We initiate a study of super-perfect zero-knowledge proof systems. Loosely speaking, these are proof systems for which the interaction can be perfectly simulated in strict probabilistic polynomial-time. In contrast, the standard definition of perfect zero-knowledge only requires that the interaction can be perfectly simulated by a strict probabilistic polynomial-time that is allowed to fail with probability at most one half. We show that two types of perfect zero-knowledge proof systems can be transformed into super-perfect ones. The first type includes the perfect zero-knowledge interactive proof system for Graph Isomorphism and other systems of the same form, including perfect zero-knowledge arguments for NP. The second type refers to perfect non-interactive zero-knowledge proof sys-tems. We also present a super-perfect non-interactive zero-knowledge proof system for the set
Blockchain is a point-to-point distributed ledger technology based on cryptographic algorithms. However, the open and transparent blockchain ledger supplemented by statistical methods such as sociological mining and data mining has caused users' privacy to face major threats. Therefore, privacy protection has become a focus of current blockchain technology research. Ring signature technology is a commonly used encryption technology in the field of privacy protection. Therefore, this paper constructs a blockchain privacy protection scheme based on ring signature. This solution built a privacy data storage protocol based on the ring signature on the elliptic curve, and used the complete anonymity of the ring signature to ensure the security of data and user identity privacy in blockchain applications. The correctness and safety proof analysis of the proposed scheme were also carried out.
Open access
Blockchain Technology Applications and Security
Privacy-Preserving Technologies in Data
Advanced Steganography and Watermarking Techniques
In modern healthcare systems, the ability to share electronic health records is crucial for providing quality care and for enabling a larger spectrum of health services. Health data sharing is dependent on obtaining individual consent which, in turn, is hindered by a lack of resources. To this extent, blockchain-based platforms facilitate data sharing by creating a trusted distributed network of users. These users are enabled to share their data without depending on the time and resources of specific actors (such as the health services). In blockchain-based platforms, data governance mechanisms become very important due to the need to specify and monitor data sharing and data use conditions. In this article, we present a blockchain-based data-sharing consent model for access control over individual health data. We use smart contracts to dynamically represent the individual’s consent over health data and to enable data requesters to search and access those data. The dynamic consent model extends to two ontologies: the Data Use Ontology (DUO) which models the individual consent of users and the Automatable Discovery and Access Matrix (ADA-M), which describes queries from data requesters. We deploy the model on Ethereum blockchain and evaluate different data sharing scenarios. The contribution of this article consists of the creation of an individual consent model for health data sharing platforms. Such a model guarantees that individual consent is respected and that all the participants in the data sharing platform are accountable. The evaluation of our solution indicates that such a data sharing model provides a flexible decision-making approach over data usage by data requesters. Our experimental evaluation shows that the proposed model is efficient and adapts to personalized access control policies in different data-sharing scenarios.
Electronic health records (EHRs) are health information of patients that are saved digitally in a network. Various opportunities to enhance patient care, performance measures in clinical practice and contribute to clinical research in the future are provided by EHRs. The schemes used to store EHRs have been very insecure in the present era of smart cities and homes. The data can be easily breached by hackers and unauthorized external parties. Also, the data is not accessible to patients and care providers. These schemes are unable to create a balance between data security and data accessibility. But blockchain can resolve these issues. Blockchain creates a ledger system that is immutable and allows the transactions to take place in a decentralized manner. The three main features of blockchain technology - Security, Decentralization, and Transparency make any application built using it secure and not accessible by unauthorized parties. The manipulation of data is almost impossible to do in a blockchain network. In this project, we propose a system to implement EHRs using blockchain technology and make EHRs more secure and private. The blockchain technology will keep control over access to information using its cryptographic techniques and decentralization. It will also maintain the balance between data privacy and data accessibility. Our main objective of this project is the framing of data privacy and security issues in electronic healthcare
Convergence of physical and digital identity and integration of various individual records, such as patient data, into a united repository remains a serious challenge. On one hand, collecting relevant data can help clinicians, specialists and healthcare service providers to facilitate care for patients. On the other hand, Self-Sovereign identity and the right to control personal data comes into question, because patients do not handle their data explicitly. Distributed Ledger Technology (DLT) is a novel method which would allow to securely record time-stamped data and enable patient-driven health and identity records. In this paper, we review the state-of-the-art in Blockchain (BC)-based self-sovereignty and patient data records in healthcare. Our motivation is to investigate the potential of BC technology for use in the patient data and identity management. As a distributed decentralized technology, BC can be very beneficial, giving patients control over their own data and self-sovereign identity. To the extent of our knowledge, there is no literature covering the same concerns. More specifically, the focus is on solutions that aim the realization of holistic BC-based Electronic Health Records (EHR) and Patient Health Records (PHR). EHR and PHR are used to record patient data, such as the doctor's notes upon a visit and radiology images. Hence, they include critical information regarding patient's privacy and identity. Therefore, development of pure decentralized Healthcare Information Systems (HIS) is a great challenge in terms of architectural and technical structure of the systems. Designing robust and reliable EHR and PHR, which represent the foundation of many other healthcare services, relies on carefully finding the balance in a trade-off between many factors, such as level of decentralization, privacy, scalability and data throughput. In this paper, we review the state-of-the-art and provide an analysis on the design trade-offs.
