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1. Blockchains and Databases: Opportunities and Challenges for the Permissioned and the Permissionless

   https://doi.org/10.1007/978-3-030-54832-2_1  

   Agrawal, D. ; El Abbadi, A. ; Amiri, M.J. ; Maiyya, S. ; Zakhary, V. ( August 2020 , Advances in Databases and Information Systems)

   Darmont, J ; Novikov, B. ; Wrembel, R. (Ed.)

   Bitcoin [12] is a successful and interesting example of a global scale peer-to-peer cryptocurrency that integrates many techniques and protocols from cryptography, distributed systems, and databases. The main underlying data structure is blockchain, a scalable fully replicated structure that is shared among all participants and guarantees a consistent view of all user transactions by all participants in the system. In a blockchain, nodes agree on their shared states across a large network of untrusted participants. Although originally devised for cryptocurrencies, recent systems exploit its many unique features such as transparency, provenance, fault tolerance, and authenticity to support a wide range of distributed applications. Bitcoin and other cryptocurrencies use permissionless blockchains. In a permissionless blockchain, the network is public, and anyone can participate without a specific identity. Many other distributed applications, such as supply chain management and healthcare, are deployed on permissioned blockchains consisting of a set of known, identified nodes that still might not fully trust each other. This paper illustrates some of the main challenges and opportunities from a database perspective in the many novel and interesting application domains of blockchains. These opportunities are illustrated using various examples from recent research in both permissionless and permissioned blockchains. Two main themes unite the various examples: (1) the important role of distribution and consensus in managing large scale systems and (2) the need to tolerate malicious failures. The advent of cloud computing and large data centers shifted large scale data management infrastructures from centralized databases to distributed systems. One of the main challenges in designing distributed systems is the need for fault-tolerance. Cloud-based systems typically assume trusted infrastructures, since data centers are owned by the enterprises managing the data, and hence the design typically only assumes and tolerates crash failures. The advent of blockchain and the underlying premise that copies of the blockchain are distributed among untrusted entities has shifted the focus of fault-tolerance from tolerating crash failures to tolerating malicious failures. These interesting and challenging settings pose great opportunities for database researchers. 

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2. Forecasting Bitcoin Price with Graph Chainlets

   https://doi.org/10.1007/978-3-319-93040-4_60  

   Akcora, C.G. ; Dey, A.K. ; Gel, Y.R. ; Kantarcioglu, M. ( January 2018 , Phung D., Tseng V., Webb G., Ho B., Ganji M., Rashidi L. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2018. Lecture Notes in Computer Science, vol 10939. Springer, Cham)

   Over the last couple of years, Bitcoin cryptocurrency and the Blockchain technology that forms the basis of Bitcoin have witnessed a flood of attention. In contrast to fiat currencies used worldwide, the Bitcoin distributed ledger is publicly available by design. This facilitates observing all financial interactions on the network, and analyzing how the network evolves in time. We introduce a novel concept of chainlets, or Bitcoin subgraphs, which allows us to evaluate the local topological structure of the Bitcoin graph over time. Furthermore, we assess the role of chainlets on Bitcoin price formation and dynamics. We investigate the predictive Granger causality of chainlets and identify certain types of chainlets that exhibit the highest predictive influence on Bitcoin price and investment risk. 

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3. A Survey ofNFC Mobile Payment: Challenges and Solutions using Blockchain and Cryptocurrencies

   https://doi.org/10.1109/DSA51864.2020.00018  

   Li, Dongcheng ; Wong, W. Eric ; Chau, Matthew ; Pan, Sean ; Koh, Liang Seng ( November 2020 , The 7th International Conference on Dependable Systems and Their Applications)

   Near-field communication (NFC) is one of the essential technologies in the Internet of Things (IoT) that has facilitated mobile payment across different services. The technology has become increasingly popular, as cryptocurrencies like Bitcoin have revolutionized how payment systems can be designed. However, this technology is subject to security problems, such as man-in-the-middle attacks, double-spending, and replay attacks, raising the need to incorporate other solutions such as blockchain technology. Concerns about the security and privacy of payments using NFC technology raise the need to adopt blockchain-based cryptocurrency payment. For instance, NFC payment has been criticized for a lack of measures to counter potential attacks, such as brute force or double-spending. Thus, incorporating blockchain technology is expected to improve the security features of the NFC mobile payment protocol and improve user experience. Blockchain technology has been praised for enabling fair payment, as it permits direct transactions without engaging a third party. Therefore, integrating blockchain cryptocurrency in IoT devices will revolutionize the NFC payment method and provide value transfer using IoT devices. Combining NFC with blockchain technology and cryptocurrencies is necessary to address security and privacy problems. The purpose of this paper is to explore the potential behind incorporating blockchain technology and cryptocurrencies like Bitcoin in the NFC payment protocol. 

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4. Sprites and State Channels: Payment Networks that Go Faster than Lightning

   Miller, Andrew ; Bentov, Iddo ; Kumaresan, Ranjit ; McCorry, Patrick ( February 2019 , Financial Cryptography and Data Security)

   Bitcoin, Ethereum and other blockchain-based cryptocurrencies, as deployed today, cannot support more than several transactions per second. Off-chain payment channels, a “layer 2” solution, are a leading approach for cryptocurrency scaling. They enable two mutually distrustful parties to rapidly send payments between each other and can be linked together to form a payment network, such that payments between any two parties can be routed through the network along a path that connects them. We propose a novel payment channel protocol, called Sprites. The main advantage of Sprites compared with earlier protocols is a reduced “collateral cost,” meaning the amount of money × time that must be locked up before disputes are settled. In the Lightning Network and Raiden, a payment across a path of ` channels requires locking up collateral for Θ(`∆) time, where ∆ is the time to commit an on-chain transaction; every additional node on the path forces an increase in lock time. The Sprites construction provides a constant lock time, reducing the overall collateral cost to Θ(` + ∆). Our presentation of the Sprites protocol is also modular, making use of a generic state channel abstraction. Finally, Sprites improves on prior payment channel constructions by supporting partial withdrawals and deposits without any on-chain transactions. 

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5. BitcoinHeist: Topological Data Analysis for Ransomware Prediction on the Bitcoin Blockchain

   https://doi.org/10.24963/ijcai.2020/612  

   Akcora, Cuneyt G. ; Li, Yitao ; Gel, Yulia R. ; Kantarcioglu, Murat ( July 2020 , Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence)

   Recent proliferation of cryptocurrencies that allow for pseudo-anonymous transactions has resulted in a spike of various e-crime activities and, particularly, cryptocurrency payments in hacking attacks demanding ransom by encrypting sensitive user data. Currently, most hackers use Bitcoin for payments, and existing ransomware detection tools depend only on a couple of heuristics and/or tedious data gathering steps. By capitalizing on the recent advances in Topological Data Analysis, we propose a novel efficient and tractable framework to automatically predict new ransomware transactions in a ransomware family, given only limited records of past transactions. Moreover, our new methodology exhibits high utility to detect emergence of new ransomware families, that is, detecting ransomware with no past records of transactions.

    

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