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1. On the role of local blockchain network features in cryptocurrency price formation

   https://doi.org/10.1002/cjs.11547  

   Dey, Asim_K; Akcora, Cuneyt_G; Gel, Yulia_R; Kantarcioglu, Murat (March 2020, Canadian Journal of Statistics)

   <italic>Abstract</italic> Cryptocurrencies and the underpinning blockchain technology have gained unprecedented public attention recently. In contrast to fiat currencies, transactions of cryptocurrencies, such as Bitcoin and Litecoin, are permanently recorded on distributed ledgers to be seen by the public. As a result, public availability of all cryptocurrency transactions allows us to create a complex network of financial interactions that can be used to study not only the blockchain graph, but also the relationship between various blockchain network features and cryptocurrency risk investment. We introduce a novel concept of chainlets, or blockchain motifs, to utilize this information. Chainlets allow us to evaluate the role of local topological structure of the blockchain on the joint Bitcoin and Litecoin 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 cryptocurrency price and investment risk. More generally, while statistical aspects of blockchain data analytics remain virtually unexplored, the paper aims to highlight various emerging theoretical, methodological and applied research challenges of blockchain data analysis that will be of interest to the broad statistical community.The Canadian Journal of Statistics48: 561–581; 2020 © 2020 Statistical Society of Canada 

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2. Why topological data analysis detects financial bubbles?

   https://doi.org/10.1016/j.cnsns.2023.107665  

   Akingbade, Samuel W; Gidea, Marian; Manzi, Matteo; Nateghi, Vahid (January 2024, Communications in Nonlinear Science and Numerical Simulation)

   We present a heuristic argument for the propensity of Topological Data Analysis (TDA) to detect early warning signals of critical transitions in financial time series. Our argument is based on the Log-Periodic Power Law Singularity (LPPLS) model, which characterizes financial bubbles as super-exponential growth (or decay) of an asset price superimposed with oscillations increasing in frequency and decreasing in amplitude when approaching a critical transition (tipping point). We show that whenever the LPPLS model is fitting with the data, TDA generates early warning signals. As an application, we illustrate this approach on a sample of positive and negative bubbles in the Bitcoin historical price. 

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3. Bijack: Breaking Bitcoin Network with TCP Vulnerabilities

   https://doi.org/10.1007/978-3-031-51479-1_16  

   Li, Shaoyu; Shi, Shanghao; Xiao, Yang; Zhang, Chaoyu; Hou, Y. Thomas; Lou, Wenjing (January 2024, Computer Security -- ESORICS 2023)

   Recent studies have shown that compromising Bitcoin’s peer-to-peer network is an effective way to disrupt the Bitcoin service. While many attack vectors have been uncovered such as BGP hijacking in the network layer and eclipse attack in the application layer, one significant attack vector that resides in the transport layer is largely overlooked. In this paper, we investigate the TCP vulnerabilities of the Bitcoin system and their consequences. We present Bijack, an off-path TCP hijacking attack on the Bitcoin network that is able to terminate Bitcoin connections or inject malicious data into the connections with only a few prior requirements and a limited amount of knowledge. This results in the Bitcoin network topology leakage, and the Bitcoin nodes isolation. 

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4. The rise and fall of cryptocurrencies

   Feder, Amir; Gandal, Neil; Hamrick, JT; Moore, Tyler; Vasek, Marie (June 2018, Workshop on the Economics of Information Security)

   Since Bitcoin’s introduction in 2009, interest in cryptocurrencies has soared. One manifestation of this interest has been the explosion of newly created coins. This paper examines the dynamics of coin creation, competition and destruction in the cryptocurrency industry. In order to conduct the analysis, we develop a methodology to identify peaks in prices and trade volume, as well as when coins are abandoned and subsequently “resurrected”. We study trading activity associated with 1 082 coins over a nearly five-year period. We present evidence that the more frequently traded coins experience the biggest price rises. They are also much less likely to be abandoned, that is, to experience a drop in average trading volume to below 1% of a prior peak value. Overall, we find that 44% of publicly-traded coins are abandoned, at least temporarily. 71% of abandoned coins are later resurrected, leaving 18% of coins to fail permanently. We then examine the association between entry and exit and other key variables such as price, volume, and market capitalization in order to analyze and provide intuition underpinning the fundamentals in this market. We conclude by examining the bursting of the Bitcoin bubble in December 2017. Unlike the end of the 2013 bubble, some alternative cryptocurrencies continue to flourish after the fall of Bitcoin. 

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5. A Tale of Two Trees: One Writes, and Other Reads: Optimized Oblivious Accesses to Bitcoin and other UTXO-based Blockchains

   https://doi.org/10.2478/popets-2020-0039  

   Le, Duc V.; Hurtado, Lizzy Tengana; Ahmad, Adil; Minaei, Mohsen; Lee, Byoungyoung; Kate, Aniket (April 2020, Proceedings on Privacy Enhancing Technologies)

   null (Ed.)

   Abstract The Bitcoin network has offered a new way of securely performing financial transactions over the insecure network. Nevertheless, this ability comes with the cost of storing a large (distributed) ledger, which has become unsuitable for personal devices of any kind. Although the simplified payment verification (SPV) clients can address this storage issue, a Bitcoin SPV client has to rely on other Bitcoin nodes to obtain its transaction history and the current approaches offer no privacy guarantees to the SPV clients. This work presents T 3 , a trusted hardware-secured Bitcoin full client that supports efficient oblivious search/update for Bitcoin SPV clients without sacrificing the privacy of the clients. In this design, we leverage the trusted execution and attestation capabilities of a trusted execution environment (TEE) and the ability to hide access patterns of oblivious random access machine (ORAM) to protect SPV clients’ requests from potentially malicious nodes. The key novelty of T 3 lies in the optimizations introduced to conventional ORAM, tailored for expected SPV client usages. In particular, by making a natural assumption about the access patterns of SPV clients, we are able to propose a two-tree ORAM construction that overcomes the concurrency limitation associated with traditional ORAMs. We have implemented and tested our system using the current Bitcoin Unspent Transaction Output (UTXO) Set. Our experiment shows that T 3 is feasible to be deployed in practice while providing strong privacy and security guarantees to Bitcoin SPV clients. 

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