In recent times, the use of cloud computing has gained popularity all over the world in the context of performing smart computations on big data. The privacy of sensitive data of the client is of utmost important issues. Data leakage or hijackers may theft significant information about the client that ultimately may affect the reputation and prestige of its owner (bank) and client (customers). In general, to save the privacy of our banking data it is preferred to store, process, and transmit the data in the form of encrypted text. But now the main concern leads to secure computation over encrypted text or another possible way to perform computation over clouds makes data more vulnerable to hacking and attacks. Existing classical encryption techniques such as RSA, AES, and others provide secure transaction procedures for data over clouds but these are not fit for secure computation over data in the clouds. In 2009, Gentry comes with a solution for such issues and presents his idea as Homomorphic encryption (HE) that can perform computation over encrypted text without decrypting the data itself. Now a day's privacy-enhancing techniques (PET) are there to explore more potential benefits in security issues and useful in historical cases of privacy failure. Differential privacy, Federated analysis, homomorphic encryption, zero-knowledge proof, and secure multiparty computation are a privacy-enhancing technique that may useful in financial services as these techniques provide a fully-fledged mechanism for financial institutes. With the collaboration of industries, these techniques are may enable new data-sharing agreements for a more secure solution over data. In this paper, the primary concern is to investigate the different standards and properties of homomorphic encryption in digital banking and financial institutions.
With the massive use of dematerialized storage, homomorphism has become one of the most widely used properties in cryptology. In this thesis we will study how to use it in concrete multi-users protocols requiring not only confidentiality but also anonymity, authentication or verifiability. Homomorphic encryption schemes, homomorphic digital signatures and homomorphic zero-knowledge proofs will be used together, but each time restricted to achieve the desired level of security.First, the confidential aspect is studied for computations on large outsourced databases. Being able to apply functions on encrypted data without having to download and decrypt it entirely may be essential and allows to take advantage of the computational power of the server. This can also be interesting when a third-party company without right-access to the database wants to obtain the result of a computation. However, some guarantees on the learned information need to be taken. To this end, we present a decentralized encryption scheme that allows controlled evaluation of quadratic functions on outsourced data thanks to a group of controllers.However, sometimes confidentiality of the data is not the most desired property for a system as it does not protect the sender. For electronic voting, each encrypted ballot must be associated with its voter to verify that he is allowed to vote. After the voting phase, anonymity is achieved by shuffling so that, during the count, which corresponds to the decryption, no link between votes and voters can be made. We propose a new construction of mix-network based on linearly homomorphic signatures which allows for the first time a verification which is cost-independent of the number of mix-servers. This scalable mix-net improves the efficiency compared to already known constructions, especially with an increasing number of shuffles.Nevertheless, with perfect anonymity comes the threat of malicious use of the system. Cryptology must consider these possible abuses and we propose the first multi-authority anonymous credential protocol with traceability property: a user asks a credential issuer for a credential and uses it to access a system while remaining anonymous. In case of abuse, an authority can revoke anonymity and trace a malicious user. The scheme is as efficient as the previously known credential schemes while achieving the multi-credential issuer functionality.
Muhammad Usman Aftab, Mehdi Hussain, Anders Lindgren, Abdul Ghafoor
To ensure traffic safety and proper operation of vehicular networks, safety messages or beacons are periodically broadcasted in Vehicular Adhoc Networks (VANETs) to neighboring nodes and road side units (RSU). Thus, authenticity and integrity of received messages along with the trust in source nodes is crucial and highly required in applications where a failure can result in life-threatening situations. Several digital signature based approaches have been described in literature to achieve the authenticity of these messages. In these schemes, scenarios having high level of vehicle density are handled by RSU where aggregated signature verification is done. However, most of these schemes are centralized and PKI based where our goal is to develop a decentralized dynamic system. Along with authenticity and integrity, trust management plays an important role in VANETs which enables ways for secure and verified communication. A number of trust management models have been proposed but it is still an ongoing matter of interest, similarly authentication which is a vital security service to have during communication is not mostly present in the literature work related to trust management systems. This paper proposes a secure and publicly verifiable communication scheme for VANET which achieves source authentication, message authentication, non repudiation, integrity and public verifiability. All of these are achieved through digital signatures, Hash Message Authentication Code (HMAC) technique and logging mechanism which is aided by blockchain technology.
