September 2020 list
If you feel a paper should belong to another category, or that we missed a relevant paper just let us know. Participation is most welcome!
- Attacks and defenses
- Blockchain-noncrypto uses
- Internet of Things (IoT)
- Proof of Work (PoW) alternatives
- Smart contracts
Attacks and defenses
ArchiveSafe: Mass-Leakage-Resistant Storage from Proof-of-Work
Authors: Moe Sabry, Reza Samavi, Douglas Stebila
Abstract: Data breaches-mass leakage of stored information-are a major security concern. Encryption can provide confidentiality, but encryption depends on a key which, if compromised, allows the attacker to decrypt everything, effectively instantly. Security of encrypted data thus becomes a question of protecting the encryption keys. In this paper, we propose using keyless encryption to construct a mass leakage resistant archiving system, where decryption of a file is only possible after the requester, whether an authorized user or an adversary, completes a proof of work in the form of solving a cryptographic puzzle. This proposal is geared towards protection of infrequently-accessed archival data, where any one file may not require too much work to decrypt, decryption of a large number of files-mass leakage-becomes increasingly expensive for an attacker. We present a prototype implementation realized as a user-space file system driver for Linux. We report experimental results of system behaviour under different file sizes and puzzle difficulty levels. Our keyless encryption technique can be added as a layer on top of traditional encryption: together they provide strong security against adversaries without the key and resistance against mass decryption by an attacker.
Share Withholding Attack in Blockchain Mining: Technical Report
Authors: Sang-Yoon Chang
Abstract: Cryptocurrency achieves distributed consensus using proof of work (PoW). Prior research in blockchain security identified financially incentivized attacks based on withholding blocks which have the attacker compromise a victim pool and pose as a PoW contributor by submitting the shares (earning credit for mining) but withholding the blocks (no actual contributions to the pool). We advance such threats to generate greater reward advantage to the attackers while undermining the other miners and introduce the share withholding attack (SWH). SWH withholds shares to increase the attacker’s reward payout within the pool, in contrast to the prior threats withholding blocks, and rather builds on the block-withholding threats in order to exploit the information about the impending block submission timing, challenging the popularly established assumption that the block submission time is completely random and unknown to miners. We analyze SWH’s incentive compatibility and the vulnerability scope by identifying the critical systems and environmental parameters which determine the attack’s impact. Our results show that SWH in conjunction with block withholding yield unfair reward advantage at the expense of the protocol-complying victim miners and that a rational miner will selfishly launch SWH to maximize its reward profit. We inform the blockchain and cryptocurrency research of the novel SWH threat and include the potential countermeasure directions to facilitate such research and development.
Stochastic Modeling Approaches for Analyzing Blockchain: A Survey
Authors: Hongyue Kang, Xiaolin Chang, Jelena Mišić, B. Vojislav Mišić, Yingying Yao, Zhi Chen
Abstract: Blockchain technology has been attracting much attention from both academia and industry. It brings many benefits to various applications like Internet of Things. However, there are critical issues to be addressed before its widespread deployment, such as transaction efficiency, bandwidth bottleneck, and security. Techniques are being explored to tackle these issues. Stochastic modeling, as one of these techniques, has been applied to analyze a variety of blockchain characteristics, but there is a lack of a comprehensive survey on it. In this survey, we aim to fill the gap and review the stochastic models proposed to address common issues in blockchain. Firstly, this paper provides the basic knowledge of blockchain technology and stochastic models. Then, according to different objects, the stochastic models for blockchain analysis are divided into network-oriented and application-oriented (mainly refer to cryptocurrency). The network-oriented stochastic models are further classified into two categories, namely, performance and security. About the application-oriented stochastic models, the widest adoption mainly concentrates on the price prediction of cryptocurrency. Moreover, we provide analysis and comparison in detail on every taxonomy and discuss the strengths and weaknesses of the related works to serve guides for further researches. Finally, challenges and future research directions are given to apply stochastic modeling approaches to study blockchain. By analyzing and classifying the existing researches, we hope that our survey can provide suggestions for the researchers who are interested in blockchain and good at using stochastic models as a tool to address problems.
