Rafael Belchior, Limaris Torres, Jonas Pfannschmid, André Vasconcelos · 5 authors
Distributed ledger technology (DLT) provides decentralized and tamper-resistant data storage, replicated among mutually untrusting participants. With the advancement of this technology, different privacy-preserving blockchains have been proposed, such as Corda, Hyperledger Fabric, and Digital Asset's Canton. These distributed ledgers only provide \emph{partial consistency}, which implies that participants can view the same ledger differently. A \emph{view} represents the states of a blockchain available to a particular stakeholder. The combination of views forms an integrated view that represents a consistent global state shared by all participants. This paper introduces BUNGEE (Blockchain UNifier view GEnErator), the first DLT view generator, to allow capturing DLT snapshots, constructing views, and performing arbitrary operations on those, such as integrating views. Creating and integrating views allows interesting applications, such as stakeholder-centric snapshots for audits, cross-chain analysis, blockchain migration, and data analytics.
With the development of mobile edge computing (MEC) and blockchain-based federated learning (BCFL), a number of studies suggest deploying BCFL on edge servers. In this case, resource-limited edge servers need to serve both mobile devices for their offloading tasks and the BCFL system for model training and blockchain consensus in a cost-efficient manner without sacrificing the service quality to any side. To address this challenge, this paper proposes a resource allocation scheme for edge servers, aiming to provide the optimal services with the minimum cost. Specifically, we first analyze the energy consumed by the MEC and BCFL tasks, and then use the completion time of each task as the service quality constraint. Then, we model the resource allocation challenge into a multivariate, multi-constraint, and convex optimization problem. To solve the problem in a progressive manner, we design two algorithms based on the alternating direction method of multipliers (ADMM) in both the homogeneous and heterogeneous situations with equal and on-demand resource distribution strategies, respectively. The validity of our proposed algorithms is proved via rigorous theoretical analysis. Through extensive experiments, the convergence and efficiency of our proposed resource allocation schemes are evaluated. To the best of our knowledge, this is the first work to investigate the resource allocation dilemma of edge servers for BCFL in MEC.
Niki Hrovatin, Aleksandar Tošić, Michaël Mrissa, Branko Kavšek
Currently, the computational power present in the sensors forming a wireless sensor network (WSN) allows for implementing most of the data processing and analysis directly on the sensors in a decentralized way. This shift in paradigm introduces a shift in the privacy and security problems that need to be addressed. While a decentralized implementation avoids the single point of failure problem that typically applies to centralized approaches, it is subject to other threats, such as external monitoring, and new challenges, such as the complexity of providing decentralized implementations for data mining algorithms. In this paper, we present a solution for privacy-aware distributed data mining on wireless sensor networks. Our solution uses a permissioned blockchain to avoid a single point of failure in the system. Contracts are used to construct an onion-like structure encompassing the Hoeffding trees and a route. The onion-routed query conceals the network identity of the sensors from external adversaries, and obfuscates the actual computation to hide it from internally compromised nodes. We validate our solution on a use case related to an air quality-monitoring sensor network. We compare the quality of our model against traditional models to support the feasibility and viability of the solution.
Hien Thi Thu Truong, José L. Hernández-Ramos, Juan A. Martínez, Jorge Bernal Bernabé · 7 authors
The increase in the interconnection of physical devices and the emergence of the 5 G paradigm foster the generation and distribution of massive amounts of data. The complexity associated with the management of these data requires a suitable access control approach that empowers citizens to control how their data are shared, so potential privacy issues can be mitigated. While well-known access control models are widely used in web and cloud scenarios, the IoT ecosystem needs to address the requirements of lightness, decentralization, and scalability to control the access to data generated by a huge number of heterogeneous devices. This work proposes CapBlock, a design that integrates a capability-based access control model and blockchain technology for a fully distributed evaluation of authorization policies and generation of access credentials using smart contracts. CapBlock is intended to manage the access to information in federated IoT environments where data need to be managed through access control policies defined by different data providers. The feasibility of CapBlock has been successfully evaluated in the scope of the EU research project IoTCrawler, which aims at building a secure search engine for IoT data in large-scale scenarios.