As the development of IoT technologies has progressed rapidly recently, most IoT data are focused on monitoring and control to process IoT data, but the cost of collecting and linking various IoT data increases, requiring the ability to proactively integrate and analyze collected IoT data so that cloud servers (data centers) can process smartly. In this paper, we propose a blockchain-based IoT big data integrity verification technique to ensure the safety of the Third Party Auditor (TPA), which has a role in auditing the integrity of AIoT data. The proposed technique aims to minimize IoT information loss by multiple blockchain groupings of information and signature keys from IoT devices. The proposed technique allows IoT information to be effectively guaranteed the integrity of AIoT data by linking hash values designated as arbitrary, constant-size blocks with previous blocks in hierarchical chains. The proposed technique performs synchronization using location information between the central server and IoT devices to manage the cost of the integrity of IoT information at low cost. In order to easily control a large number of locations of IoT devices, we perform cross-distributed and blockchain linkage processing under constant rules to improve the load and throughput generated by IoT devices.
The consensus algorithm is the core component of a blockchain system, which determines the efficiency, security, and scalability of the blockchain network. The representative consensus algorithm is the proof of work (PoW) proposed in Bitcoin, where the consensus process consumes large amount of compute in solving meaningless Hash puzzel. Meanwhile, the deep learning (DL) has brought unprecedented performance gains at heavy computate cost. In this demo, we channels the otherwise wasted computational power to the practical purpose of training neural network models, through the proposed proof of learning (PoL) consensus algorithm. In PoLe, the training/testing data are released to the entire blockchain network (BCN) and the consensus nodes train NN models on the data, which serves as the proof of learning. When the consensus on the BCN considers a NN model to be valid, a new block is appended to the blockchain. Through our system, we investigate the potential of enpowering machine learning with consensus building on blockchains.
Amal Abid, Saoussen Cheikhrouhou, Slim Kallel, Mohamed Jmaïel
The COVID-19 pandemic has emerged as a highly transmissible disease which has caused a disastrous impact worldwide by adversely affecting the global economy, health, and human lives. This sudden explosion and uncontrolled worldwide spread of COVID-19 has revealed the limitations of existing healthcare systems regarding handling public health emergencies. As governments seek to effectively re-establish their economies, open workplaces, ensure safe travels and progressively return to normal life, there is an urgent need for technologies that may alleviate the severity of the losses. This article explores a promising solution for secure Digital Health Certificate, called NovidChain, a Blockchain-based privacy-preserving platform for COVID-19 test/vaccine certificates issuing and verifying. More precisely, NovidChain incorporates several emergent concepts: (i) Blockchain technology to ensure data integrity and immutability, (ii) self-sovereign identity to allow users to have complete control over their data, (iii) encryption of Personally Identifiable Information to enhance privacy, (iv) W3C verifiable credentials standard to facilitate instant verification of COVID-19 proof, and (v) selective disclosure concept to permit user to share selected pieces of information with trusted parties. Therefore, NovidChain is designed to meet a high level of protection of personal data, in compliant with the GDPR and KYC requirements, and guarantees the user's self-sovereignty, while ensuring both the safety of populations and the user's right to privacy. To prove the security and efficiency of the proposed NovidChain platform, this article also provides a detailed technical description, a proof-of-concept implementation, different experiments, and a comparative evaluation. The evaluation shows that NovidChain provides better financial cost and scalability results compared to other solutions. More precisely, we note a high difference in time between operations (i.e., between 46% and 56%). Furthermore, the evaluation confirms that NovidChain ensures security properties, particularly data integrity, forge, binding, uniqueness, peer-indistinguishability, and revocation.
