Data is the input for various artificial intelligence (AI) algorithms to mine valuable features, yet data in Internet is scattered everywhere and controlled by different stakeholders who cannot believe in each other, and usage of the data in complex cyberspace is difficult to authorize or to validate. As a result, it is very difficult to enable data sharing in cyberspace for the real big data, as well as a real powerful AI. In this paper, we propose theSecNet, an architecture that can enable secure data storing, computing, and sharing in the large-scale Internet environment, aiming at a more secure cyberspace with real big data and thus enhanced AI with plenty of data source, by integrating three key components: 1) blockchain-based data sharing with ownership guarantee, which enables trusted data sharing in the large-scale environment to form real big data; 2) AI-based secure computing platform to produce more intelligent security rules, which helps to construct a more trusted cyberspace; 3) trusted value-exchange mechanism for purchasing security service, providing a way for participants to gain economic rewards when giving out their data or service, which promotes the data sharing and thus achieves better performance of AI. Moreover, we discuss the typical use scenario of SecNet as well as its potentially alternative way to deploy, as well as analyze its effectiveness from the aspect of network security and economic revenue.
The vehicular ad-hoc networks (VANETs) is one of the most promising application in the communications of smart vehicles and the smart transportation systems. However, authentication and privacy of users are still two vital issues in VANETs. It is crucial to prevent internal vehicles from broadcasting the forged messages while preserving the privacy of vehicles against the tracking attack. Moreover, in the traditional mode, the transactional data storage provides no distributed and decentralized security, so that the third party initiates the dishonest behaviors possibly. In this paper, based on blockchain technique, we propose a traceable and decentralized the Internet of Vehicle system framework for communication among smart vehicles by employing of a secure access authentication scheme between vehicles and RoadSide Units (RSUs). On the one hand, this scheme allows that vehicles employ pseudonyms for Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) communications anonymously in the non-fully trusted environment. On the other hand, the transparency of vehicles in authentication and announcement is preformed efficiently by the blockchain technology. In addition, the transaction information is tamper-resistant that provides the distributed and decentralized property for the different cloud servers. With the help of Certificate Authority (CA) and the RoadSide Units (RSUs), our proposal achieves the conditional privacy to trace the real identity of the malicious vehicle in the anonymous announcements as well. Finally, through the theoretical analysis and simulations, our scheme is able to construct a secure and decentralized system framework of VANETs with accountability and privacy preservation.
ETH Zurich, Switzerland, Mathieu Chanson, Andreas Bogner, ETH Zurich, Switzerland · 8 authors
An ever growing variety of smart, connected Internet of Things (IoT) devices poses completely new challenges for businesses regarding security and privacy. In fact, the adoption of smart products may depend on the ability of organizations to offer systems that ensure adequate sensor data integrity while guaranteeing sufficient user privacy. In light of these challenges, previous research indicates that blockchain technology could be a promising means to mitigate issues of data security arising in the IoT. Building upon the existing body of knowledge, we propose a design theory, including requirements, design principles, and features, for a blockchain-based sensor data protection system (SDPS) that leverages data certification. To support this, we designed and developed an instantiation of an SDPS (CertifiCar) in three iterative cycles intented to prevent the fraudulent manipulation of car mileage data. Following the explication of our SDPS, we provide an ex post evaluation of our design theory considering CertifiCar and two additional use cases in the areas of pharmaceutical supply chains and energy microgrids. Our results suggest that the proposed design ensures the tamper-resistant gathering, processing, and exchange of IoT sensor data in a privacy-preserving, scalable, and efficient manner.
Vasavi Bande, Karu Prasada Rao, Sreenivas Mekala, T. Venkat Narayana Rao · 5 authors
A blockchain as a trustworthy and secure decentralized and distributed network has been emerged for many applications such as in banking, finance, insurance, healthcare and business. Recently, many communities in blockchain networks want to deploy machine learning models to get meaningful knowledge from geographically distributed large-scale data owned by each participant. To run a learning model without data centralization, distributed machine learning (DML) for blockchain networks has been studied. While several works have been proposed, privacy and security have not been sufficiently addressed, and as we show later, there are vulnerabilities in the architecture and limitations in terms of efficiency. In this paper, we propose a privacy-preserving DML model for a permissioned blockchain to resolve the privacy, security, and performance issues in a systematic way. We develop a differentially private stochastic gradient descent method and an error-based aggregation rule as core primitives. Our model can treat any type of differentially private learning algorithm where non-deterministic functions should be defined. The proposed error-based aggregation rule is effective to prevent attacks by an adversarial node that tries to deteriorate the accuracy of DML models. Our experiment results show that our proposed model provides stronger resilience against adversarial attacks than other aggregation rules under a differentially private scenario. Finally, we show that our proposed model has high usability because it has low computational complexity and low transaction latency.
