Blockchain Papers

Muneeb Ul Hassan, Mubashir Husain Rehmani, Jinjun Chen

No abstract is available for this record.

Jie Xiong, Qi Wang

ABSTRACT The Ethereum block chain as a decentralized platform is so successful that many applications deployed on it. However, for the inherent transparency properties and the lack of privacy, deploying a financial application on top of it is always a challenge. In this paper, we tackle this challenge and propose an anonymous sealed-bid auction protocol based on time-released encryption atop Consortium Block chain. We adopt a strict digital certificate-based identity mechanism of the consortium block chain to permit legitimate participants, and utilize the blind signature based on elliptic curve technology to allowing anonymous participation. Moreover, a timed release public key encryption algorithm is adopted to encrypt bids and prevent auctioneer from colluding with bidders. This is completely different from the method (zero-knowledge proof) used in other papers to prevent collusion between auctioneer and bidder. We provide a specific analysis of our protocol, which shows that our protocol meets anonymity and applicability. KEYWORDS Consortium Block chain, Smart Contract, Sealed-Bid Auction, Time-Released Encryption, Blind signature

Changbing Tang, Luya Wu, Guanghui Wen, Zheng Zheng

The core security of proof-of-work (PoW)-based blockchain networks, relies on PoW consensus algorithm and requires miners solving a crypto-puzzles of hash computation. However, the mining process in the blockchain is resource-intensive where only the first miner who completes full PoW will be rewarded. Therefore, miners exhibit malicious behaviors which cause a waste of distributed computation resource, even posing a threat on the efficiency of blockchain networks. In this brief, we propose a new reputation-based mechanism for the PoW computation in the blockchain, in which miners are incentivized to conduct honest mining. Based on the game theory, we design a reputation-based algorithm to encourage honest mining of miners, and thereby increase the overall revenue of the pool. In addition, numerical illustrations are also presented to support the performance of our proposed mechanism.

Andrea Coladangelo

We put forward the idea that classical blockchains and smart contracts are potentially useful primitives not only for classical cryptography, but for quantum cryptography as well. Abstractly, a smart contract is a functionality that allows parties to deposit funds, and release them upon fulfillment of algorithmically checkable conditions, and can thus be employed as a formal tool to enforce monetary incentives. In this work, we give the first example of the use of smart contracts in a quantum setting. We describe a simple hybrid classical-quantum payment system whose main ingredients are a classical blockchain capable of handling stateful smart contracts, and quantum lightning, a strengthening of public-key quantum money introduced by Zhandry [Eurocrypt 2019]. Our hybrid payment system uses quantum states as banknotes and a classical blockchain to settle disputes and to keep track of the valid serial numbers. It has several desirable properties: it is decentralized, requiring no trust in any single entity; payments are as quick as quantum communication, regardless of the total number of users; when a quantum banknote is damaged or lost, the rightful owner can recover the lost value.

Evangelos Pournaras, Srivatsan Yadhunathan, Ada Diaconescu

Structure plays a key role in learning performance. In centralized computational systems, hyperparameter optimization and regularization techniques such as dropout are computational means to enhance learning performance by adjusting the deep hierarchical structure. However, in decentralized deep learning by the Internet of Things, the structure is an actual network of autonomous interconnected devices such as smart phones that interact via complex network protocols. Self-adaptation of the learning structure is a challenge. Uncertainties such as network latency, node and link failures or even bottlenecks by limited processing capacity and energy availability can significantly downgrade learning performance. Network self-organization and self-management is complex, while it requires additional computational and network resources that hinder the feasibility of decentralized deep learning. In contrast, this paper introduces a self-adaptive learning approach based on holarchic learning structures for exploring, mitigating and boosting learning performance in distributed environments with uncertainties. A large-scale performance analysis with 864,000 experiments fed with synthetic and real-world data from smart grid and smart city pilot projects confirm the cost-effectiveness of holarchic structures for decentralized deep learning.