Hai Jin, Xiaohai Dai, Jiang Xiao, Baochun Li · 6 authors
Federated learning (FL) has been gaining popularity as a way to provide privacy-preserving data sharing for the Internet of Medical Things (IoMT). As a complementary, blockchain technology is used in recent literature to make FL secure. However, existing blockchain-based FL (BFL) solutions do not perform well when data in a BFL cluster are sparse. A direct solution is to collect as many devices as possible to establish a large BFL cluster. However, these devices may locate in geographically distant areas and be separated by great distance, which further results in high communication latency. The high latency will lead to BFL’s low system efficiency due to frequent communications in the blockchain consensus. In this article, we propose that the large cluster should be divided into multiple smaller clusters, each in its own geographical area and organized with a BFL. In this context, we propose CFL, a cross-cluster FL system facilitated by the cross-chain technique. CFL connects multiple BFL clusters, where only a few aggregated updates are transmitted over long distances across clusters, thus improving the system efficiency. The design of CFL focuses on a cross-chain consensus protocol, which guarantees the model updates to be exchanged securely across clusters. We carry out extensive experiments to evaluate CFL in comparison with BFL, and show both CFL’s feasibility and efficiency.
Big Data As A Service Is Used In Today’s Scenario To Handle And Process The Big Amount Of Data Which Are Generated From Different Source Every Day. Since Data Is Stored On The Cloud Platform, The System Could Suffer A Failure And Give Attackers The Opportunity To Launch Various Categories Of Attacks.Manyresearcheshave Been Done In This Domain To Provide Security And Protection To The Data On Cloud. The Blockchain Technology Is A Secure, Distributed And Privacy-Preserving Decentralized Ledger Where The Transactions Are Flexible, Secure,Verifiable And Permanent Way.Here, The Transaction Data Is Encrypted Andkept In A Wrapped Block (I.E., Record) Which Are Spreadthrough The N/W In A Provable And Unabashedmode Across The Entire Network To Enhance Information Security And Data Privacy. In This Paperwe Have Proposed A Framework For An Access Control With Privacy Protection In Bdaas Based On Blockchain Technology. Here Blockchain Technology Is Used Only For Storing The Transaction Log Information Whenever Any Kind Of Event Log Occurred In System.
Vehicular crowd sensing is a promising approach to address the problem of traffic data collection by leveraging the power of vehicles. In various applications of vehicular crowd sensing, there exist two burning issues. First, privacy can be easily compromised when a vehicle is performing a crowd sensing task. Second, vehicles have no incentive to submit high-quality data due to the lack of fairness, which means that everyone gets the same paid, regardless of the quality of the submitted data. To address these issues, we propose a smart privacy-preserving incentive mechanism (SPPIM) for vehicular crowd sensing. Specifically, we first propose a new SPPIM model for the scenario of vehicular crowd sensing via smart contract on the blockchain. Then, we design a privacy-preserving incentive mechanism based on budget-limited reverse auction. Anonymous authentication based on zero-knowledge proof is utilized to ensure the privacy preservation of vehicles. To ensure fairness, the reward payments of winning vehicles are determined by not only the bids of vehicles but also their reputation and the data quality. Then, any rewarded vehicle can get the fair payment; on the contrary, malicious vehicles or task initiators will be punished. Finally, SPPIM is implemented by using smart contracts written via Solidity on a local Ethereum blockchain network. Both security analysis and experimental results show that the proposed SPPIM achieves privacy preservation and fair incentives at acceptable execution costs.
Christian Delgado‐von‐Eitzen, Luis Anido, Manuel J. Fernández Iglesias
Blockchain technologies are awakening in recent years the interest of different actors in various sectors and, among them, the education field, which is studying the application of these technologies to improve information traceability, accountability, and integrity, while guaranteeing its privacy, transparency, robustness, trustworthiness, and authenticity. Different interesting proposals and projects were launched and are currently being developed. Nevertheless, there are still issues not adequately addressed, such as scalability, privacy, and compliance with international regulations such as the General Data Protection Regulation in Europe. This paper analyzes the application of blockchain technologies and related challenges to issue and verify educational data and proposes an innovative solution to tackle them. The proposed model supports the issuance, storage, and verification of different types of academic information, both formal and informal, and complies with applicable regulations, protecting the privacy of users’ personal data. This proposal also addresses the scalability challenges and paves the way for a global academic certification system.
