With the digitization of traditional medical records, medical institutions encounter difficult problems, such as electronic health record storage and sharing. Patients and doctors spend considerable time querying the required data when accessing electronic health records, but the obtained data are not necessarily correct, and access is sometimes restricted. On this basis, this study proposes a medical data sharing scheme based on permissioned blockchains, which use ciphertext-based attribute encryption to ensure data confidentiality and access control of medical data. Under premise of ensuring patient identity privacy, a polynomial equation is used to achieve an arbitrary connection of keywords, and then blockchain technology is combined. In addition, the proposed scheme has keyword-indistinguishability against adaptive chosen keyword attacks under the random oracle model. Analysis shows that the scheme has high retrieval efficiency.
It is increasingly popular to leverage the wisdom of crowd for knowledge discovery and monetization. Among others, crowdsensing with truth discovery has emerged as a promising way for leveraging the crowd wisdom, which can mine reliable knowledge from the generally unreliable sensory data contributed collected from diverse sources. Building a knowledge marketplace based on crowdsensing with truth discovery for knowledge discovery and monetization, however, is non-trivial and has to overcome several challenges. First, the sensory data should be protected as they may carry sensitive information. Second, many real crowdsensing applications usually yield sensory data in a streaming fashion, posing the demand that truth discovery should be conducted over data streams to continuously mine reliable knowledge in each data collection epoch. Third, knowledge monetization should be well treated, fully addressing the practical needs of parties in the monetization ecosystem. In this article, we take the first research attempt and propose a new full-fledged framework for building a secure knowledge marketplace over crowdsensed data streams. Our marketplace supports secure monetization of reliable knowledge mined privately from data streams in crowdsensing applications. Our framework leverages lightweight cryptographic techniques like additive secret sharing to enable privacy-preserving streaming truth discovery, continuously producing reliable knowledge over data streams. For monetization of the learned truth, i.e., knowledge, we resort to the emerging blockchain technology and deliver a tailored and full-fledged design, which promises monetization fairness, knowledge confidentiality, and streamlined processing. Extensive experiments on Amazon cloud and Ethereum blockchain demonstrate the practically affordable performance of our design.
For the modern world where data is becoming one of the most valuable assets,\nrobust data privacy policies rooted in the fundamental infrastructure of\nnetworks and applications are becoming an even bigger necessity to secure\nsensitive user data. In due course with the ever-evolving nature of newer\nstatistical techniques infringing user privacy, machine learning models with\nalgorithms built with respect for user privacy can offer a dynamically adaptive\nsolution to preserve user privacy against the exponentially increasing\nmultidimensional relationships that datasets create. Using these privacy aware\nML Models at the core of a Federated Learning Ecosystem can enable the entire\nnetwork to learn from data in a decentralized manner. By harnessing the\never-increasing computational power of mobile devices, increasing network\nreliability and IoT devices revolutionizing the smart devices industry, and\ncombining it with a secure and scalable, global learning session backed by a\nblockchain network with the ability to ensure on-device privacy, we allow any\nInternet enabled device to participate and contribute data to a global privacy\npreserving, data sharing network with blockchain technology even allowing the\nnetwork to reward quality work. This network architecture can also be built on\ntop of existing blockchain networks like Ethereum and Hyperledger, this lets\neven small startups build enterprise ready decentralized solutions allowing\nanyone to learn from data across different departments of a company, all the\nway to thousands of devices participating in a global synchronized learning\nnetwork.\n
Muqaddas Naz, Fahad Ahmed Al-Zahrani, Rabiya Khalid, Nadeem Javaid · 7 authors
In a research community, data sharing is an essential step to gain maximum knowledge from the prior work. Existing data sharing platforms depend on trusted third party (TTP). Due to the involvement of TTP, such systems lack trust, transparency, security, and immutability. To overcome these issues, this paper proposed a blockchain-based secure data sharing platform by leveraging the benefits of interplanetary file system (IPFS). A meta data is uploaded to IPFS server by owner and then divided into n secret shares. The proposed scheme achieves security and access control by executing the access roles written in smart contract by owner. Users are first authenticated through RSA signatures and then submit the requested amount as a price of digital content. After the successful delivery of data, the user is encouraged to register the reviews about data. These reviews are validated through Watson analyzer to filter out the fake reviews. The customers registering valid reviews are given incentives. In this way, maximum reviews are submitted against every file. In this scenario, decentralized storage, Ethereum blockchain, encryption, and incentive mechanism are combined. To implement the proposed scenario, smart contracts are written in solidity and deployed on local Ethereum test network. The proposed scheme achieves transparency, security, access control, authenticity of owner, and quality of data. In simulation results, an analysis is performed on gas consumption and actual cost required in terms of USD, so that a good price estimate can be done while deploying the implemented scenario in real set-up. Moreover, computational time for different encryption schemes are plotted to represent the performance of implemented scheme, which is shamir secret sharing (SSS). Results show that SSS shows the least computational time as compared to advanced encryption standard (AES) 128 and 256.