Open banking enables individual customers to own their banking data, which provides fundamental support for the boosting of a new ecosystem of data marketplaces and financial services. In the near future, it is foreseeable to have decentralized data ownership in the finance sector using federated learning. This is a just-in-time technology that can learn intelligent models in a decentralized training manner. The most attractive aspect of federated learning is its ability to decompose model training into a centralized server and distributed nodes without collecting private data. This kind of decomposed learning framework has great potential to protect users' privacy and sensitive data. Therefore, federated learning combines naturally with an open banking data marketplaces. This chapter will discuss the possible challenges for applying federated learning in the context of open banking, and the corresponding solutions have been explored as well.
Tonglai Liu, Jigang Wu, Long Chen, Yalan Wu · 5 authors
In a mobile blockchain network, many mobile devices have insufficient computational capacity to execute computation-intensive tasks locally. To tackle this problem, blockchain tasks can be offloaded to edge servers with the aid of auction. However, most auction mechanisms on mobile blockchain ignore the automatic parallel execution and long-term performance. This paper aims to solve the problem of computation offloading in a mobile blockchain network. We transform this problem into a multi-choice multi-dimensional knapsack problem which is NP-hard. To improve the total utility of auction participants, this paper proposes a smart-contract-based double auction mechanism, named long-term auction for mobile blockchain (LAMB). The subtasks can be offloaded from one mobile device to heterogeneous edge servers. Also, LAMB satisfies the economic properties of an auction mechanism. Experimental results demonstrate that, the utility and utilization ratio can be achieved by 130.55% higher and 138.64% higher, respectively, in comparison to the existing auction algorithm WBD. Furthermore, the proposed LAMB can guarantee long-term performance for task offloading, and it can achieve automatic execution in an autonomous and secure environment.
High availability of data is responsible for the current trends in Artificial Intelligence (AI) and Machine Learning (ML). However, high-grade datasets are reluctantly shared between actors because of lacking trust and fear of losing control. Provenance tracing systems are a possible measure to build trust by improving transparency. Especially the tracing of AI assets along complete AI value chains bears various challenges such as trust, privacy, confidentiality, traceability, and fair remuneration. In this paper we design a graph-based provenance model for AI assets and their relations within an AI value chain. Moreover, we propose a protocol to exchange AI assets securely to selected parties. The provenance model and exchange protocol are then combined and implemented as a smart contract on a permission-less blockchain. We show how the smart contract enables the tracing of AI assets in an existing industry use case while solving all challenges. Consequently, our smart contract helps to increase traceability and transparency, encourages trust between actors and thus fosters collaboration between them.
Cloud is a computing model that provides sharing and supports ubiquitous on-demand access computing, providing new data processing and services for many industries, significantly reducing user computing and storage costs, and improving ease of use. With the development of cloud-scale and intensification, cloud security has become an essential issue in the field of cloud computing. Access control is one of the critical security technologies for protecting sensitive data stored in the cloud by enterprises and individuals. Since the centralized access control mechanism is adopted in the cloud, the sensitive data in the cloud are easy to be tampered with or leaked by hackers or cloud internal managers. To address this issue, we propose a blockchain-based access control framework with privacy protection called AuthPrivacyChain. Firstly, we use the account address of the node in blockchain as the identity, and at the same time, redefine the access control permission of data for the cloud, which is encrypted and stored in blockchain. After that, we design processes of access control, authorization, and authorization revocation in AuthPrivacyChain. Finally, we implement AuthPrivacyChain based on enterprise operation system (EOS), and the results show that AuthPrivacyChain can not only prevent hackers and administrators from illegally accessing resources, but also protect authorized privacy.