David Froelicher, Juan Ramón Troncoso-Pastoriza, João Sá Sousa, Jean‐Pierre Hubaux

Data sharing has become of primary importance in many domains such as big-data analytics, economics and medical research, but remains difficult to achieve when the data are sensitive. In fact, sharing personal information requires individuals' unconditional consent or is often simply forbidden for privacy and security reasons. In this paper, we propose Drynx, a decentralized system for privacy-conscious statistical analysis on distributed datasets. Drynx relies on a set of computing nodes to enable the computation of statistics such as standard deviation or extrema, and the training and evaluation of machine-learning models on sensitive and distributed data. To ensure data confidentiality and the privacy of the data providers, Drynx combines interactive protocols, homomorphic encryption, zero-knowledge proofs of correctness, and differential privacy. It enables an efficient and decentralized verification of the input data and of all the system's computations thus provides auditability in a strong adversarial model in which no entity has to be individually trusted. Drynx is highly modular, dynamic and parallelizable. Our evaluation shows that it enables the training of a logistic regression model on a dataset (12 features and 600,000 records) distributed among 12 data providers in less than 2 seconds. The computations are distributed among 6 computing nodes, and Drynx enables the verification of the query execution's correctness in less than 22 seconds.

David Froelicher, Juan Ramón Troncoso-Pastoriza, João M. C. Sousa, Jean‐Pierre Hubaux

Data sharing has become of primary importance in many domains such as\nbig-data analytics, economics and medical research, but remains difficult to\nachieve when the data are sensitive. In fact, sharing personal information\nrequires individuals' unconditional consent or is often simply forbidden for\nprivacy and security reasons. In this paper, we propose Drynx, a decentralized\nsystem for privacy-conscious statistical analysis on distributed datasets.\nDrynx relies on a set of computing nodes to enable the computation of\nstatistics such as standard deviation or extrema, and the training and\nevaluation of machine-learning models on sensitive and distributed data. To\nensure data confidentiality and the privacy of the data providers, Drynx\ncombines interactive protocols, homomorphic encryption, zero-knowledge proofs\nof correctness, and differential privacy. It enables an efficient and\ndecentralized verification of the input data and of all the system's\ncomputations thus provides auditability in a strong adversarial model in which\nno entity has to be individually trusted. Drynx is highly modular, dynamic and\nparallelizable. Our evaluation shows that it enables the training of a logistic\nregression model on a dataset (12 features and 600,000 records) distributed\namong 12 data providers in less than 2 seconds. The computations are\ndistributed among 6 computing nodes, and Drynx enables the verification of the\nquery execution's correctness in less than 22 seconds.\n

Yannan Li, Willy Susilo, Guomin Yang, Yong Yu · 6 authors

The Internet of Things (IoT) is experiencing explosive growth and has gained extensive attention from academia and industry in recent years. Most of the existing IoT infrastructures are centralized, in which the presence of a cloud server is mandatory. However, centralized frameworks suffer from the issues of unscalability and single-point-of-failure. Consequently, decentralized IoT has been proposed by taking advantage of the emerging technology of Blockchain. Voting systems are widely adopted in IoT, such as a leader election in wireless sensor networks. Self-tallying voting systems are alternatives to traditional centralized voting systems in decentralized IoT since the traditional ones are not suitable for such scenarios. Unfortunately, self-tallying voting systems inherently suffer from fairness issues, such as adaptive and abortive issues caused by malicious voters. In this paper, we introduce a framework of self-tallying systems in decentralized IoT based on Blockchain. We propose a concrete construction and prove the proposed system satisfies all the security requirements including fairness, dispute-freeness and maximal ballot secrecy. The implementations on mobile phones demonstrate the practicability of our system.