Graviton: interchain swaps and wrapped tokens liquidity incentivisation solution
Authors: Aleksei Pupyshev, Ilya Sapranidi, Elshan Dzhafarov, Shamil Khalilov, Ilya Teterin
Abstract: This paper discusses the issues with liquidity that inhibit adoption of so-called wrapped tokens, i.e. digital assets issued in one blockchain ecosystem (origin) with representation in other blockchain networks (destination), and an incentive model and a governance mechanism for solving these issues are suggested. The proposed liquidity model called Graviton can be implemented both within the framework of a single destination chain, or as a blockchain-agnostic solution combining various blockchain platforms together and providing liquidity to wrapped tokens in each of them. This model does not depend on how cross-chain transfer gateways are implemented, and can work with both centralized gates and bridges, or decentralized trustless gateways, as well as gateways based on oracle networks and threshold signatures.
SilkViser:A Visual Explorer of Blockchain-based Cryptocurrency Transaction Data
Authors: Zengsheng Zhong, Shuirun Wei, Yeting Xu, Ying Zhao, Fangfang Zhou, Feng Luo, Ronghua Shi
Abstract: Many blockchain-based cryptocurrencies provide users with online blockchain explorers for viewing online transaction data. However, traditional blockchain explorers mostly present transaction information in textual and tabular forms. Such forms make understanding cryptocurrency transaction mechanisms difficult for novice users (NUsers). They are also insufficiently informative for experienced users (EUsers) to recognize advanced transaction information. This study introduces a new online cryptocurrency transaction data viewing tool called SilkViser. Guided by detailed scenario and requirement analyses, we create a series of appreciating visualization designs, such as paper ledger-inspired block and blockchain visualizations and ancient copper coin-inspired transaction visualizations, to help users understand cryptocurrency transaction mechanisms and recognize advanced transaction information. We also provide a set of lightweight interactions to facilitate easy and free data exploration. Moreover, a controlled user study is conducted to quantitatively evaluate the usability and effectiveness of SilkViser. Results indicate that SilkViser can satisfy the requirements of NUsers and EUsers. Our visualization designs can compensate for the inexperience of NUsers in data viewing and attract potential users to participate in cryptocurrency transactions.
A Survey on Blockchain for Big Data: Approaches, Opportunities, and Future Directions
Authors: Natarajan Deepa, Quoc-Viet Pham, C. Dinh Nguyen, Sweta Bhattacharya, Prabadevi B, Reddy Thippa Gadekallu, Reddy Kumar Praveen Maddikunta, Fang Fang, N. Pubudu Pathirana
Abstract: Big data has generated strong interest in various scientific and engineering domains over the last few years. Despite many advantages and applications, there are many challenges in big data to be tackled for better quality of service, e.g., big data analytics, big data management, and big data privacy and security. Blockchain with its decentralization and security nature has the great potential to improve big data services and applications. In this article, we provide a comprehensive survey on blockchain for big data, focusing on up-to-date approaches, opportunities, and future directions. First, we present a brief overview of blockchain and big data as well as the motivation behind their integration. Next, we survey various blockchain services for big data, including blockchain for secure big data acquisition, data storage, data analytics, and data privacy preservation. Then, we review the state-of-the-art studies on the use of blockchain for big data applications in different vertical domains such as smart city, smart healthcare, smart transportation, and smart grid. For a better understanding, some representative blockchain-big data projects are also presented and analyzed. Finally, challenges and future directions are discussed to further drive research in this promising area.