Mohammad Madine, Ammar Battah, Ibrar Yaqoob, Khaled Salah · 8 authors
Personal health records (PHRs) are valuable assets to individuals because they enable them to integrate and manage their medical data. A PHR is an electronic application through which patients can manage their health information. Giving patients control over their medical data offers an advantageous realignment of the doctor-patient dynamic. However, today's PHR management systems fall short of giving reliable, traceable, trustful, and secure patients control over their medical data, which poses serious threats to their authenticity and accuracy. Moreover, most of the current approaches and systems leveraged for managing PHR are centralized that not only make medical data sharing difficult but also poses a risk of single point of failure problem. In this paper, we propose Ethereum blockchain-based smart contracts to give patients control over their data in a manner that is decentralized, immutable, transparent, traceable, trustful, and secure. The proposed system employs decentralized storage of interplanetary file systems (IPFS), proxy re-encryption, and trusted reputation-based oracles to securely fetch, store, and share patients' medical data. We present algorithms along with their full implementation details. We evaluate the proposed smart contracts using two important performance metrics, such as cost and correctness. Furthermore, we provide security analysis and discuss the generalization aspects of our solution. We outline the limitations of the proposed approach. We make the smart contract source code publicly available on Github.
Md. Abdur Rahman, M. Shamim Hossain, Mohammad Saiful Islam, Nabil Alrajeh · 5 authors
Recent advancements in the Internet of Health Things (IoHT) have ushered in the wide adoption of IoT devices in our daily health management. For IoHT data to be acceptable by stakeholders, applications that incorporate the IoHT must have a provision for data provenance, in addition to the accuracy, security, integrity, and quality of data. To protect the privacy and security of IoHT data, federated learning (FL) and differential privacy (DP) have been proposed, where private IoHT data can be trained at the owner's premises. Recent advancements in hardware GPUs even allow the FL process within smartphone or edge devices having the IoHT attached to their edge nodes. Although some of the privacy concerns of IoHT data are addressed by FL, fully decentralized FL is still a challenge due to the lack of training capability at all federated nodes, the scarcity of high-quality training datasets, the provenance of training data, and the authentication required for each FL node. In this paper, we present a lightweight hybrid FL framework in which blockchain smart contracts manage the edge training plan, trust management, and authentication of participating federated nodes, the distribution of global or locally trained models, the reputation of edge nodes and their uploaded datasets or models. The framework also supports the full encryption of a dataset, the model training, and the inferencing process. Each federated edge node performs additive encryption, while the blockchain uses multiplicative encryption to aggregate the updated model parameters. To support the full privacy and anonymization of the IoHT data, the framework supports lightweight DP. This framework was tested with several deep learning applications designed for clinical trials with COVID-19 patients. We present here the detailed design, implementation, and test results, which demonstrate strong potential for wider adoption of IoHT-based health management in a secure way.
Open access
Privacy-Preserving Technologies in Data
Blockchain Technology Applications and Security
Artificial Intelligence in Healthcare and Education
Distributed Ledger Technologies (DLTs), like Blockchain, are characterized by features such as transparency, traceability, and security by design. These features make the adoption of Blockchain attractive to enhance information security, privacy, and trustworthiness in very different contexts. This paper provides a comprehensive survey and aims at analyzing and assessing the use of Blockchain in the context of Distributed Trust and Reputation Management Systems (DTRMS). The analysis includes academic research as well as initiatives undertaken in the business domain. The paper defines two taxonomies for both Blockchain and DTRMS and applies a Formal Concept Analysis. Such an approach allowed us to identify the most recurrent and stable features in the current scientific landscape and several important implications among the two taxonomies. The results of the analysis have revealed significant trends and emerging practices in the current implementations that have been distilled into recommendations to guide Blockchain's adoption in DTRMS systems.
Secure computation protocols enable mutually distrusting parties to compute a function of their private inputs while revealing nothing but the output. Protocols with full security (also known as guaranteed output delivery) in particular protect against denial-of-service attacks, guaranteeing that honest parties receive a correct output. This feature can be realized in the presence of an honest majority, and significant research effort has gone toward attaining full security with good asymptotic and concrete efficiency.
Vadim Lyubashevsky, Ngoc Khanh Nguyen, Gregor Seiler
We present a novel lattice-based zero-knowledge proof system for showing that (arbitrary-sized) committed integers satisfy additive and multiplicative relationships. The proof sizes of our schemes are between two to three orders of magnitude smaller than in the lattice proof system of Libert et al. (CRYPTO 2018) for the same relations. Because the proof sizes of our protocols grow linearly in the integer length, our proofs will eventually be longer than those produced by quantum-safe succinct proof systems for general circuits (e.g. Ligero, Aurora, etc.). But for relations between reasonably-sized integers (e.g. $512$-bit), our proofs still result in the smallest zero-knowledge proof system based on a quantum-safe assumption. Of equal importance, the run-time of our proof system is at least an order of magnitude faster than any other quantum-safe scheme.
