Payment channel networks (PCNs) have emerged as a promising alternative to mitigate the scalability issues inherent to cryptocurrencies like Bitcoin and are often assumed to improve privacy, as payments are not stored on chain. However, a systematic analysis of possible deanonymization attacks is still missing. In this paper, we focus on the Bitcoin Lightning Network (LN), which is the most widespread implementation of PCNs to date. We present clustering heuristics that group Bitcoin addresses, based on their interaction with the LN, and LN nodes, based on shared naming and hosting information. We also present cross-layer linking heuristics that can, with our dataset, link 43.7% of all LN nodes to 26.3% Bitcoin addresses interacting with the LN. These cross-layer links allow us to attribute information (e.g., aliases, IP addresses) to 17% of the Bitcoin addresses contributing to their deanonymization. Further, we find the security and privacy of the LN are at the mercy of as few as five actors that control 34 nodes and over 44% of the total capacity. Overall, we present the first quantitative analysis of the security and privacy issues opened up by cross-layer interactions, demonstrating their impact and proposing suitable mitigation strategies.

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The system consisting of the reciprocating compressor and associated bottles, known as the “Cylinder manifold” may potentially be the source and location of high vibration problems. Consequently special attention must be paid to the complete simulation of the system to assure smooth and safe operation. Applicable standards specify the items to be included in the study (crosshead guides, distance pieces, cylinder flanges, joints, supports, etc.). However only a model built using manufacturing drawings and validated by site measurements can provide a sufficient accurate description of the characteristics of these critical components and therefore realistic results. Knowledge of the frequencies and amplitudes of pulsation induced shaking forces defined by acoustical simulation, internal gas forces in the cylinder, and unbalanced mechanical forces and moments allows a proper forced response analysis of the cylinder manifold system to be performed. These forces are applied to the finite element model to calculate the relevant vibrations and stress amplitudes by performing a harmonic analysis. When the dynamic stresses are out of the limits it is necessary to go back to the cylinder manifold system analysis or to the acoustical study to find a solution using different supports, with lower shaking forces, or by modifying the volume bottle design. This enables an iterative analysis of the system until all requirements have been satisfied. Additional results of a forced response analysis are the reaction forces on the cylinder and discharge volume bottle supports. When the application requires a large and heavy acoustic damping system with consequently a low mechanical natural frequency, or the compressor speed is significantly high, the possibility of mechanical resonance in the first design is very high. Therefore the execution of these studies at a very early stage of the project is fundamental. The proper solution can be found only by close cooperation between the compressor manufacturer, end user, engineering contractor and vibration specialist.

Proposed in 2016 and launched in 2018, the Bitcoin (BTC) Lightning Network (LN) can scale-up the capacity of the BTC blockchain network to process a significantly higher amount of transactions, in a faster, cheaper, and more privacy preserving manner. The number of LN nodes has been significantly increasing since 2018, and today there are more than twelve thousand nodes actively participating of so-called LN payment channels. The upcoming Taproot upgrade to the Bitcoin protocol would further boost the development and adoption of the LN. Taproot is the most significant upgrade to the Bitcoin network since the block size increase of 2017, and it will make LN transactions cheaper, more flexible, and more private. We focus on the characterization of the LN network topology, using network active measurements. By crawling the underlying P2P network supporting the Bitcoin LN over a span of 10-months, we unveil the LN in terms of size and location of its nodes as well as connectivity protocols, comparing it to the P2P IP network supporting the BTC blockchain. Among our findings, we show that IP addresses exposed by LN nodes correspond mainly to customer networks, even if most BTC nodes are actually deployed at major cloud providers, and that LN nodes significantly rely on anonymized networks and protocols such as Onion, with more than 40% of LN nodes connect through Tor.