A vehicular ad-hoc network (VANET) can improve the flow of traffic to facilitate intelligent transportation and to provide convenient information services, where the goal is to provide self-organizing data transmission capabilities for vehicles on the road to enable applications, such as assisted vehicle driving and safety warnings. VANETs are affected by issues such as identity validity and message reliability when vehicle nodes share data with other nodes. The method used to allow the vehicle nodes to upload sensor data to a trusted center for storage is susceptible to security risks, such as malicious tampering and data leakage. To address these security challenges, we propose a data security sharing and storage system based on the consortium blockchain (DSSCB). This digital signature technique based on the nature of bilinear pairing for elliptic curves is used to ensure the reliability and integrity when transmitting data to a node. The emerging consortium blockchain technology provides a decentralized, secure, and reliable database, which is maintained by the entire network node. In DSSCB, smart contracts are used to limit the triggering conditions for preselected nodes when transmitting and storing data and for allocating data coins to vehicles that participate in the contribution of data. The security analysis and performance evaluations demonstrated that our DSSCB solution is more secure and reliable in terms of data sharing and storage. Compared with the traditional blockchain system, the time required to confirm the data block was reduced by nearly six times and the transmission efficiency was improved by 83.33%.
Jorge Bernal Bernabé, José Luis Cánovas, José L. Hernández-Ramos, Rafael Torres Moreno · 5 authors
Blockchains offer a decentralized, immutable and verifiable ledger that can record transactions of digital assets, provoking a radical change in several innovative scenarios, such as smart cities, eHealth or eGovernment. However, blockchains are subject to different scalability, security and potential privacy issues, such as transaction linkability, crypto-keys management (e.g. recovery), on-chain data privacy, or compliance with privacy regulations (e.g. GDPR). To deal with these challenges, novel privacy-preserving solutions for blockchain based on crypto-privacy techniques are emerging to empower users with mechanisms to become anonymous and take control of their personal data during their digital transactions of any kind in the ledger, following a Self-Sovereign Identity (SSI) model. In this sense, this paper performs a systematic review of the current state of the art on privacy-preserving research solutions and mechanisms in blockchain, as well as the main associated privacy challenges in this promising and disrupting technology. The survey covers privacy techniques in public and permissionless blockchains, e.g. Bitcoin and Ethereum, as well as privacy-preserving research proposals and solutions in permissioned and private blockchains. Diverse blockchain scenarios are analyzed, encompassing, eGovernment, eHealth, cryptocurrencies, Smart cities, and Cooperative ITS.
With the rapid development of the Internet of things (IoT), more and more IoT\ndevices are connected and communicate frequently. In this background, the\ntraditional centralized security architecture of IoT will be limited in terms\nof data storage space, data reliability, scalability, operating costs and\nliability judgment. In this paper, we propose an new key information storage\nframework based on a small distributed database generated by blockchain\ntechnology and cloud storage. Specifically, all encrypted key communication\ndata will be upload to public could server for enough storage, but the\nabstracts of these data (called "communication logs") will be recorded in "IoT\nledger" (i.e., an distributed database) that maintained by all IoT devices\naccording to the blockchain generation approach, which could solve the problem\nof data reliability, scalability and liability judgment. Besides, in order to\nefficiently search communication logs and not reveal any sensitive information\nof communication data, we design the secure search scheme for our "IoT ledger",\nwhich exploits the Asymmetric Scalar-product Preserving Encryption (ASPE)\napproach to guarantee the data security, and exploits the 2-layers index which\nis tailor-made for blockchain database to improve the search efficiency.\nSecurity analysis and experiments on synthetic dataset show that our schemes\nare secure and efficient.\n
Blockchains have recently been under the spotlight due to the boom of cryptocurrencies and decentralized applications. There is an increasing demand for querying the data stored in a blockchain database. To ensure query integrity, the user can maintain the entire blockchain database and query the data locally. However, this approach is not economic, if not infeasible, because of the blockchain's huge data size and considerable maintenance costs. In this paper, we take the first step toward investigating the problem of verifiable query processing over blockchain databases. We propose a novel framework, called vChain, that alleviates the storage and computing costs of the user and employs verifiable queries to guarantee the results' integrity. To support verifiable Boolean range queries, we propose an accumulator-based authenticated data structure that enables dynamic aggregation over arbitrary query attributes. Two new indexes are further developed to aggregate intra-block and inter-block data records for efficient query verification. We also propose an inverted prefix tree structure to accelerate the processing of a large number of subscription queries simultaneously. Security analysis and empirical study validate the robustness and practicality of the proposed techniques.
