# Evaluating Methods for the Identification of Off-Chain Transactions in the Lightning Network
## 6 Conclusions  
We have conducted the analysis of Lightning network (LN) from the data stored in the underlying blockchain. We have observed, when attempting to locate on-chain LN transactions, that one can never be certain that a transaction is LN-related if one only uses the information available in the blockchain. The different characteristics used to identify potential on-chain transactions have a different level of uniqueness and will have an impact on the sensitivity of the method. Transaction uniqueness makes it likely that the transaction is LN related, but as stated previously: users are free to create whatever scripts and transaction chains they wish in the blockchain, and this naturally obfuscates the LN-related information. The identified channels also contain a limited amount of information allowing us to infer further LN properties, especially in terms of off-chain transactions. As the on-chain transactions relate to the channels in the LN, most of the information we can gather from the blockchain is related to the channels and users participating in the channels. This information allows us to determine some of the structure of the LN, in terms of which channels are connected. It is hard or impossible to determine anything regarding off-chain transaction flows. We can, therefore, conclude, from the blockchain analysis perspective, that doing transfers within the LN provides much better privacy than doing transfers on-chain in Bitcoin proper. Figure 19: Channel network from the blockchain, mainnet, first interval. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**(short) Channel Id** & **Degree Centrality** & **Closeness Centrality** & **Betweenness Centrality** \\ \hline
565863659278368768 & 239 & 0.0001790831 & 23,084.374 \\
565758106242187265 & 176 & 0.0001743679 & 10,285.468 \\
569069835159470080 & 159 & 0.0001737619 & 11,597.068 \\
566469490153553920 & 153 & 0.0001763668 & 5733.209 \\
567496434090377216 & 152 & 0.0001756543 & 6881.286 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Centrality measures for the highest degree nodes for the network in Figure 17.  
In our data collection interval comparing the channels found with our two methods of identification, and channels collected through the LN, we observed that more than 75% of all 2 of2 multisig transactions are, in fact, LN channels. This was due to the possible limits of our LN node to collect all channels, as other nodes will remove inactive channels, and they will therefore not be propagated. This might become apparent if one were to use longer data collection intervals or run the collection node longer before starting to collect, allowing the node to receive many channels that will eventually be pruned by other nodes. The timelocked identification provides a good middle ground between precision and sensitivity, i.e., how many channels it will be able to find, with the results from the second interval indicating that around 25% of channels in LN are unilaterally closed. As shown in our correlation between P2 WSH outputs and number of channels on the LN, the P2 WSH outputs can be used as an indicator of the number of channels, at least providing an upper limit on active channels at any point in time. It is important to note, however, that these numbers are reliant on the current use cases of the things used for identification and correlation. For example, if 2 of2 multisig is used commonly for other purposes than LN channels, identification based on it will become notably unreliable. Using new keys for each transaction providing the users with pseudo-anonymity continues to be important for providing privacy in channels graphs. In addition to enabling us to link channels containing the same keys, it also enables linking of keys within the channel graph itself. We discussed previously how to distinguish users within a graph would enable us to determine which users provide a link between channels, allowing us to recreate parts of the structure of the LN more accurately.