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A key component of security in decentralized blockchains is proof of opportunity cost among block producers. In the case of proof-of-work (PoW), currently used by the most prominent systems, the cost is due to spent computation. In this paper, we characterize the security investment of miners in terms of its cost in fiat money. This enables comparison of security allocations across PoW blockchains that generally use different PoW algorithms and reward miners in different cryptocurrency units. We prove that there exists a unique allocation equilibrium, depending on market prices only, that is achieved by both strategic miners (who contemplate the actions of others) and by miners seeking only short-term profit. In fact, the latter will unknowingly compensate for any attempt to deliberately shift security allocation away from equilibrium. Our conclusions are supported analytically through the development of a Markov decision process, game theoretical analysis, and derivation of no arbitrage conditions. We corroborate those results with empirical evidence from more than two years of blockchain and price data. Overall agreement is strong. We show that between January 1, 2018 and August 1, 2020, market prices predicted security allocation between Bitcoin and Bitcoin Cash with error less than 0.6%. And from the beginning of October 2019, until August 1, 2020, market prices predicted security allocation between Bitcoin and Litecoin with error of 0.45%. These results are further corroborated by our establishment of Granger-causality between change in market prices and change in security allocation. To demonstrate the practicality of our results, we describe a trustless oracle that leverages the equilibrium to estimate the price ratios of PoW cryptocurrencies from on-chain information only.

Wireless service providers track the time and location of all user connections. Location inference attacks have been effective in revealing the identity of anonymous users of wireless services. In this paper, we propose ZipPhone, a solution that leverages existing cellular infrastructure to improve user privacy. Spartacus allows a community of users to strategically time their connections to remain anonymous while incurring a minimal loss of utility. We evaluate ZipPhone from the perspective of a cell service provider and a community of privacy-seeking users, and quantify the privacy/utility trade-off of ZipPhone using two datasets containing cell tower logs of hundreds of users. We present and assess a deanonymization algorithm that uses both location profiling and trajectory linking. We find that by renewing identifiers every ten minutes and remaining offline for 30 seconds, users can reduce their identifiability by up to 45%.

All public blockchains are secured by a proof of opportunity cost among block producers. For example, the security offered by proof-of-work (PoW) systems, like Bitcoin, is due to spent computation; it is work precisely because it cannot be performed for free. In general, more resources provably lost in producing blocks yields more security for the blockchain. When two blockchains share the same mechanism for providing opportunity cost, as is the case when they share the same PoW algorithm, the two chains compete for resources from block producers. Indeed, if there exists a liquid market between resource types, then theoretically all blockchains will compete for resources. In this paper, we show that there exists a resource allocation equilibrium between any two blockchains, which is essentially driven by the fiat value of reward that each chain offers in return for providing security. We go on to prove that this equilibrium is singular and always achieved provided that block producers behave in a greedy, but cautious fashion. The opposite is true when they are overly greedy: resource allocation oscillates in extremes between the two chains. We show that these results hold both in practice and in a block generation simulation. Finally, we demonstrate several applications of this theory including a trustless price-ratio oracle, increased security for blockchains whose coins have lower fiat value, and a quantification of cost to allocating resources away from the equilibrium.

Proof-of-work blockchains must implement a difficulty adjustment algorithm (DAA) in order to maintain a consistent inter-arrival time between blocks. Conventional DAAs are essentially feedback controllers, and as such, they are inherently reactive. This approach leaves them susceptible to manipulation and often causes them to either under- or over-correct. We present Bonded Mining, a proactive DAA that works by collecting hash rate commitments secured by bond from miners. The difficulty is set directly from the commitments and the bond is used to penalize miners who deviate from their commitment. We devise a statistical test that is capable of detecting hash rate deviations by utilizing only on-blockchain data. The test is sensitive enough to detect a variety of deviations from commitments, while almost never misclassifying honest miners. We demonstrate in simulation that, under reasonable assumptions, Bonded Mining is more effective at maintaining a target block time than the Bitcoin Cash DAA, one of the newest and most dynamic DAAs currently deployed. In this preliminary work, the lowest hash rate miner our approach supports is 1% of the total and we directly consider only two types of fundamental attacks. Future work will address these limitations.

Abrupt changes in the miner hash rate applied to a proof-of-work (PoW) blockchain can adversely affect user experience and security. Because different PoW blockchains often share hashing algorithms, miners face a complex choice in deciding how to allocate their hash power among chains. We present an economic model that leverages Modern Portfolio Theory to predict a miner's allocation over time using price data and inferred risk tolerance. The model matches actual allocations with mean absolute error within 20% for four out of the top five miners active on both Bitcoin (BTC) and Bitcoin Cash (BCH) blockchains. A model of aggregate allocation across those four miners shows excellent agreement in magnitude with the actual aggregate as well a correlation coefficient of 0.649. The accuracy of the aggregate allocation model is also sufficient to explain major historical changes in inter-block time (IBT) for BCH. Because estimates of miner risk are not time-dependent and our model is otherwise price-driven, we are able to use it to anticipate the effect of a major price shock on hash allocation and IBT in the BCH blockchain. Using a Monte Carlo simulation, we show that, despite mitigation by the new difficulty adjustment algorithm, a price drop of 50% could increase the IBT by 50% for at least a day, with a peak delay of 100%.