With the adoption of the 5G network, the exponen-tial increase in the volume of data generated by the Internet of Things (IoT) devices, pushes the system to learn the model locally to support real-time applications. However, it also raises concerns about the security and privacy of local nodes and users. In addition, the approach such as collaborative learning where local nodes participate in the learning process of global model also raise critical concern regarding the cyber resilience of the network architecture. To address these issues, in this article, we identify the research gaps and pro-pose a blockchain and federated learning-enabled distributed secure and privacy-preserving computing architecture for IoT network. The proposed model introduces the lightweight authentication and model training algorithms to build secure and robust system. The proposed model also addresses the reward and penalty issues of the collaborative learning with local nodes and propose a reward system scheme. We con-duct the experimental analysis of the proposed model based on various parametric metrics to assess the effectiveness of the model. The experimental result shows that the proposed model is effective and capable of providing a cyber-resilience system.
With the healthcare system’s ongoing digital transformation and the need for patient data sharing to become an essential step to understanding the patient’s health history, cyber security must stay at the forefront and be made a top priority. As a result, most existing data-sharing systems depend on trusted third parties. As a result, these systems lack interoperability, data fragmentation, integrity, security, and privacy. In our previous work, we designed a framework based on Blockchain to secure patient health records exchange(SPHRS) that is fully controlled by the patient in terms of revoking or granting access and creating access policies for care providers. The framework achieves security by using smart contracts for user identity authentication and verification. The distributed IPFS storage is applied to store the encrypted patient health records and ensure immutability. In addition, NuCypher software takes advantage of a proxy re-encryption protocol to store the encryption and decryption keys securely. In this study, we assess the framework’s performance by testing metrics such as blockchain transactions’ gas consumption, throughput, Average response time, and average. Bytes. Furthermore, the security of the framework is discussed. SPHRS demonstrates how we can establish a novel approach to efficiently secure patient health record sharing. However, it shows a promising result that can potentially transform the digital patient healthcare system.
The traditional centralized data sharing systems have potential risks such as single point of failures and excessive working load on the central node. As a distributed and collaborative alternative, approaches based upon blockchain have been explored recently for Internet of Things (IoTs). However, the access from a legitimate user may be denied without the pre-defined policy and data update on the blockchain could be costly to the owners. In this paper, we first address these issues by incorporating the Accountable Subgroup Multi-Signature (ASM) algorithm into the Attribute-based Access Control (ABAC) method with Policy Smart Contract, to provide a finegrained and flexible solution. Next, we propose a policy-based Chameleon Hash algorithm that allows the data to be updated in a reliable and convenient way by the authorized users. Finally, we evaluate our work by comparing its performance with the benchmarks. The results demonstrate significant improvement on the effectiveness and efficiency.
Jun 1, 2022·2022 IEEE 9th International Conference on Cyber Security and Cloud Computing (CSCloud)/2022 IEEE 8th International Conference on Edge Computing and Scalable Cloud (EdgeCom)
Muhammad Firdaus, Harashta Tatimma Larasati, Kyung-Hyune Rhee
Federated learning (FL) has considerably emerged as a promising solution to enhance user privacy and data security by enabling collaboratively multi-party model learning without exchanging confidential data. Nevertheless, most existing FL approaches still rely on a central server to obtain a global model by collecting all uploaded models from participants, which may lead to several threats from malicious participants and even expose participant privacy. Therefore, to tackle these problems, we proposed a secure FL framework by empowering blockchain to replace the centralized aggregator sever and utilize Differential Privacy (DP) to address various attacks, e.g., membership inference attacks, during the collaborative FL model training process. The proposed framework has been implemented through two scenarios, i.e., blockchain-based FL to form a decentralized system and DP-based FL to construct the randomized privacy protection using the IBM DP Library.
Empowered by promising artificial intelligence, the traditional Internet of Things is evolving into the Artificial Intelligence of Things (AIoT), which is an important enabling technology for Industry 4.0. Collaborative learning is a key technology for AIoT to build machine learning (ML) models on distributed datasets. However, there are two critical concerns of collaborative learning for AIoT: privacy leakage of sensitive data and dishonest computation. Specifically, data contains sensitive information of users, which cannot be openly shared for model learning. Furthermore, to protect the privacy of data or other selfish purposes, participants of collaborative learning may behave dishonestly, submitting dummy data or incorrect model computation. Therefore, it is important to guarantee privacy preservation of data and honest computation on collaborative learning. Our work tackles the two concerns wherein a model demander can securely train ML models with sensitive data and can regulate the computation of participants. To this end, we propose a secure and trusted collaborative learning framework called TrusCL. The framework guarantees privacy preservation via a delicate combination of homomorphic encryption (HE) and differential privacy (DP), achieving the trade-off between efficiency and accuracy. Furthermore, based on blockchain, in our design, the key steps of secure collaborative learning are recorded on blockchain so that malicious behaviors can be effectively tracked and choked in a timely manner to facilitate trusted computation. Experimental results validate the trade-off performance of Trus-CL between model training efficiency and trained model accuracy.