Incorporation of connected vehicle (CV) data into real-time traffic management systems presents a host of new challenges resulting from the current lack of data integrity and data privacy in traffic networks. Over the past few years, blockchain technologies have been inspiring extensive innovations in the transportation field. However, due to the transparency property, sensitive data stored on the blockchain would be accessible to anyone, resulting in a lack of privacy. In this paper, we propose a decentralized and location-aware architecture to address the data integrity along with the privacy-preserving issues in blockchain-based traffic management systems. Our proposed architecture integrates with permissioned and modular blockchain network and non-interactive zero-knowledge range proof (ZKRP) protocol. We develop the prototype system on the Hyperledger Fabric platform and Hyperledger Ursa cryptographic library. The performance results show that our approach is effective and feasible for real-time traffic management while preserving the data privacy requirements.
Lasse Herskind, Panagiota Katsikouli, Nicola Dragoni
Our transaction history in the current centralized banking system has the ability to reveal a lot of private information for each spender, both to the banking system itself, but also to those entities that surround it (e.g., governments, industry etc). Examples of leaking information constitute the amounts spent, the goods on which the amounts were spent, the spending locations and the users we exchange money with. This knowledge is powerful in the hands of those who have it, and can be used in multiple ways, not always to our benefit. Cryptocurrencies, such as the famous Bitcoin, were proposed as a means to address the limitations of centralized banking systems and to offer its users privacy with regards to their transactional data. In this work, we perform a systematic literature review on the realm of privacy for electronic currencies. We present the development of digital money from electronic cash to cryptocurrencies and focus on the techniques that are employed to enhance user-privacy. Furthermore, we present flaws of the current cryptocurrency systems, which reduce the privacy of the cryptocurrency users. Finally, we describe three research directions to enhance privacy for cryptocurrencies: transaction propagation mechanisms, succinct ZK proof systems without a trusted setup, and specialised trustless zero-knowledge proofs.
Mirko Zichichi, Michele Contu, Stefano Ferretti, Victor Rodrı́guez-Doncel
Personal data has undoubtedly assumed a great value with the advancements on technologies able to gather it and infer from it. The businesses that operate in a data-driven economy offer services that rely on data collected about their users and usually they store this personal information in “silos” that impede transparency on their use and possibilities of easy interactions. The introduction in EU of the General Data Protection Regulation (GDPR) moves this economy towards a user-centered vision, in which individuals have rights for their data sovereignty and the free portability of it. However, more efforts are needed to reach both transparency and balance between privacy and data sharing. In this paper, we present a solution to promote the development of personal data marketplaces, exploiting the use of Distributed Ledger Technologies (DLTs) and a Sensing-as-a-Service (SaaS) model, in order to enhance the privacy of individuals, following the principles of personal data sovereignty and interoperability. Moreover, we provide experimental results of an implementation based on IOTA, a promising DLT for managing and transacting IoT data
Electronic medical records can help people prevent diseases, improve cure rates, provide a significant basis for medical institutions and pharmaceutical companies, and provide legal evidence for medical negligence and medical disputes. However, the integrity and security problems of electronic medical data still intractable. In this paper, based on the ciphertext policy attribute-based encryption system and IPFS storage environment, combined with blockchain technology, we constructed an attribute-based encryption scheme for secure storage and efficient sharing of electronic medical records in IPFS storage environment. Our scheme is based on ciphertext policy attribute encryption, which effectively controls the access of electronic medical data without affecting efficient retrieval. Meanwhile, we store the encrypted electronic medical data in the decentralized InterPlanetary File System (IPFS), which not only ensures the security of the storage platform but also solves the problem of the single point of failure. Besides, we leverage the non-tamperable and traceable nature of blockchain technology to achieve secure storage and search for medical data. The security proof shows that our scheme achieves selective security for the choose keyword attacks. Performance analysis and real data set simulation experiments shows that our scheme is efficient and feasible.