Intelligent Transportation System (ITS) is critical to cope with traffic events, e.g., traffic jams and accidents, and provide services for personal traveling. However, existing researches have not jointly considered the user data safety, utility and system latency comprehensively, to the best of our knowledge. Since both safe and efficient transmissions are significant for ITS, we construct a blockchain-enabled crowdsensing framework for distributed traffic management. First, we illustrate the system model and formulate a multi-objective optimization problem. Due to its complexity, we decompose it into two subproblems, and propose the corresponding schemes, i.e., a Deep Reinforcement Learning (DRL)-based algorithm and a DIstributed Alternating Direction mEthod of Multipliers (DIADEM) algorithm. Extensive experiments are carried out to evaluate the performance of our solutions, and experimental results demonstrate that the DRL-based algorithm can legitimately select active miners and transactions to make a satisfied trade-off between the blockchain safety and latency, and the DIADEM algorithm can effectively select task computation modes for vehicles in a distributed way to maximize their social welfare.
The integration of multi-access edge computing (MEC) and RAFT consensus makes it feasible to deploy blockchain on trustful base stations and gateways to provide efficient and tamper-proof edge data services for Internet of Things (IoT) applications. However, reducing the latency of storing data on blockchain remains a challenge, especially when an anomaly-triggered data flow in a certain area exceeds the block generation speed. This letter proposes an intelligent transaction migration scheme for RAFT-based private blockchain in IoT applications to migrate transactions in busy areas to idle regions intelligently. Simulation results show that the proposed scheme can apparently reduce the latency in high data flow circumstances.
The fast proliferation of edge computing devices brings an increasing growth of data, which directly promotes machine learning (ML) technology development. However, privacy issues during data collection for ML tasks raise extensive concerns. To solve this issue, synchronous federated learning (FL) is proposed, which enables the central servers and end devices to maintain the same ML models by only exchanging model parameters. However, the diversity of computing power and data sizes leads to a significant difference in local training data consumption, and thereby causes the inefficiency of FL. Besides, the centralized processing of FL is vulnerable to single-point failure and poisoning attacks. Motivated by this, we propose an innovative method, federated learning with asynchronous convergence (FedAC) considering a staleness coefficient, while using a blockchain network instead of the classic central server to aggregate the global model. It avoids real-world issues such as interruption by abnormal local device training failure, dedicated attacks, etc. By comparing with the baseline models, we implement the proposed method on a real-world dataset, MNIST, and achieve accuracy rates of 98.96% and 95.84% in both horizontal and vertical FL modes, respectively. Extensive evaluation results show that FedAC outperforms most existing models.
Crowdsensing has become increasingly popular in recent years by leveraging the sensing capability of the ubiquitous Internet of Things (IoT) and mobile devices. A critical component to enable effective crowdsensing is a reliable reward system to motivate the participation of crowdsensing. In traditional crowdsensing applications, the assessment of data quality and the evaluation of each participant's rewards are performed by the central server. Hence, their fairness and reliability are based on the assumption that the server will behave correctly. Once the server suffers from software or hardware failures, or even cheats on purpose, the profit of participants cannot be guaranteed. In this paper, we propose a reliable decentralized reward system for crowdsensing by harnessing the blockchain technique. Different from existing blockchain-based crowdsensing solutions that utilize expensive consensus mechanisms in terms of computation or financial cost, we explore the power of reputation system that exists in most crowdsensing applications and securely integrate it into blockchain to design a proof of reputation consensus mechanism. On top of it, an efficient and reliable reward system using blockchain is further designed. We evaluated the performance of our proposed reward system using numerical analysis as well as simulation.