Participatory sensing is gaining popularity as a method for collecting and sharing information from distributed local environments using sensor-rich mobile devices. There are a number of participatory sensing applications currently in wide use, such as location-based service applications (e.g., Waze navigation). Usually, these participatory applications collect tremendous amounts of sensing data containing personal information, including user identity and current location. Due to the high sensitivity of this information, participatory sensing applications need a privacy-preserving mechanism, such as anonymity, to secure and protect personal user data. However, using anonymous identifiers for sensing sources proves difficult when evaluating sensing data trustworthiness. From this perspective, a successful participatory sensing application must be designed to consider two challenges: (1) user privacy and (2) data trustworthiness. To date, a number of privacy-preserving reputation techniques have been proposed to satisfy both of these issues, but the protocols contain several critical drawbacks or are impractical in terms of implementation. In particular, there is no work that can transparently manage user reputation values while also tracing anonymous identities. In this work, we present a blockchain-based privacy-preserving reputation framework called BPRF to transparently manage user reputation values and provide a transparent tracing process for anonymous identities. The performance evaluation and security analysis show that our solution is both practical and able to satisfy the two requirements for user privacy and data trustworthiness.
Meng Shen, Jie Zhang, Liehuang Zhu, Ke Xu · 5 authors
Machine learning (ML) techniques are expected to be used for specific applications in Vehicular Social Networks (VSNs). Support vector machine (SVM) is one of the typical ML methods and widely used for its high efficiency. Due to the limitation of data sources, the data collected by different entities usually contain attributes that are quite different. However, in some real-world scenarios, when training an SVM classifier, many entities face the same problem that they are lacking in data with adequate attributes. Thus multiple entities are required to share data to combine a dataset with diverse attributes and then jointly train a comprehensive classifier. However, data privacy concerns are raised because of data sharing. To sovle the problem, we propose a privacy-preserving SVM classifier training scheme over vertically-partitioned datasets posessed by multiple data providers. In our scheme, we utilize consortium blockchain and threshold homomorphic cryptosystem to establish a secure SVM classifier training platform without a trusted third-party. We keep lots of training operations locally over original data and necessary interactions between participants are protected by the threshold Paillier and consortium blockchain. Security analysis proves that our scheme can preserve the privacy of the original data and the training intermediate values. Extensive experiments indicate that our scheme has high efficiency and no accuracy loss.
Privacy-Preserving Technologies in Data
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Alevtina Dubovitskaya, Petr Novotny, Zhigang Xu, Fusheng Wang
BACKGROUND: Timely sharing of electronic health records across providers, while ensuring data security and privacy, is essential for prompt care of cancer patients, as well as for the development of medical research and the enhancement of personalized medicine. Yet, it is not trivial to achieve efficient consent management, data exchange, and access-control policy enforcement, in particular, in decentralized settings, and given the gravity of the condition such as cancer. Using blockchain technology (BCT) has been recently advocated by research communities and gained momentum from the industry perspective. However, most of the proposed solutions are at the level of a prototype, and blockchain-based healthcare data management systems are not in place yet. SUMMARY: This paper presents a systematic literature review, aiming to analyze the motivations, advantages, and limitations, as well as barriers and future challenges faced when applying the state-of-the-art distributed ledger technology in oncology. We then discuss its outcomes and propose the direction of the future research that can help to attain integration and adoption of the BCT for data-sharing, medical research, and the pharmaceutical supply chain in oncology, as well as in healthcare in general. Key Messages:BCT has the potential to enhance data-sharing (for primary care and medical research), as well as to attain optimization of the pharmaceutical supply chain by bringing properties such as transparency, traceability, and immutability to the applications. However, BCT itself cannot guarantee data privacy and security. Thus, it is never proposed as a stand-alone technology, but as a combined technology with cryptographic techniques. Regardless of the number of existing prototypes of blockchain-based healthcare systems, due to the existing barriers of the adoption (e.g., legal, social, and technological limitations), there is a lack of evaluation in real-world settings. Aiming to overcome these limitations, we propose future research directions that include design of the privacy-preserving hybrid data storage, interoperable infrastructures and architecture, and are compliant with the international laws and regulations.