With the more and more extensive application of blockchain, blockchain security has been widely concerned by the society and deeply studied by scholars. Moreover, the security of blockchain data directly affects the security of various applications of blockchain. In this survey, we perform a comprehensive classification and summary of the security of blockchain data. First, we present classification of blockchain data attacks. Subsequently, we present the attacks and defenses of blockchain data in terms of privacy, availability, integrity and controllability. Data privacy attacks present data leakage or data obtained by attackers through analysis. Data availability attacks present abnormal or incorrect access to blockchain data. Data integrity attacks present blockchain data being tampered. Data controllability attacks present blockchain data accidentally manipulated by smart contract vulnerability. Finally, we present several important open research directions to identify follow-up studies in this area.
Open access
Blockchain Technology Applications and Security
Privacy-Preserving Technologies in Data
Advanced Steganography and Watermarking Techniques
Tsung-Ting Kuo, Rodney A. Gabriel, Lucila Ohno‐Machado
OBJECTIVE: Decentralized privacy-preserving predictive modeling enables multiple institutions to learn a more generalizable model on healthcare or genomic data by sharing the partially trained models instead of patient-level data, while avoiding risks such as single point of control. State-of-the-art blockchain-based methods remove the "server" role but can be less accurate than models that rely on a server. Therefore, we aim at developing a general model sharing framework to preserve predictive correctness, mitigate the risks of a centralized architecture, and compute the models in a fair way. MATERIALS AND METHODS: We propose a framework that includes both server and "client" roles to preserve correctness. We adopt a blockchain network to obtain the benefits of decentralization, by alternating the roles for each site to ensure computational fairness. Also, we developed GloreChain (Grid Binary LOgistic REgression on Permissioned BlockChain) as a concrete example, and compared it to a centralized algorithm on 3 healthcare or genomic datasets to evaluate predictive correctness, number of learning iterations and execution time. RESULTS: GloreChain performs exactly the same as the centralized method in terms of correctness and number of iterations. It inherits the advantages of blockchain, at the cost of increased time to reach a consensus model. DISCUSSION: Our framework is general or flexible and can also address intrinsic challenges of blockchain networks. Further investigations will focus on higher-dimensional datasets, additional use cases, privacy-preserving quality concerns, and ethical, legal, and social implications. CONCLUSIONS: Our framework provides a promising potential for institutions to learn a predictive model based on healthcare or genomic data in a privacy-preserving and decentralized way.
Open access
Privacy-Preserving Technologies in Data
Artificial Intelligence in Healthcare and Education
Mei Yu, Jie Zhang, Jianrong Wang, Jie Gao · 8 authors
Now, the security and privacy-preserving of Internet of Things are receiving more attention. This article proposes a new method of Internet of Things security and privacy preserving by combining differentiated nodes, precision clustering, RSA, multi-signature, and blockchain. To calculate the node rank value in Internet of Things, the number of nodes’ links is taken as the weight of nodes based on PageRank. This method increases the node rank values’ difference between nodes. Accurate clustering determines the initial central nodes of k-means based on the differentiated node rank values, which effectively differentiates the active and inactive nodes in Internet of Things and achieve different levels of protection. To ensure data’s reusability, we adopt the multi-signature. And considering node’s incoming nodes have certain decision on whether data can be transmitted, we filter out two nodes with the highest node rank value from active node’s incoming nodes to do 2/3 multi-signature which saves resources. We encrypt and sign the transmitted data with RSA, helping receivers to verify data. Data are transmitted safely through the blockchain. Successful validation indicates successful transmission. Experiments show that the proposed method can effectively distinguish between active and inactive nodes, which increases the difficulty of attackers’ attack and effectively protects the security and privacy of Internet of Things nodes and data.