Federated learning (FL) algorithms provide privileges in personal data protection and information islands elimination for distributed machine learning. As an increasing number of edge devices connected in networks, we still see a lot of computing resources and data remaining underutilized and there is no platform for users to trade FL tasks. In this demonstration, we propose a blockchain-based federated learning application trading platform called FLeX, on which users can buy and sell computing resources for training machine learning models with no sacrifice of data privacy. We design FLeX in a highly distributed and scalable manner. We separate the data plane and control plane in the platform. In FLeX, trading mechanisms and FL algorithms are deployed in smart contracts of the blockchain. Control messages and trading information are well protected in the blockchain. With FLeX, we realize a distributed trading platform for executing FL tasks.
5G-enabled Industrial Internet of Things (IIoT) deployment will bring more severe security and privacy challenges, which puts forward higher requirements for access control. Blockchain-based access control method has become a promising security technology, but it still faces high latency in consensus process and weak adaptability to dynamic changes in network environment. This article proposes a novel access control framework for 5G-enabled IIoT based on consortium blockchain. We design three types of chaincodes for the framework named policy management chaincode (PMC), access control chaincode (ACC), and credit evaluation chaincode (CEC). The PMC and ACC are deployed on the same data channel to implement the management of access control policies and the authorization of access. The CEC deployed on another channel is used to add behavior records collected from IIoT devices and calculate the credit value of IIoT domain. Specifically, we design a two-step credit-based Raft consensus mechanism, which can select the orderer nodes dynamically to achieve fast and reliable consensus based on historical behavior records stored in the ledger. Furthermore, we implement the proposed framework on a real-world testbed and compare it with the framework based on practical Byzantine fault tolerance consensus. The experiment results show that our proposed framework can maintain lower consensus cost time with 100 ms level and achieves four to five times throughput with lower hardware resource consumption and communication consumption. Besides, our design also improves the security and robustness of the access control process.
Dongkun Hou, Jie Zhang, Ka Lok Man, Jieming Ma · 5 authors
Federal learning (FL) can realize a distributed training machine learning models in multiple devices while protecting their data privacy, but some defect still exists such as single point failure and lack of motivation. Blockchain as a distributed ledger can be utilized to provide a novel FL framework to address those issues. This paper aims to discuss how the blockchain technology is employed to compensate for shortcomings in FL. A systematic literature review is conducted to investigate existing FL problems and to summarize knowledge about the existing Blockchain-based FL (BFL). The differences among these collected BFL architectures are presented and discussed, and the applications of BFL are categorized and analyzed. Finally, some suggestions for future development and application of BFL are discussed.
IPFS is a distributed file system. Each node in the system works in a collaborative manner to implement decentralized file access. Anyone can share files through the system. However, there are some data privacy issues of the system during the sharing of file data. Therefore, we need to add the data access mechanism to alleviate data privacy issues in IPFS. In response to this problem, we propose a privacy protection method based on blockchain and improved IPFS. The method utilizes the blockchain to store file information and user permissions, and the improved IPFS can control file data sharing according to the user's permission. At the same time, we have extended the user group and file directory management functions based on smart contracts. Through this method, the user can centrally manage the shared users in the system and efficiently organize the file directory while realizing the protection of the private files.
There are mainly three traditional data sharing methods. The first is the most direct data copy, the second is to share data based on a data sharing protocol, and the third is to share data through a data center. These methods have a common feature, that is, the data requester will get the data of the data owner. This may cause serious problems in data security, such as data leakage and data abuse. As a data center is a centralized organization, there are risks such as data loss and data tampering. In addition, various countries have also issued a series of policies on data security issues, such as the GDPR implemented by the European Union in 2018. The blockchain technology using a decentralized model can be used as a new attempt to solve the above problems. This paper studies a data sharing scheme based on blockchain, and proposes a model that combines the Ethereum blockchain and federated learning ideas, and uses off-chain storage methods to share data. In this model, users can upload data description information to the blockchain through smart contracts, and can retrieve the required data through keywords, and then send the data identification and data processing model to the data owner in the form of transactions. The data owner can use this model to process the data, and finally return the result to the data requester. Because the data owner is in full control of his data and does not expose the source data to the outside, the use of this model for data sharing can effectively avoid problems such as data leakage, data loss, and data abuse.