Lingyue Zhang, Huilin Li, Yannan Li, Yong Yu · 6 authors

Cryptocurrencies, led by bitcoin launched in 2009, have obtained wide attention due to the emerging Blockchain in recent years. Anonymous cryptocurrencies are highly essential since users want to preserve their privacy when conducting transactions. However, some users might misbehave with the cover of anonymity such as rampant trafficking and extortion. Thus, it is important to balance anonymity and accountability of anonymous cryptocurrencies. In this paper, we solve this issue by proposing a linkable group signature (LGS) for signing cryptocurrency transactions, which can be used to trace a payer's identity in consortium blockchain based anonymous cryptocurrencies, in case the payer tries illegal activities. A payer keeps anonymous if he/she behaves honestly. We prove that the proposed scheme achieves full-anonymity, full-traceability and linkability in the random oracle. Implementation of the proposed LGS scheme demonstrates its high efficiency thus, can be adopted in anonymous cryptocurrencies in reality.

David Hawig, C. Zhou, Sebastian Fuhrhop, Andre S Fialho · 5 authors

BACKGROUND Distributed ledger technology (DLT) holds great potential to improve health information exchange. However, the immutable and transparent character of this technology may conflict with data privacy regulations and data processing best practices. OBJECTIVE The aim of this paper is to develop a proof-of-concept system for immutable, interoperable, and General Data Protection Regulation (GDPR)–compliant exchange of blood glucose data. METHODS Given that there is no ideal design for a DLT-based patient-provider data exchange solution, we proposed two different variations for our proof-of-concept system. One design was based purely on the public IOTA distributed ledger (a directed acyclic graph-based DLT) and the second used the same public IOTA ledger in combination with a private InterPlanetary File System (IPFS) cluster. Both designs were assessed according to (1) data reversal risk, (2) data linkability risks, (3) processing time, (4) file size compatibility, and (5) overall system complexity. RESULTS The public IOTA design slightly increased the risk of personal data linkability, had an overall low processing time (requiring mean 6.1, SD 1.9 seconds to upload one blood glucose data sample into the DLT), and was relatively simple to implement. The combination of the public IOTA with a private IPFS cluster minimized both reversal and linkability risks, allowed for the exchange of large files (3 months of blood glucose data were uploaded into the DLT in mean 38.1, SD 13.4 seconds), but involved a relatively higher setup complexity. CONCLUSIONS For the specific use case of blood glucose explored in this study, both designs presented a suitable performance in enabling the interoperable exchange of data between patients and providers. Additionally, both systems were designed considering the latest guidelines on personal data processing, thereby maximizing the alignment with recent GDPR requirements. For future works, these results suggest that the conflict between DLT and data privacy regulations can be addressed if careful considerations are made regarding the use case and the design of the data exchange system.

Karuna Pande Joshi, Agniva Banerjee

An essential requirement of any information management system is to protect data and resources against breach or improper modifications, while at the same time ensuring data access to legitimate users. Systems handling personal data are mandated to track its flow to comply with data protection regulations. We have built a novel framework that integrates semantically rich data privacy knowledge graph with Hyperledger Fabric blockchain technology, to develop an automated access-control and audit mechanism that enforces users’ data privacy policies while sharing their data with third parties. Our blockchain based data-sharing solution addresses two of the most critical challenges: transaction verification and permissioned data obfuscation. Our solution ensures accountability for data sharing in the cloud by incorporating a secure and efficient system for End-to-End provenance. In this paper, we describe this framework along with the comprehensive semantically rich knowledge graph that we have developed to capture rules embedded in data privacy policy documents. Our framework can be used by organizations to automate compliance of their Cloud datasets.

Jiawen Kang, Zehui Xiong, Dusit Niyato, Dongdong Ye · 6 authors

In the Internet of Vehicles (IoV), data sharing among vehicles is critical for improving driving safety and enhancing vehicular services. To ensure security and traceability of data sharing, existing studies utilize efficient delegated proof-of-stake consensus scheme as hard security solutions to establish blockchain-enabled IoV (BIoV). However, as the miners are selected from miner candidates by stake-based voting, defending against voting collusion between the candidates and compromised high-stake vehicles becomes challenging. To address the challenge, in this paper, we propose a two-stage soft security enhancement solution: 1) miner selection and 2) block verification. In the first stage, we design a reputation-based voting scheme to ensure secure miner selection. This scheme evaluates candidates' reputation using both past interactions and recommended opinions from other vehicles. The candidates with high reputation are selected to be active miners and standby miners. In the second stage, to prevent internal collusion among active miners, a newly generated block is further verified and audited by standby miners. To incentivize the participation of the standby miners in block verification, we adopt the contract theory to model the interactions between active miners and standby miners, where block verification security and delay are taken into consideration. Numerical results based on a real-world dataset confirm the security and efficiency of our schemes for data sharing in BIoV.