Zicong Hong, Song Guo, Rui Zhang, Li Peng · 6 authors
Payment channel network (PCN) is a promising off-chain technology for blockchain scalability, but it suffers from poor sustainability in practice. In other words, due to the imbalanced transfer in channels, the balance in one direction of channels gradually becomes exhausted until the PCN is rebalanced via a consensus-based rebalancing protocol, during which the involved channels must be suspended. This paper presents Cycle, the first off-chain protocol for a sustainable PCN. It not only keeps the PCN at a balanced level consistently but also avoids the channel freeze incurred by the rebalancing protocol, leading to minimum failed payments and sustained PCN service, respectively. Cycle achieves these benefits based on a novel idea of asynchronous rebalancing. During the normal off-chain running, the participants share the information about their payments and asynchronously rebalance the PCN following the principle that payments along circular channels can cancel each other out. To guarantee security, the protocol resolves the disputes resulting from network latency or malicious participants by a message mechanism for synchronization and a smart contract for arbitration. Moreover, to address the privacy concern during the information sharing, a truncated Laplace mechanism is designed to achieve differential privacy. Finally, we provide a proof-of-concept implementation in Ethereum, over which a real data-based simulation shows that Cycle satisfies 31% more payments than the state-of-the-art technique.
In the context of today's big data era, there is an urgent need for data sharing in various industries. Traditional data sharing schemes are highly centralized and have problems such as single point of failure and data privacy leakage caused by the vulnerability of data storage systems to attackers, and there are also problems such as difficulty in determining data ownership, insufficient granularity of access control, and low transparency of data sharing process. In this paper, an access control model for data sharing based on smart contract and user credit (SCUC-DSAC) is proposed. Based on the consortium blockchain, the attribute-based access control strategy and user credit are combined to provide dynamic and fine-grained access control for users. The data in the model is encrypted and stored in the interstellar file system. The access authorization process is implemented in the smart contract to improve the transparency of the data sharing process. Theoretical and experimental analysis shows that this model meets the functional and security requirements in data sharing scenarios, and the performance of blockchain network is good.
With the development of artificial intelligence, the need for data sharing is becoming more and more urgent. However, the existing data sharing methods can no longer fully meet the data sharing needs. Privacy breaches, lack of motivation and mutual distrust have become obstacles to data sharing. We design a privacy-preserving, decentralized data sharing method based on blockchain smart contracts, named PPDS. To protect data privacy, we transform the data sharing problem into a model sharing problem. This means that the data owner does not need to directly share the raw data, but the AI model trained with such data. The data requester and the data owner interact on the blockchain through a smart contract. The data owner trains the model with local data according to the requester's requirements. To fairly assess model quality, we set up several model evaluators to assess the validity of the model through voting. After the model is verified, the data owner who trained the model will receive reward in return through a smart contract. The sharing of the model avoids direct exposure of the raw data, and the reasonable incentive provides a motivation for the data owner to share the data. We describe the design and workflow of our PPDS, and analyze the security using formal verification technology, that is, we use Coloured Petri Nets (CPN) to build a formal model for our approach, proving its security through simulation execution and model checking. Finally, we demonstrate effectiveness of PPDS by developing a prototype with its corresponding case application.
Mishri Saleh Al-Marshoud, Ali H. Al‐Bayatti, Mehmet Sabır Kiraz
Security and privacy in vehicular ad hoc networks (VANETs) are challenging in terms of Intelligent Transportation Systems (ITS) features. The distribution and decentralisation of vehicles could threaten location privacy and confidentiality in the absence of trusted third parties (TTP)s or if they are otherwise compromised. If the same digital signatures (or the same certificates) are used for different communications, then adversaries could easily apply linking attacks. Unfortunately, most of the existing schemes for VANETs in the literature do not satisfy the required levels of security, location privacy, and efficiency simultaneously. This paper presents a new and efficient end-to-end anonymous key exchange protocol based on Yang et al. 's self-blindable signatures. In our protocol, vehicles first privately blind their own private certificates for each communication outside the mix-zone and then compute an anonymous shared key based on zero-knowledge proof of knowledge (PoK). The efficiency comes from the fact that once the signatures are verified, the ephemeral values in PoK are also used to compute a shared key through an authenticated Diffie-Hellman key exchange protocol. Therefore, the protocol does not require any further external information to generate a shared key. Our protocol also does not require an interference with the Roadside Units or Certificate Authorities, and hence can be securely run outside the mixed-zones. We demonstrate the security of our protocol in an ideal/real simulation paradigm. Hence, our protocol achieves secure authentication, forward unlinkability, and accountability. Furthermore, the performance analysis shows that our protocol is more efficient in terms of computational and communication overheads compared to existing schemes.