SuSy: a blockchain-agnostic cross-chain asset transfer gateway protocol based on Gravity
Authors: Aleksei Pupyshev, Elshan Dzhafarov, Ilya Sapranidi, Inal Kardanov, Shamil Khalilov, Sten Laureyssens
Abstract: This document is a specialized technical description of one of the potential implementations of a second layer protocol over Gravity, a blockchain-/token-agnostic decentralized oracle protocol. The SuSy protocol prescribes an implementation of cross-chain transfers of digital assets (tokens) in blockchain networks that support smart contracts, focused primarily on popular blockchains with varying architectures, consensuses and cryptography. SuSy is centered exclusively around technical implementation of transfers, without bringing any incentive models for cross-chain transfer providers. In addition, we describe the most popular inter-chain communication solutions such as Polkadot, Cosmos Hub, Rainbow and RenVM, as a backdrop for the new solution proposed in this paper.
Immutable Log Storage as a Service on Private and Public Blockchains
Authors: William Pourmajidi, Lei Zhang, John Steinbacher, Tony Erwin, Andriy Miranskyy
Abstract: During the normal operation of a Cloud solution, no one pays attention to the logs except the system reliability engineers, who may periodically check them to ensure that the Cloud platform’s performance conforms to the Service Level Agreements (SLA). However, the moment a component fails, or a customer complains about a breach of SLA, the importance of logs increases significantly. All departments, including management, customer support, and even the actual customer, may turn to logs to determine the cause and timeline of the issue and to find the party responsible for the issue. The party at fault may be motivated to tamper with the logs to hide their role. Given the number and volume of logs generated by the Cloud platforms, many tampering opportunities exist. We argue that the critical nature of logs calls for immutability and verification mechanism without the presence of a single trusted party. This paper proposes such a mechanism by describing a blockchain-based log system, called Logchain, which can be integrated with existing private and public blockchain solutions. Logchain uses the immutability feature of blockchain to provide a tamper-resistance storage platform for log storage. Additionally, we propose a hierarchical structure to combine the hash-binding of two blockchains to address blockchains’ scalability issues. To validate the mechanism, we integrate Logchain into two different types of blockchains. We choose Ethereum as a public, permission-less blockchain and IBM Blockchain as a private, permission-based one. We show that the solution is scalable on both the private and public blockchains. Additionally, we perform the analysis of the cost of ownership for private and public blockchains implementations to help a reader selecting an implementation that would be applicable to their needs.
A Blockchain-based Platform Architecture for Multimedia Data Management
Authors: Yue Liu, Qinghua Lu, Chunsheng Zhu, Qiuyu Yu
Abstract: Massive amounts of multimedia data (i.e., text, audio, video, graphics and animation) are being generated everyday. Conventionally, multimedia data are managed by the platforms maintained by multimedia service providers, which are generally designed using centralised architecture. However, such centralised architecture may lead to a single point of failure and disputes over royalties or other rights. It is hard to ensure the data integrity and track fulfilment of obligations listed on the copyright agreement. To tackle these issues, in this paper, we present a blockchain-based platform architecture for multimedia data management. We adopt self-sovereign identity for identity management and design a multi-level capability-based mechanism for access control. We implement a proof-of-concept prototype using the proposed approach and evaluate it using a use case. The results show that the proposed approach is feasible and has scalable performance.
Embedded Blockchains: A Synthesis of Blockchains, Spread Spectrum Watermarking, Perceptual Hashing & Digital Signatures
Authors: Sam Blake
Abstract: In this paper we introduce a scheme for detecting manipulated audio and video. The scheme is a synthesis of blockchains, encrypted spread spectrum watermarks, perceptual hashing and digital signatures, which we call an Embedded Blockchain. Within this scheme, we use the blockchain for its data structure of a cryptographically linked list, cryptographic hashing for absolute comparisons, perceptual hashing for flexible comparisons, digital signatures for proof of ownership, and encrypted spread spectrum watermarking to embed the blockchain into the background noise of the media. So each media recording has its own unique blockchain, with each block holding information describing the media segment. The problem of verifying the integrity of the media is recast to traversing the blockchain, block-by-block, and segment-by-segment of the media. If any chain is broken, the difference in the computed and extracted perceptual hash is used to estimate the level of manipulation.