In the age of Big Data, enabling task scheduling while protecting users' privacy is critical for various decentralized applications in blockchain-based smart contract platforms. Such a privacy-preserving task scheduler requires the task input data to be secretly maintained until a prescribed task execution time and be automatically recorded into the blockchain to enabling the execution of the task at the execution time, even if the user goes offline. While straight-forward centralized approaches provide a basic solution to the problem, unfortunately they are limited to a single point of trust and involve a single point of control. This paper presents decentralized techniques for supporting privacy-preserving task scheduling using smart contracts in Ethereum blockchain networks. We design a privacy-preserving task scheduling protocol that is managed by a manager smart contract. The protocol requires a user to schedule a task by deploying a proxy smart contract maintaining the non-sensitive information of the task while creating decentralized secret trust and selecting trustees from the network to maintain the sensitive information of the task. With security techniques including secret sharing and layered encryption as well as security deposit paid by trustees as economic deterrence, the protocol can protect the sensitive information against possible attacks including some trustees destroying the sensitive information (drop attack) or secretly releasing the sensitive information before the execution time (release-ahead attack). We demonstrate the attack-resilience of the proposed protocol through rigorous analysis.Our implementation and experimental evaluation on the Ethereum official test network demonstrate the low monetary cost and the low time overhead associated with the proposed approach.
The Internet of Things aims at connecting everything, ranging from individuals, organizations, and companies to things in the physical and virtual world. The digital identity has always been considered as the keystone for all online services and the foundation for building security mechanisms such as authentication and authorization. However, the current literature still lacks a comprehensive study on the digital identity management for the Internet of Things (IoT). In this paper, we firstly identify the requirements of building identity management systems for IoT, which comprises scalability, interoperability, mobility, security and privacy. Then, we trace the identity problem back to the origin in philosophy, analyze the Internet digital identity management solutions in the context of IoT and investigate recent surging blockchain sovereign identity solutions. Finally, we point out the promising future research trends in building IoT identity management systems and elaborate challenges of building a complete identity management system for the IoT, including access control, privacy preserving, trust and performance respectively.
Data is of unprecedented importance today. The most valuable companies of today treat data as a commodity, which they trade and earn revenues. To facilitate such trading, data marketplaces have emerged. Present data marketplaces are inadequate as they fail to satisfy all the desirable properties - fairness, efficiency, security, privacy and adherence to regulations. In this article, we propose a blockchain enabled data marketplace solution that fulfills all required properties. We outline the design, show how to design such a system and discuss the challenges in building a complete data marketplace.
Yunhui Long, Tanmay Gangwani, Haris Mughees, Carl A. Gunter
Privacy preserving multi-party computation has many applications in areas such as medicine and online advertisements. In this work, we propose a framework for distributed, secure machine learning among untrusted individuals. The framework consists of two parts: a two-step training protocol based on homomorphic addition and a zero knowledge proof for data validity. By combining these two techniques, our framework provides privacy of per-user data, prevents against a malicious user contributing corrupted data to the shared pool, enables each user to self-compute the results of the algorithm without relying on external trusted third parties, and requires no private channels between groups of users. We show how different ML algorithms such as Latent Dirichlet Allocation, Naive Bayes, Decision Trees etc. fit our framework for distributed, secure computing.
Jingwei Liu, Xiaolu Li, Lin Ye, Hongli Zhang · 6 authors
Electronic medical record (EMR) is a crucial form of healthcare data, currently drawing a lot of attention. Sharing health data is considered to be a critical approach to improve the quality of healthcare service and reduce medical costs. However, EMRs are fragmented across decentralized hospitals, which hinders data sharing and puts patients' privacy at risks. To address these issues, we propose a blockchain based privacy-preserving data sharing for EMRs, called BPDS. In BPDS, the original EMRs are stored securely in the cloud and the indexes are reserved in a tamper-proof consortium blockchain. By this means, the risk of the medical data leakage could be greatly reduced, and at the same time, the indexes in blockchain ensure that the EMRs can not be modified arbitrarily. Secure data sharing can be accomplished automatically according to the predefined access permissions of patients through the smart contracts of blockchain. Besides, the joint-design of the CP-ABE-based access control mechanism and the content extraction signature scheme provides strong privacy preservation in data sharing. Security analysis shows that BPDS is a secure and effective way to realize data sharing for EMRs.