Dominic Deuber, Bernardo Magri, Sri Aravinda Krishnan Thyagarajan

Bitcoin is an immutable permissionless blockchain system that has been extensively used as a public bulletin board by many different applications that heavily relies on its immutability. However, Bitcoin's immutability is not without its fair share of demerits. Interpol exposed the existence of harmful and potentially illegal documents, images and links in the Bitcoin blockchain, and since then there have been several qualitative and quantitative analysis on the types of data currently residing in the Bitcoin blockchain. Although there is a lot of attention on blockchains, surprisingly the previous solutions proposed for data redaction in the permissionless setting are far from feasible, and require additional trust assumptions. Hence, the problem of harmful data still poses a huge challenge for law enforcement agencies like Interpol (Tziakouris, IEEE S&P'18). We propose the first efficient redactable blockchain for the permissionless setting that is easily integrable into Bitcoin, and that does not rely on heavy cryptographic tools or trust assumptions. Our protocol uses a consensus-based voting and is parameterised by a policy that dictates the requirements and constraints for the redactions; if a redaction gathers enough votes the operation is performed on the chain. As an extra feature, our protocol offers public verifiability and accountability for the redacted chain. Moreover, we provide formal security definitions and proofs showing that our protocol is secure against redactions that were not agreed by consensus. Additionally, we show the viability of our approach with a proof-of-concept implementation that shows only a tiny overhead in the chain validation of our protocol when compared to an immutable one.

Sarah Azouvi, Haaroon Yousaf, Alexander Hicks

Privacy was one of the key points mentioned in Nakamoto's Bitcoin whitepaper, and one of the selling points of Bitcoin in its early stages. In hindsight, however, de-anonymising Bitcoin users turned out to be more feasible than expected. Since then, privacy focused cryptocurrencies such as Zcash and Monero have surfaced. Both of these examples cannot be described as fully successful in their aims, as recent research has shown. Incentives are integral to the security of cryptocurrencies, so it is interesting to investigate whether they could also be aligned with privacy goals. A lack of privacy often results from low user counts, resulting in low anonymity sets. Could users be incentivised to use the privacy preserving implementations of the systems they use? Not only is Zcash much less used than Bitcoin (which it forked from), but most Zcash transactions are simply transparent transactions, rather than the (at least intended to be) privacy-preserving shielded transactions. This paper and poster briefly discusses how incentives could be incorporated into systems like cryptocurrencies with the aim of achieving privacy goals. We take Zcash as example, but the ideas discussed could apply to other privacy-focused cryptocurrencies. This work was presented as a poster at OPERANDI 2018, the poster can be found within this short document.

Amandine Jambert

No abstract is available for this record.

Jiale Guo, Wenzhuo Yang, Kwok‐Yan Lam, Xun Yi

No abstract is available for this record.

Mark Bell, Alex Green, John Sheridan, John Collomosse · 8 authors

Archives have well-established practices which have been developed over years of working with analogue records. Now they face huge challenges due to the inexorable development of digital technologies. Not only is the heterogeneous nature of the records, their instability and the rapid pace of technological development a threat to the records’ survival, but the ease with which digital records can be altered has put archives in a technology arms race with those parties who would seek to falsify our digital inheritance and undermine democracy.In order to tackle these challenges, the ARCHANGEL project is breaking new ground by using blockchain to record checksums (cryptographic hashes) and other metadata derived from either scanned physical records or born-digital records to allow verification of their integrity over decade- or century-long time spans. This data is permanently preserved through peer-to-peer distribution and consensus checking without the need for a trusted third party, thereby enabling archives to prove the authenticity of the records in their custody.