As the infrastructure of the intelligent transportation system, vehicular ad hoc networks (VANETs) have greatly improved traffic efficiency. However, due to the openness characteristics of VANETs, trust and privacy are still two challenging issues in building a more secure network environment: it is difficult to protect the privacy of vehicles and meanwhile to determine whether the message sent by the vehicle is credible. In this article, a blockchain-based trust management model, combined with conditional privacy-preserving announcement scheme (BTCPS), is proposed for VANETs. First, an anonymous aggregate vehicular announcement protocol is designed to allow vehicles to send messages anonymously in the nonfully trusted environment to guarantee the privacy of the vehicle. Second, a blockchain-based trust management model is present to realize the message synchronization and credibility. Roadside units (RSUs) are able to calculate message reliability based on vehicles' reputation values which are safely stored in the blockchain. In addition, BTCPS also achieves conditional privacy since trusted authority can trace malicious vehicles' identities in anonymous announcements with the related public addresses. Finally, a mixed consensus algorithm based on proof-of-work and practical Byzantine fault tolerates algorithm is suggested for better efficiency. Security analysis and performance evaluation demonstrate that the proposed scheme is secure and effective in VANETs.
Modern services of logistics has developed and expanded rapidly in China. The information of package delivery is managed by the logistics enterprises. Thus, while enjoying the modern services of logistics, consumers face the threats of personal privacy disclosure. In order to address such challenges in modern services of logistics, we present techniques to exploit blockchain to secure logistics for protecting personal privacy. The main contribution is that we propose alogistics blockchain model. We implement the logistics blockchain on the decentralised and distributed platform, which shows that it is efficient and secure.
Dec 1, 2019·2019 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom)
Yang Yang, Jialiang Chen, Xianghan Zheng, Ximeng Liu · 6 authors
With the rapid development of computation and communication technologies, the traditional vehicle ad hoc networks (VANETs) are changing to Internet of vehicle (IoV). Vehicular announcement networks in IoV have been widely used in the communication of vehicles. Generally, we need to solve two problems while establishing a vehicular announcement system. First, we need to protect user's privacy when broadcasting the message. Second, participants usually lack the enthusiasm to reply to the announcement. To solve these two problems, we propose a novel blockchain-based incentive announcement system that not only allows participants to anonymously announce their message on the blockchain in a non-trusted environment, but also motivates witnesses to respond to the request of the traffic information with incentive mechanism. Meanwhile, traffic messages and signatures in our system are tamper-resistant, which are recorded on the blockchain. According to the security and performance analysis, it shows that our system is privacy-preserving and efficient in computation cost.
Wearable devices continuously produce physiological data that can provide individuals critical information about their daily routine or fitness level in combination with their smartphones without requiring manual calculations or maintaining log-books. Real-time participant-generated data can enable large scale observational studies of health conditions, provide better insights into medical conditions of individuals and streamline clinical trial processes in medical research. However, privacy is a major concern for health data and there can be a lack of trust among different parties in the health data collection process. In addition, individuals often do not have sufficient control over the sharing of their data from the wearable devices. The lack of control, trust and privacy are key barriers to research participants being prepared to share their personal data from wearable devices. In this work, we propose a trust model to overcome the trust deficit among different parties. Then, we present a reference system architecture, rooted on the developed trust model, that provides incentive for individuals to securely share their health data through a data marketplace. By encouraging individuals to share their real-time health data, researchers will have access to large data sets at low cost.
Hao Jin, Chen Xu, Yan Luo, Peilong Li · 6 authors
In the era of cloud computing and big data analysis, how to efficiently share and utilize medical information scattered across various care providers has become a critical problem. This paper proposes a new framework for sharing medical data in a secure and privacy-preserving way. This framework holistically integrates multi-authority attribute based encryption, blockchain and smart contract, as well as software defined networking to define and enforce sharing policies. Specifically in our framework, patients' medical records are encrypted and stored in hospital databases, where strict access controls are enforced with attribute based encryption coupled with privacy level classification. Our framework leverages blockchain technology to connect scattered private databases from participating hospitals for efficient and secure data provision, smart contracts to enable the business logic of clinical data usage, and software defined networking to revoke sharing privileges. The performance evaluation of our prototype demonstrates that the associated computation costs are reasonable in practice.