Applications of Blockchain (BC) technology and Cyber-Physical Systems (CPS) are increasing exponentially. However, framing resilient and correct smart contracts (SCs) for these smart application is a quite challenging task because of the complexity associated with them. SC is modernizing the traditional industrial, technical, and business processes. It is self-executable, self-verifiable, and embedded into the BC that eliminates the need for trusted third-party systems, which ultimately saves administration as well as service costs. It also improves system efficiency and reduces the associated security risks. However, SCs are well encouraging the new technological reforms in Industry 4.0, but still, various security and privacy challenges need to be addressed. In this paper, a survey on SC security vulnerabilities in the software code that can be easily hacked by a malicious user or may compromise the entire BC network is presented. As per the literature, the challenges related to SC security and privacy are not explored much by the authors around the world. From the existing proposals, it has been observed that designing a complex SCs cannot mitigate its privacy and security issues. So, this paper investigates various Artificial Intelligence (AI) techniques and tools for SC privacy protection. Then, open issues and challenges for AI-based SC are analyzed. Finally, a case study of retail marketing is presented, which uses AI and SC to preserve its security and privacy.
McCorry et al. (Financial Cryptography 2017) presented the first implementation of a decentralized self-tallying voting protocol on Ethereum. However, their implementation did not scale beyond 40 voters since all the computations were performed on the smart contract. In this paper, we tackle this problem by delegating the bulk computations to an off-chain untrusted administrator in a verifiable manner. Specifically, the administrator tallies the votes off-chain and publishes a Merkle tree that encodes the tallying computation trace. Then, the administrator submits the Merkle tree root and the tally result to the smart contract. Subsequently, the smart contract transits to an intermediate phase where at least a single honest voter can contend the administrator’s claimed result if it was not computed correctly. Then, in the worst case, the smart contract verifies the dispute at the cost of an elliptic curve point addition and scalar multiplication, and two Merkle proofs of membership which are logarithmic in the number of voters. This allows our protocol to achieve higher scalability without sacrificing the public verifiability or voters’ privacy. To assess our protocol, we implemented an open-source prototype on Ethereum and carried out multiple experiments for different numbers of voters. The results of our implementation confirm the scalability and efficiency of our proposed solution which does not exceed the current block gas limit for any practical number of voters.
Konstantin D. Pandl, Scott Thiebes, Manuel Schmidt-Kraepelin, Ali Sunyaev
Developments in artificial intelligence (AI) and distributed ledger technology (DLT) currently lead to lively debates in academia and practice. AI processes data to perform tasks that were previously thought possible only for humans. DLT has the potential to create consensus over data among a group of participants in untrustworthy environments. In recent research, both technologies are used in similar and even the same systems. This can lead to a convergence of AI and DLT, which in the past, has paved the way for major innovations of other information technologies. Previous work highlights several potential benefits of a convergence of AI and DLT but only provides a limited theoretical framework to describe upcoming real-world integration cases of both technologies. In this research, we review and synthesize extant research on integrating AI with DLT and vice versa to rigorously develop a future research agenda on the convergence of both technologies. In terms of integrating AI with DLT, we identified research opportunities in the areas of secure DLT, automated referee and governance, and privacy-preserving personalization. With regard to integrating DLT with AI, we identified future research opportunities in the areas of decentralized computing for AI, secure data sharing and marketplaces, explainable AI, and coordinating devices. In doing so, this research provides a four-fold contribution. First, it is not constrained to blockchain but instead investigates the broader phenomenon of DLT. Second, it considers the reciprocal nature of a convergence of AI and DLT. Third, it bridges the gap between theory and practice by helping researchers active in AI or DLT to overcome current limitations in their field, and practitioners to develop systems along with the convergence of both technologies. Fourth, it demonstrates the feasibility of applying the convergence concept to research on AI and DLT.
Markus Lücking, Niclas Kannengieber, Maurice Kilgus, Till Riedel · 7 authors
Pollution-monitoring systems (PMSs) are used worldwide to sense environmental changes, such as air quality conditions or temperature increases, and to monitor compliance with regulations. However, organizations manage the environmental data collected by such PMSs in a centralized manner, which is why recorded environmental data are vulnerable to manipulation. Moreover, the analysis of pollution data often lacks transparency to outsiders, which may lead to wrong decisions regarding environmental regulations. To address these challenges, we propose a software design for PMSs based on distributed ledger technology (DLT) and the long-range (LoRa) protocol for flexible, transparent, and energy-efficient environment monitoring and data management. To design the PMS, we conducted a comprehensive requirements analysis for PMSs. We benchmarked different consensus mechanisms (e.g., BFT-SMaRt and Raft) and digital signature schemes (e.g., ECDSA and EdDSA) to adequately design the PMS and fulfill the identified requirements. On this basis, we designed and implemented a prototype PMS and evaluated it in the field. The evaluation shows the effectiveness of DLT-based PMSs that include portable low-energy sensor nodes and demonstrates the applicability of the proposed software design for PMSs in contexts other than air pollution.