Blockchain-enabled Internet of Things (IoT) systems have received extensive attention from academia and industry. Most previous constructions face the risk of leaking sensitive information since the servers can obtain plaintext data from the devices. To address this issue, in this paper, we propose a decentralized outsourcing computation (DOC) scheme, where the servers can perform fully homomorphic computations on encrypted data from the data owner according to the request of the data owner. In this process, the servers cannot obtain any plaintext data, and dishonest servers can be detected by the data owner. Then, we apply the DOC scheme in the IoT scenario to achieve a confidential blockchain-enabled IoT system, called BeeKeeper 2.0. To the best of our knowledge, this is the first work in which servers of a blockchain-enabled IoT system can perform any-degree homomorphic multiplications and any number of additions on encrypted data from devices according to the requests of the devices without obtaining any plaintext data of the devices. Finally, we provide a detailed performance evaluation for the BeeKeeper 2.0 system by deploying it on Hyperledger Fabric and using Hyperledger Caliper for performance testing. According to our tests, the time consumed between the request stage and recover stage is no more than 3.3 s, which theoretically satisfies the production needs.
Presented here is a functional implementation of Distributed Ledger Technology applied to the task of cataloguing CCTV video evidence. We describe and demonstrate a prototype camera that participates in blockchain creation in real time, and the system designed to manage and coordinate its distribution and use. This application is of specific interest to law enforcement agencies charged with the management of high volumes of CCTV evidence. We discuss applicability and scalability with reference to simulation results and real-world testing. The combination of blockchain technology with a novel digital watermarking application is demonstrated here providing immediate benefit against an existing real-world problem of trustworthy evidence protection in distributed network environments.
Open access
Advanced Steganography and Watermarking Techniques
A significant progress in modern power grids is witnessed by the tendency of becoming complex cyber‐physical systems. As a fundamental physical infrastructure, smart meter in the demand side provides real‐time energy consumption information to the utility. However, ensuring information security and privacy in the meter data aggregation process is a non‐trivial task. This study proposes a distributed, privacy‐preserving, and secure meter data aggregation framework, backed up by Blockchain and homomorphic encryption (HE) technologies. Meter data are aggregated and verified by a hierarchical Blockchain system, in which the consensus mechanism is supported by the practical Byzantine fault tolerance algorithm. On the top of the Blockchain system, HE technology is used to protect the privacy of individual meter data items during the aggregation process. Performance analysis is conducted to validate the proposed method.
Currently, the number of Internet of Thing (IoT) devices making up the IoT is more than 11 billion and this number has been continuously increasing. The prevalence of these devices leads to an emerging IoT business model called Device-as-a-service(DaaS), which enables sensor devices to collect data disseminated to all interested devices. The devices sharing data with other devices could receive some financial reward such as Bitcoin. However, side-channel attacks, which aim to exploit some information leaked from the IoT devices during data trade execution, are possible since most of the IoT devices are vulnerable to be hacked or compromised. Thus, it is challenging to securely realize data trading in IoT environment due to the information leakage such as leaking the private key for signing a Bitcoin transaction in Bitcoin system. In this paper, we propose LRCoin, a kind of leakage-resilient cryptocurrency based on bitcoin in which the signature algorithm used for authenticating bitcoin transactions is leakage-resilient. LRCoin is suitable for the scenarios where information leakage is inevitable such as IoT applications. Our core contribution is proposing an efficient bilinear-based continual-leakage-resilient ECDSA signature. We prove the proposed signature algorithm is unforgeable against adaptively chosen messages attack in the generic bilinear group model under the continual leakage setting. Both the theoretical analysis and the implementation demonstrate the practicability of the proposed scheme.
Open access
3 source records
Blockchain Technology Applications and Security
Cryptography and Data Security
Advanced Steganography and Watermarking Techniques