Distributed transaction model has gradually replaced the traditional centralized transaction model and has become the leading direction of development in energy trading. As the underlying support, blockchain technology is attracting more and more attention due to its advantages, i.e., integrity and non-repudiation. However, most blockchain-based trading models face the problem of privacy protection. In this paper, to solve this problem, Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is introduced as the core algorithm to reconstruct the transaction model. Specifically, we build a general model for distributed transaction called PP-BCTS (Privacy- Preserving Blockchain Trading Scheme). It can achieve fine-grained access control through transaction arbitration in ciphertext form. This design can maximize the protection of private information and can greatly improve the security and reliability of the transaction model. Additionally, a credibility-based equity proof consensus mechanism is proposed in PP-BCTS, which can greatly improve the operational efficiency. Security analysis and experimental evaluations are conducted to prove the validity and practicability of our proposed scheme.
This paper reviews current progress for privacy support in IoT blockchains. It starts by reviewing motivations for IoT architectures to incorporate blockchain capabilities and address privacy concerns. Currently available technological tools for enhancing and measuring privacy need enhancement for consumer use. Privacy concerns have moved beyond public relations angst and are emerging as regulatory imperatives. Regulations, privacy principles and impact statements move beyond traditional legal classifications of privacy violations to provide some guidance in the context of operating IoT blockchains. Legal and technical innovations have enabled new data ownership and control options. Operating IoT blockchains need adequate privacy patterns across the scope of privacy threats and the life cycles of the IoT blockchain.
The vehicular reputation management scheme based on blockchain has been studied with the rapidly development of blockchain technology. Existing works mainly focus on provisioning the security of the reputation management scheme by using the blockchain technology. However, the broadcast latency of blocks and the throughput capacity of the blockchain also have significant impacts on the performance of the reputation management scheme. This paper proposes a hierarchical blockchain based reputation management scheme in vehicular networks. Our hierarchical blockchain architecture assures that each vehicle can obtain the latest reputation value of any neighboring vehicle in a short time. Moreover, our designed blockchain structure aims at reducing the confirmation time of reputation values and increasing the capacity of blockchain. We finally conduct simulation experiments to compare our proposed scheme with the existing schemes.
With the development of social information and network technology, the era of big data has arrived. However, data security and privacy have become a bottleneck of big data development. To address this issue, the blockchain with distributed ledger as an important key technology provides an effective solution for data security in big data. In this paper, a system model based on blockchain for data sharing and security is proposed. Based on the model, a data sharing scheme has proposed. In the scheme, block consensus and data sharing as two important stages are highlighted. In the simulation, the result shows that the performance of proposed scheme is much higher than that of attribute-based encryption scheme when the size of the encrypting data is large.
Dec 1, 2019·2019 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom)
Muhammad I.H. Sukmana, Marvin Petzolt, Kennedy A. Torkura, Hendrik Graupner · 6 authors
CloudRAID for Business (CfB) is a proof-of-concept enterprise cloud storage broker (ECSB) system that provides data security in the cloud. It applies a single-authority ciphertext-based policy attribute-based encryption (CP-ABE) scheme into its key management system (KMS) to solve its scalability and access control issues for multi-users and multi-devices scenarios. Unfortunately, this approach is not suitable for managing multiple companies as CfB customers since it could not provide secure and scalable zero-knowledge file sharing in the system. In this paper, we propose the practical implementation of a modified multi-authority attribute-based encryption (MA-ABE) scheme for CfB to provide a better secure and scalable KMS and zero-knowledge file sharing in a multi-company management scenario. Our work allows each company to have the authority to manage its attributes and keys for the company's employees as CfB users while CfB manages multiple companies without global decryption power to decrypt the company's encrypted data. Our evaluation shows that our proposal provides better performance with the smaller size of ciphertext and key, faster processing time, and better security compared with current CfB system.