Daniel Amo-Filvà, David Fonseca, Marc Alier, Francisco José García‐Peñalvo · 5 authors

No abstract is available for this record.

Dimaz Ankaa Wijaya, Joseph K. Liu, Ron Steinfeld, Dongxi Liu · 5 authors

No abstract is available for this record.

Mirko Zichichi

Data protection is about protecting information about per-sons, which is currently flowing without much control –individuals can-not easily exercise the rights granted by the EU General Data Protection Regulation (GDPR). Individuals benefit from “free” services offered by companies in exchange of their data, but these companies keep their users’ data in “silos” that impede transparency on their use and possibilities of easy interactions. The introduction of the GDPR warrants control rights to individuals and the free portability of personal data from one entity to another. However it is still beyond the individual’s capability to perceive whether their data is managed in compliance with GDPR. To this regard, in this work the proposed approach consists in using decentralized mechanisms to provide transparency through distributed ledgers, data flow governance by using smart contracts and interoperability relying on semantic web technologies.

Jens Leicht, Maritta Heisel

Many Internet users employ a multitude of online services. Many services require the same data to be entered and users enter it repeatedly. Instead of entering information for every new service a user wants to use, we propose a system that allows users to simply share a set of information with any service they want to use. The information is entered once and stored in a distributed storage system. Users can easily share the data with any service provider, in order to use a service. Our proposed system makes use of the distributed ledger, provided by blockchains, to manage access rights. By taking the data away from the service providers, the personal data is also protected against unwanted data leaks.

M. Magdalena Payeras–Capellà, Macià Mut–Puigserver, Miquel À. Cabot-Nadal

European Regulation (EU) 910/2014 lays down the rules for electronic identification and trust services for electronic transactions. Qualified Electronic Registered Delivery is one of the trust services included in the regulation, and it requires nonrepudiation of origin and reception together with the integrity of the data. This kind of services usually relies heavily on the use of trusted third parties. These entities are an obstacle to extend the use of protocols. In this paper, we use the blockchain-based technologies to provide, for the first time, two multiparty registered eDelivery schemes that reduce the involvement of trusted third parties compared with traditional approaches while satisfying the requirements of the norms created by the European Union for registered eDeliveries. Since confidentiality is not considered a compulsory property in the directive, we propose two protocols. The first one is well suited for those deliveries that do not require the confidentiality of the message or delivered data or even for those in which it is required that the message can be public and accessible to everybody. The second solution for multiparty registered eDelivery allows the message to be hidden to others than the receiver. We present the smart contracts for both proposals and also a complete analysis of their properties and performance.

Zhen Liu, Khoa Nguyen, Guomin Yang, Huaxiong Wang · 5 authors

No abstract is available for this record.

Marielle S. Gross, Robert C. Miller

We propose that blockchain technology complemented by secure computation methods can foster implementation of a learning healthcare system (LHCS) by minimizing upfront patient-facing compromises with unsurpassed data security and privacy, and by optimizing the system’s fulfillment of its obligations to respect patients through transparency, engagement, and accountability. We demonstrate how a blockchain-enabled LHCS could foster patient willingness to contribute to learning by providing desired security and control over health data. In addition, secure computation methods could enable meta-analysis without exposing individual-level data, thus allowing the system to protect patients’ privacy while simultaneously learning from their data. The transparency and immutability of blockchain ledgers would also support the public’s trust in the system by allowing patients to audit and oversee which of their data are used, how they are used, and by whom. Furthermore, blockchain communities are community-governed peer-to-peer networks in which sharing builds mutually beneficial value, offering a model for engaging patients as LHCS stakeholders. Smart contracts could be used to ensure accountability of the system by embedding feedback mechanisms by which patients directly and automatically realize benefits of sharing their data.