A survey on Blockchain-based applications for reforming data protection, privacy and security
Authors: The Phan Duy, Thu Thi Do Hien, Van-Hau Pham
Abstract: The modern society, economy and industry have been changed remarkably by many cutting-edge technologies over the last years, and many more are in development and early implementation that will in turn led even wider spread of adoptions and greater alteration. Blockchain technology along with other rising ones is expected to transform virtually every aspect of global business and individuals’ lifestyle in some areas. It has been spreading with multi-sector applications from financial services to healthcare, supply chain, and cybersecurity emerging every passing day. Simultaneously, in the digital world, data protection and privacy are the most enormous issues which customers, companies and policymakers also take seriously into consideration due to the recent increase of security breaches and surveillance in reported incidents. In this case, blockchain has the capability and potential to revolutionize trust, security and privacy of individual data in the online world. Hence, the purpose of this paper is to study the actual cases of Blockchain applied in the reformation of privacy and security field by discussing its impacts as well as the opportunities and challenges.
TRUSTD: Combat Fake Content using Blockchain and Collective Signature Technologies
Authors: Zakwan Jaroucheh, Mohamad Alissa, J William Buchanan
Abstract: The growing trend of sharing news/contents, through social media platforms and the World Wide Web has been seen to impact our perception of the truth, altering our views about politics, economics, relationships, needs and wants. This is because of the growing spread of misinformation and disinformation intentionally or unintentionally by individuals and organizations. This trend has grave political, social, ethical, and privacy implications for society due to 1) the rapid developments in the field of Machine Learning (ML) and Deep Learning (DL) algorithms in creating realistic-looking yet fake digital content (such as text, images, and videos), 2) the ability to customize the content feeds and to create a polarized so-called “filter-bubbles” leveraging the availability of the big-data. Therefore, there is an ethical need to combat the flow of fake content. This paper attempts to resolve some of the aspects of this combat by presenting a high-level overview of TRUSTD, a blockchain and collective signature-based ecosystem to help content creators in getting their content backed by the community, and to help users judge on the credibility and correctness of these contents.
Recent scaling properties of Bitcoin price returns
Authors: Tetsuya Takaishi
Abstract: While relevant stylized facts are observed for Bitcoin markets, we find a distinct property for the scaling behavior of the cumulative return distribution. For various assets, the tail index $μ$ of the cumulative return distribution exhibits $μ\approx 3$, which is referred to as “the inverse cubic law.” On the other hand, that of the Bitcoin return is claimed to be $μ \approx 2$, which is known as “the inverse square law.” We investigate the scaling properties using recent Bitcoin data and find that the tail index changes to $μ\approx 3$, which is consistent with the inverse cubic law. This suggests that some properties of the Bitcoin market could vary over time. We also investigate the autocorrelation of absolute returns and find that it is described by a power-law with two scaling exponents. By analyzing the absolute returns standardized by the realized volatility, we verify that the Bitcoin return time series is consistent with normal random variables with time-varying volatility.
Covid-19 impact on cryptocurrencies: evidence from a wavelet-based Hurst exponent
Authors: Belén M. Arouxet, F. Aurelio Bariviera, E. Verónica Pastor, Victoria Vampa
Abstract: Cryptocurrency history begins in 2008 as a means of payment proposal. However, cryptocurrencies evolved into complex, high yield speculative assets. Contrary to traditional financial instruments, they are not (mostly) traded in organized, law-abiding venues, but on online platforms, where anonymity reigns. This paper examines the long term memory in return and volatility, using high frequency time series of eleven important coins. Our study covers the pre-Covid-19 and the subsequent pandemia period. We use a recently developed method, based on the wavelet transform, which provides more robust estimators of the Hurst exponent. We detect that, during the peak of Covid-19 pandemic (around March 2020), the long memory of returns was only mildly affected. However, volatility suffered a temporary impact in its long range correlation structure. Our results could be of interest for both academics and practitioners.