With the emerging artificial intelligence technology, especially machine learning and deep learning, an increasing number of healthcare institutions have designed and implemented various data-driven analysis tools and models to assist disease diagnosis. However, it is difficult for a single healthcare institution to collect sufficient medical records to support a sophisticated automatic disease recognition model, especially for some rare diseases, which urges the collaboration among different medical institutions and hospitals. The current mainstream solutions, like centralized distributed machine learning, heavily rely on a central server, which may be vulnerable to attackers or even malicious itself. Meanwhile, data privacy concerns make hospitals reluctant to share their patients' records with others. To solve these issues, in this work, we utilize the blockchain to build a decentralized privacy-preserving cross-institution disease classification framework, called Health-Chain. Specifically, we combined differential privacy and pseudo-identity mechanism to protect data privacy in distributed stochastic gradient descent (SGD) algorithm. Meanwhile, we equip gradient delay compensation to address the asynchronous issues in the decentralized blockchain-based learning system. In the experiments, we implement our Health-Chain in two popular disease recognition tasks, breast cancer diagnosis, and ECG arrhythmia classification, and demonstrate the efficiency and effectiveness of the proposed framework.
Statistical analysis of health data is an essential task in healthcare. However, existing healthcare systems are incompatible with this critical need due to privacy restrictions. A recently emerged technology called Blockchain has shown great promise for mitigating this incompatibility. In this work, we aim to improve existing secure statistical analysis protocols by leveraging the blockchain technology. We propose a novel method that enables researchers to perform statistical analysis on health data in a privacy-preserving, secure, and precise manner.
Dec 1, 2019·2019 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom)
Xi Rui, Kang Liu, Shuo Liu, Wuhui Chen · 5 authors
Recently, Internet of Vehicles (IoV) equipped with autopilot technology show much concern in their quality of service (QoS), especially in how to ensure the quality of crowdsourcing data for QoS. It is an open issue to encourage high-quality data to be sold as digital goods. Although existing works manage to design incentive mechanisms in data trading for IoV, they fail to address the trust problem. The blockchain technology has been widely studied to establish trust among participants, however, little is currently known about the perishability in the data market, which leads to the failure in explaining the price difference of digital goods. In this paper, we propose a perishability-oriented pricing mechanism to support perishable digital goods trading among IoVs. We also introduce consortium blockchain that provides distributed hyper ledger to address the trust issue in the market. By employing Stackelberg game theory, we obtain the optimal response of selfish users and providers. And finally, we propose a distributed algorithm to simulate our mechanism. Our experiment results demonstrate the efficiency of our distributed algorithm and prove the correctness and consistency of our mechanism.
The large-scale deployment of eHealth systems has brought deep impact on human society. However, the centralized Electronic health records (EHRs) outsourcing system faces some critical security and privacy issues, which have raised wide concerns in both academia and industry. Moreover, the patients lose control of their health data. There is a need to construct a decentralized and secure EHRs with more flexible control by patients themselves instead of the third party. Fortunately, we observe that the characteristics of blockchain technology such as decentralization, immutability, and auditability perfectly match these aforementioned requirements. Specifically, to satisfy our application requirements, we build a consortium blockchain (PESchain) which is maintained by a set of medical institutions. The EHRs of patients are encrypted and stored in the medical institutions by local cloud while the corresponding hash values are stored on PESchain. Moreover, to enable privacy-preserving EHRs sharing, we construct a stealth authorization scheme to achieve access authorization delivery on the blockchain. Besides, we pack the transactions according to different types to guarantee efficient block deletion. The security analysis and performance evaluation show that PESchain is secure and practical for EHRs sharing.
Growing interest in educational data mining (EDM) and learning analytics (LA) to leverage big data and to benefit education and the science of learning has made data ownership an important focus point for institutions and students. While EDM and LA can provide important information that help enhance the quality of teaching and learning, it has become critical to ensure data privacy and student agency over data. In this paper, we introduce Kratos: an immutable and publicly verifiable data management system that enables EDM and LA, while maintaining data privacy and empowering students with a user interface for data governance and participation in school processes. The system aims to achieve data interoperability, which facilitates EDM and LA as incentives to educational stakeholders (policy makers, educators, developers of education technologies, etc.), while prioritizing student agency over their data. Our system gives students and schools an immutable log along with comprehensive access to data that is otherwise scattered across systems and vendors. The underlying set of rules of the system are defined in a set of smart contracts, codified from existing non-virtual agreements [1] between schools and education technology (edutech) vendors. We propose the smart contracts to be deployed on a public blockchain (like Ethereum or Bitcoin), for notarizing and time-stamping various interactions which users of Kratos may have with data. Third parties requesting access to school data have a unique virtual token assigned to them on the blockchain which helps keep track of data modifications, access and use.