Privacy is a huge concern for cryptocurrencies and blockchains as most of these systems log everything in the clear. This has resulted in several academic and industrial initiatives to address privacy. Starting with the UTXO model of Bitcoin, initial works brought confidentiality and anonymity to payments. Recent works have expanded to support more generalized forms of private computation. Such solutions tend to be highly involved as they rely on advanced cryptographic primitives and creative techniques to handle issues related to dealing with private records (e.g. concurrency and double spending). This situation makes it hard to comprehend the current state-of-the-art, much less build on top of it. To address these challenges, we develop a systematization of knowledge for privacy-preserving solutions in blockchain. To the best of our knowledge, our work is the first of its kind. After motivating design challenges, we devise two systematization frameworks-the first as a stepping stone to the second- and use them to study the state-of-the-art. For our first framework, we study the zero-knowledge proof systems used in surveyed solutions, based on their key features and limitations. Our second is for privacy-preserving solutions; we define several dimensions to categorize the surveyed schemes and, in doing so, identify two major paradigms employed to achieve private computation. We go on to provide insights to guide solutions' adoption and development. Finally, we touch upon challenges related to limited functionality and accommodating new developments.
Lejun Zhang, Yanfei Zou, Muhammad Hassam Yousuf, Weizheng Wang · 7 authors
Due to the increasing need for data sharing in the age of big data, how to achieve data access control and implement user permission revocation in the blockchain environment becomes an urgent problem. To solve the above problems, we propose a novel blockchain-based data sharing scheme (BDSS) with fine-grained access control and permission revocation in this paper, which regards the medical environment as the application scenario. In this scheme, we separate the public part and private part of the electronic medical record (EMR). Then, we use symmetric searchable encryption (SSE) technology to encrypt these two parts separately, and use attribute-based encryption (ABE) technology to encrypt symmetric keys which used in SSE technology separately. This guarantees better fine-grained access control and makes patients to share data at ease. In addition, we design a mechanism for EMR permission grant and revocation so that hospital can verify attribute set to determine whether to grant and revoke access permission through blockchain, so it is no longer necessary for ciphertext re-encryption and key update. Finally, security analysis, security proof and performance evaluation demonstrate that the proposed scheme is safe and effective in practical applications.
The application of artificial intelligence technology in Internet of Vehicles (IoV) has attracted great research interests with the goal of enabling smart transportation and traffic management. Meanwhile, concerns have been raised over the security and privacy of the tons of traffic and vehicle data. In this regard, Federated Learning (FL) with privacy protection features is considered a highly promising solution. However, in the FL process, the server side may take advantage of its dominant role in model aggregation to steal sensitive information of users, while the client side may also upload malicious data to compromise the training of the global model. Most existing privacy-preserving FL schemes in IoV fail to deal with threats from both of these two sides at the same time. In this paper, we propose a Blockchain based Privacy-preserving Federated Learning scheme named BPFL, which uses blockchain as the underlying distributed framework of FL. We improve the Multi-Krum technology and combine it with the homomorphic encryption to achieve ciphertext-level model aggregation and model filtering, which can enable the verifiability of the local models while achieving privacy-preservation. Additionally, we develop a reputation-based incentive mechanism to encourage users in IoV to actively participate in the federated learning and to practice honesty. The security analysis and performance evaluations are conducted to show that the proposed scheme can meet the security requirements and improve the performance of the FL model.
Industrial applications often require federated cloud services from multiple providers to improve reliability and flexibility. Traditional selection methods through auctions usually involve a centralized auctioneer to coordinate the auction procedure. Blockchain and smart contracts provide a decentralized mechanism to automate the cloud auction process; however, existing solutions fail in the selection of the most suitable providers and the violation detection of the signed auction agreements, which are also known as service-level agreements (SLAs). To tackle these problems, we propose an integrated auction model using Bayesian game theory and blockchain techniques. The proposed model is enhanced with two Bayesian Nash Equilibriums (BNEs); the first BNE enables the selection of cost-effective providers to construct the federated cloud services, while the second BNE ensures consistent and trustworthy monitoring of federated SLAs. Moreover, a timed message submission (TMS) algorithm is proposed to protect the auction privacy during the message submission phase. This paper validates the equilibrium results of two BNEs and implements the proposed model on the Ethereum blockchain. The analytical and experimental results demonstrate the feasibility, trustworthiness, and cost-effectiveness of our model.