Investing with Cryptocurrencies — evaluating their potential for portfolio allocation strategies
Authors: Alla Petukhina, Simon Trimborn, Karl Wolfgang Härdle, Hermann Elendner
Abstract: Cryptocurrencies (CCs) have risen rapidly in market capitalization over the last years. Despite striking price volatility, their high average returns have drawn attention to CCs as alternative investment assets for portfolio and risk management. We investigate the utility gains for different types of investors when they consider cryptocurrencies as an addition to their portfolio of traditional assets. We consider risk-averse, return-seeking as well as diversificationpreferring investors who trade along different allocation frequencies, namely daily, weekly or monthly. Out-of-sample performance and diversification benefits are studied for the most popular portfolio-construction rules, including mean-variance optimization, risk-parity, and maximum-diversification strategies, as well as combined strategies. To account for low liquidity in CC markets, we incorporate liquidity constraints via the LIBRO method. Our results show that CCs can improve the risk-return profile of portfolios. In particular, a maximum-diversification strategy (maximizing the Portfolio Diversification Index, PDI) draws appreciably on CCs, and spanning tests clearly indicate that CC returns are non-redundant additions to the investment universe. Though our analysis also shows that illiquidity of CCs potentially reverses the results.
Rise of the Machines? Intraday High-Frequency Trading Patterns of Cryptocurrencies
Authors: A. Alla Petukhina, G. C. Raphael Reule, Karl Wolfgang Härdle
Abstract: This research analyses high-frequency data of the cryptocurrency market in regards to intraday trading patterns related to algorithmic trading and its impact on the European cryptocurrency market. We study trading quantitatives such as returns, traded volumes, volatility periodicity, and provide summary statistics of return correlations to CRIX (CRyptocurrency IndeX), as well as respective overall high-frequency based market statistics with respect to temporal aspects. Our results provide mandatory insight into a market, where the grand scale employment of automated trading algorithms and the extremely rapid execution of trades might seem to be a standard based on media reports. Our findings on intraday momentum of trading patterns lead to a new quantitative view on approaching the predictability of economic value in this new digital market.
Topological Data Analysis for Portfolio Management of Cryptocurrencies
Authors: Rodrigo Rivera-Castro, Polina Pilyugina, Evgeny Burnaev
Abstract: Portfolio management is essential for any investment decision. Yet, traditional methods in the literature are ill-suited for the characteristics and dynamics of cryptocurrencies. This work presents a method to build an investment portfolio consisting of more than 1500 cryptocurrencies covering 6 years of market data. It is centred around Topological Data Analysis (TDA), a recent approach to analyze data sets from the perspective of their topological structure. This publication proposes a system combining persistence landscapes to identify suitable investment opportunities in cryptocurrencies. Using a novel and comprehensive data set of cryptocurrency prices, this research shows that the proposed system enables analysts to outperform a classic method from the literature without requiring any feature engineering or domain knowledge in TDA. This work thus introduces TDA-based portfolio management of cryptocurrencies as a viable tool for the practitioner.
