privacy-preservation. Despite the potential benefits of FL, one centralized shared ML model across all the devices produce coarse-grained predictions which, in essence, are not required in many application areas involving personalized prediction services. In this paper, we present a novel concept of fine-grained FL to decentralize the shared ML models on the edge servers. We then present a formal extended definition of fine-grained FL process in mobile edge computing systems. In addition, we define the core requirements of fine-grained FL systems including personalization, decentralization, fine-grained FL, incentive mechanisms, trust, activity monitoring, heterogeneity and context-awareness, model synchronization, and communication and bandwidth-efficiency. Moreover, we present the concept of blockchain-based reputation-aware fine-grained FL in order to ensure trustworthy collaborative training in mobile edge computing systems. Finally, we perform the qualitative comparison of proposed approach with state-of-the-art related work and found some promising initial results.
The celebrated result of Fischer, Lynch and Paterson is the fundamental lower\nbound for asynchronous fault tolerant computation: any 1-crash resilient\nasynchronous agreement protocol must have some (possibly measure zero)\nprobability of not terminating. In 1994, Ben-Or, Kelmer and Rabin published a\nproof-sketch of a lesser known lower bound for asynchronous fault tolerant\ncomputation with optimal resilience against a Byzantine adversary: if $n\\le 4t$\nthen any t-resilient asynchronous verifiable secret sharing protocol must have\nsome non-zero probability of not terminating.\n Our main contribution is to revisit this lower bound and provide a rigorous\nand more general proof. Our second contribution is to show how to avoid this\nlower bound. We provide a protocol with optimal resilience that is almost\nsurely terminating for a strong common coin functionality. Using this new\nprimitive we provide an almost surely terminating protocol with optimal\nresilience for asynchronous Byzantine agreement that has a new fair validity\nproperty. To the best of our knowledge this is the first asynchronous Byzantine\nagreement with fair validity in the information theoretic setting.\n
Yanhong Xu, Reihaneh Safavi–Naini, Khoa Nguyen, Huaxiong Wang
Policy-based signatures (PBS) were proposed by Bellare and Fuchsbauer (PKC 2014) to allow an {\em authorized} member of an organization to sign a message on behalf of the organization. The user's authorization is determined by a policy managed by the organization's trusted authority, while the signature preserves the privacy of the organization's policy. Signing keys in PBS do not include user identity information and thus can be passed to others, violating the intention of employing PBS to restrict users' signing capability. In this paper, we introduce the notion of {\em traceability} for PBS by including user identity in the signing key such that the trusted authority will be able to open a suspicious signature and recover the signer's identity should the needs arise. We provide rigorous definitions and stringent security notions of traceable PBS (TPBS), capturing the properties of PBS suggested by Bellare-Fuchsbauer and resembling the "full traceability" requirement for group signatures put forward by Bellare-Micciancio-Warinschi (Eurocrypt 2003). As a proof of concept, we provide a modular construction of TPBS, based on a signature scheme, an encryption scheme and a zero-knowledge proof system. Furthermore, to demonstrate the feasibility of achieving TPBS from concrete, quantum-resistant assumptions, we give an instantiation based on lattices.
Internet of Vehicles (IoVs) is highly characterized by collaborative environment data sensing, computing and processing. Emerging Big Data and Artificial Intelligence (AI) technologies show significant advantages and efficiency for knowledge sharing among intelligent vehicles. However, it is challenging to guarantee the security and privacy of knowledge during the sharing process. Moreover, conventional AI-based algorithms cannot work properly in distributed vehicular networks. In this paper, a hierarchical blockchain framework and a hierarchical federated learning algorithm are proposed for knowledge sharing, by which vehicles learn environmental data through machine learning methods and share the learning knowledge with each others. The proposed hierarchical blockchain framework is feasible for the large scale vehicular networks. The hierarchical federated learning algorithm is designed to meet the distributed pattern and privacy requirement of IoVs. Knowledge sharing is then modeled as a trading market process to stimulate sharing behaviours, and the trading process is formulated as a multi-leader and multi-player game. Simulation results show that the proposed hierarchical algorithm can improve the sharing efficiency and learning quality. Furthermore, the blockchain-enabled framework is able to deal with certain malicious attacks effectively.
Erik Boetto, Davide Golinelli, Gherardo Carullo, Maria Pia Fantini
Frauds and misconducts have been common in the history of science. Recent events connected to the COVID-19 pandemic have highlighted how the risks and consequences of this are no longer acceptable. Two papers, addressing the treatment of COVID-19, have been published in two of the most prestigious medical journals. In both, the authors declared to have analysed electronic records from a private corporation, which apparently collected data of tens of thousands of patients, coming from thousands of hospitals. Both papers have been retracted a few weeks later. When such events happen, the confidence of the population in scientific research is likely to be weakened. The objective of this paper is to highlight how the current system endangers not only the reliability of scientific research, but also the very foundations of the trust system on which modern healthcare is based. Having shed the light on the dangers of a system without appropriate monitoring, we propose to improve the research process using the promising aspects of the distributed ledger technology which, thanks to the characteristics of immutability, decentralization and transparency, appears among the best solutions to avoid the repetition of the mistakes linked to the recent and past history of research.