Distributed Ledger Technology is also known as blockchain technology. It brings the characteristics like transparency, decentralization, immutability, and the distributed ledger in one package. The user identity of the public blockchain is anonymous on the network and cannot control the confidentiality and privacy of their data. The Anonymity of a user does not mean privacy. The importance of user and data privacy and confidentiality in blockchain technology is realized and is yet to be addressed by blockchain protocols. Zero-Knowledge-Proof can be the best solution to this problem. It is relatively new and is developing very rapidly. Zero-Knowledge-Proof can provide strong privacy if the security model is well understood and used carefully. Zero-Knowledge-Proof makes sure that no one accesses the secured data except the user. Paper discusses the types and the various applications of the Zero-Knowledge-Proof in the blockchain.
Minal Patel, Bhavesh N. Gohil, Sanjay Chaudhary, Sanjay Garg
Blockchain enables smart contract for secure data transfer by which fog offloading servers can have trustworthy access control to work with data execution. When cloud is used for handling requests from mobile users, the attacker may perform denial of service attack and the same is possible at fog nodes and the same can be handled with the help of blockchain technology. In this paper, smart city application is discussed a use case study for blockchain based fog computing architecture. We propose a novel offload chain architecture for blockchain-based offloading in internet of things (IoT) networks where mobile devices can offload their data to fog servers for computation by an access control mechanism. The offload chain model using deep reinforcement learning (DRL) is proposed to improve the efficiency of blockchain based fog offloading amongst existing models.
With the rapid increase of the industrial data and the development of the industrial Internet of Things (IIoT) paradigm, the efficiency and the quality of service of the emerging applications have been improved. However, the contradiction between data sharing and privacy preserving is still an obstacle in the IIoT. To this end, in this article, we propose a privacy-preserving and publicly auditable multiparty computation scheme for industrial data sharing and computing, which avoids privacy leakage and computation misbehavior by separating the data ownership, data use, and data verification. Using the blockchain technology, a transparent management platform is provided to recognize and trace the illegal data and computation behavior. Moreover, we integrate the noninteractive zero-knowledge proof in the multiparty interaction mechanism, wherein the verification of data consistency and computation validity is executed publicly on the blockchain. Finally, we implement experiment to evaluate the performance of the computation latency, communication overhead and the influence of encryption parameter, and the numerical results illustrate the efficiency and feasibility of our scheme.
In the modern digital world, large-scale data and the analytic processing of the data make privacy-preserving computation even more critical. SMPC is a cryptographic protocol used to compute a function over the inputs of multiple parties such that the other party’s input is unknown. This then provides for computing in parallel with other participants, without requiring a coordinator, which, in today’s privacy-conscious world, is beneficial in avoiding using a central authority in data-entrusted activities. In a nutshell, a decentralized AI approach is based on distributed computing principles and the blockchain to create a solid architecture for SMPC implementation. In this manner, decentralized AI eliminates several drawbacks of data centralization, such as single points of failure and data breaches. SMPC and decentralized networks are the foundation of the privacy-preserving ML, where sensitive data train models without revealing the data points. Specifically, the growing necessity for protecting data with the help of laws like the GDPR and CCPA enhances SMPC’s application in decentralized AI. Blockchain technology extends this implementation by having additional qualities of having an unchangeable record and consensus mechanisms that guarantee computation reliability and openness. However, scalability, ITY, computational cost, and system compatibility are drawbacks to integrating decentralized AI and SMPC. Solving these needs more be a continuous effort in the search for cryptographic techniques in communication, network design, and protocol formation. The combination of decentralized AI and SMPC presents a new and revolutionary way of multi-party computation through data privacy and access to cooperation and innovation in sectors such as health, finance, and supply chain. With the development of technology, these intelligent computing applications of decentralized AI and SMPC will continue to develop and open up new areas for efficient and secure data usage.
This letter studies the task offloading in reputation-based consortium blockchain networks, where the tasks are transmitted to Edge Computing Servers (ECSs) due to limited resources. We propose a novel Delegated Proof of Stake (DPoS) consensus mechanism in which validation nodes (including active nodes and backup nodes) are voted based on their reputation. The incentive identifies the reputation and the consensus delay as two major factors determining the reward. A three-stage Stackelberg game is developed to jointly minimize cost of the users and maximize utilities of the master node and the validation nodes. We analyze the unique Stackelberg equilibrium exists in the proposed game by the backward induction. The simulation results demonstrate that the designed incentive is feasible for trust management, and the proposed consensus has lower delay and higher decentralization compared with the traditional DPoS.