Internet of Things (IoT)
Blockchain-based Federated Learning for Failure Detection in Industrial IoT
Authors: Weishan Zhang, Qinghua Lu, Qiuyu Yu, Zhaotong Li, Yue Liu, Kit Sin Lo, Shiping Chen, Xiwei Xu, Liming Zhu
Abstract: Federated learning is an emerging privacy-preserving machine learning paradigm which has attracted great interests from the community of Industrial Internet of Things (IIoT). Blockchain has been recently leveraged in IIoT federated learning to provide data integrity and incentives to attract sufficient client data and computation resources for training. However, there is a lack of systematic architecture design for blockchain-based federated learning systems to support methodical development and tackle the challenge of data heterogeneity in failure detection of IIoT. Also, the current solutions do not consider the incentive mechanism design and scalability issue of blockchain. Therefore, in this paper, we present a platform architecture of blockchain-based federated learning systems for failure detection in IIoT. To address the data heterogeneity issue in IIoT failure detection, we propose a novel centroid distance weighted federated averaging (CDW\_FedAvg) algorithm taking into account the distance between positive class and negative class of each client dataset. To enable verifiable integrity of client data in a scalable way, each client server periodically creates a Merkle tree in which each leaf node represents a client data record, and stores the tree root on a blockchain. An on-chain incentive mechanism is designed based on the size of client data used in local model training to accurately and timely calculate each client’s contribution. A prototype of the proposed architecture is implemented with our industry partner, and evaluated in terms of feasibility, accuracy and performance. The results show that the approach ensures data integrity and has satisfactory detection accuracy and performance.
Proof of Work (PoW) alternatives
Defending Against Malicious Reorgs in Tezos Proof-of-Stake
Authors: Michael Neuder, J. Daniel Moroz, Rithvik Rao, C. David Parkes
Abstract: Blockchains are intended to be immutable, so an attacker who is able to delete transactions through a chain reorganization (a malicious reorg) can perform a profitable double-spend attack. We study the rate at which an attacker can execute reorgs in the Tezos Proof-of-Stake protocol. As an example, an attacker with 40% of the staking power is able to execute a 20-block malicious reorg at an average rate of once per day, and the attack probability increases super-linearly as the staking power grows beyond 40%. Moreover, an attacker of the Tezos protocol knows in advance when an attack opportunity will arise, and can use this knowledge to arrange transactions to double-spend. We show that in particular cases, the Tezos protocol can be adjusted to protect against deep reorgs. For instance, we demonstrate protocol parameters that reduce the rate of length-20 reorg opportunities for a 40% attacker by two orders of magnitude. We also observe a trade-off between optimizing for robustness to deep reorgs (costly deviations that may be net profitable because they enable double-spends) and robustness to selfish mining (mining deviations that result in typically short reorgs that are profitable even without double-spends). That is, the parameters that optimally protect against one make the other attack easy. Finally, we develop a method that monitors the Tezos blockchain health with respect to malicious reorgs using only publicly available information.
zkay v0.2: Practical Data Privacy for Smart Contracts
Authors: Nick Baumann, Samuel Steffen, Benjamin Bichsel, Petar Tsankov, Martin Vechev
Abstract: Recent work introduces zkay, a system for specifying and enforcing data privacy in smart contracts. While the original prototype implementation of zkay (v0.1) demonstrates the feasibility of the approach, its proof-of-concept implementation suffers from severe limitations such as insecure encryption and lack of important language features. In this report, we present zkay v0.2, which addresses its predecessor’s limitations. The new implementation significantly improves security, usability, modularity, and performance of the system. In particular, zkay v0.2 supports state-of-the-art asymmetric and hybrid encryption, introduces many new language features (such as function calls, private control flow, and extended type support), allows for different zk-SNARKs backends, and reduces both compilation time and on-chain costs.
Characterizing Erasable Accounts in Ethereum
Authors: Xiaoqi Li, Ting Chen, Xiapu Luo, Jiangshan Yu
Abstract: Being the most popular permissionless blockchain that supports smart contracts, Ethereum allows any user to create accounts on it. However, not all accounts matter. For example, the accounts due to attacks can be removed. In this paper, we conduct the first investigation on erasable accounts that can be removed to save system resources and even users’ money (i.e., ETH or gas). In particular, we propose and develop a novel tool named GLASER, which analyzes the State DataBase of Ethereum to discover five kinds of erasable accounts. The experimental results show that GLASER can accurately reveal 508,482 erasable accounts and these accounts lead to users wasting more than 106 million dollars. GLASER can help stop further economic loss caused by these detected accounts. Moreover, GLASER characterizes the attacks/behaviors related to detected erasable accounts through graph analysis.