Blockchain systems are designed to produce blocks at a constant average rate. The most popular systems currently employ a Proof of Work (PoW) algorithm as a means of creating these blocks. Bitcoin produces, on average, one block every 10 minutes. An unfortunate limitation of all deployed PoW blockchain systems is that the time between blocks has high variance. For example, 5% of the time, Bitcoin's inter-block time is at least 40 minutes. This variance impedes the consistent flow of validated transactions through the system. We propose an alternative process for PoW-based block discovery that results in an inter-block time with significantly lower variance. Our algorithm, called Bobtail, generalizes the current algorithm by comparing the mean of the k lowest order statistics to a target. We show that the variance of inter-block times decreases as k increases. If our approach were applied to Bitcoin, about 80% of blocks would be found within 7 to 12 minutes, and nearly every block would be found within 5 to 18 minutes; the average inter-block time would remain at 10 minutes. Further, we show that low-variance mining significantly thwarts doublespend and selfish mining attacks. For Bitcoin and Ethereum currently (k=1), an attacker with 40% of the mining power will succeed with 30% probability when the merchant sets up an embargo of 8 blocks; however, when k>=20, the probability of success falls to less than 1%. Similarly, for Bitcoin and Ethereum currently, a selfish miner with 40% of the mining power will claim about 66% of blocks; however, when k>=5, the same miner will find that selfish mining is less successful than honest mining. The cost of our approach is a larger block header.

We make several contributions that quantify the real-time hash rate and therefore the consensus of a blockchain. We show that by using only the hash value of blocks, we can estimate and measure the hash rate of all miners or individual miners, with quanti able accuracy. We apply our techniques to the Ethereum and Bitcoin blockchains; our solution applies to any proof-of-work-based blockchain that relies on a numeric target for the validation of blocks. We also show that if miners regularly broadcast status reports of their partial proof-of- work, the hash rate estimates are signi cantly more accurate at a cost of slightly higher bandwidth. Whether using only the blockchain, or the additional information in status reports, merchants can use our techniques to quantify in real-time the threat of double-spend attacks.

The fundamental attack against blockchain systems is the double-spend attack. In this tutorial, we provide a very detailed explanation of just one section of Satoshi Nakamoto's original paper where the attack's probability of success is stated. We show the derivation of the mathematics relied upon by Nakamoto to create a model of the attack. We also validate the model with a Monte Carlo simulation, and we determine which model component is not perfect.

Ethereum contracts can be designed to function as fully decentralized applications called DAPPs. Many DAPPs have already been fielded, including an online marketplace, a role playing game, a prediction market, and an Internet service provider. Unfortunately, DAPPs can be hacked, and the assets they control can be stolen. A recent attack on an Ethereum decentralized application called The DAO demonstrated that smart contract bugs are more than an academic concern. Ether worth tens of millions of US dollars was extracted by an attacker from The DAO, sending the value of its tokens and the overall exchange price of ether tumbling. We present a market-based technique for insuring the ether holdings of a DAPP using futures contracts indexed by the trade price of ether for DAPP tokens. Under fairly general circumstances, our technique is capable of recovering the majority of ether lost from theft with high probability even when all of the ether holdings are stolen; and the only cost to DAPP token holders is an adjustable ether withdrawal fee. If the probability of a margin call in $d$ days is $p$ for a futures contract with 20 times leverage, then our approach will allow for the recovery of half the stolen ether with probability $p$ and a withdrawal fee of 5%. A higher withdrawal fee of 25% allows for more than 80% of the ether to be recovered with probability $p$.

We present and validate a novel mathematical model of the blockchain mining process and use it to conduct an economic evaluation of the double-spend attack, which is fundamental to all blockchain systems. Our analysis focuses on the value of transactions that can be secured under a conventional double-spend attack, both with and without a concurrent eclipse attack. Our model quantifies the importance of several factors that determine the attack's success, including confirmation depth, attacker mining power, and any confirmation deadline set by the merchant. In general, the security of a transaction against a double-spend attack increases roughly logarithmically with the depth of the block, made easier by the increasing sum of coin turned-over (between individuals) in the blocks, but more difficult by the increasing proof of work required. In recent blockchain data, we observed a median block turnover value of 6 BTC. Based on this value, a merchant requiring a single confirmation is protected against only attackers that can increase the current mining power by 1% or less. However, similar analysis shows that a merchant that requires a much longer 72 confirmations (~12 hours) will eliminate all potential profit for any double-spend attacker adding mining power less than 40% of the current mining power.

This paper examines two parallel case studies to promote remote medical consultation in Ghana. These projects, initiated independently by different researchers in different organizations, both deployed ICT solutions in the same medical community in the same year. The Ghana Consultation Network currently has over 125 users running a Web-based application over a delay-tolerant network of servers. OneTouch MedicareLine is currently providing 1700 doctors in Ghana with free mobile phone calls and text messages to other members of the medical community. We present the consequences of (1) the institutional context and identity of the investigators, as well as specific decisions made with respect to (2) partnerships formed, (3) perceptions of technological infrastructure, and (4) high-level design decisions. In concluding, we discuss lessons learned and high-level implications for future ICTD research agendas., Comment: 11 pages