Non-Intrusive and High-Efficient Balance Tomography in the Lightning Network

The Lightning Network (LN) is a second layer technology for solving the scalability problem of blockchain-based cryptocurrencies such as Bitcoin. The LN nodes (i.e., LN users), linked by payment channels, can make payments to each other directly or through multiple hops of payment channels, subject to the available balances of the serving channels. In current LN implementation, the channel capacity (i.e., the sum of the bidirectional balances in the channel) is open to the public, but the bidirectional balances are kept secret for privacy concerns. Nevertheless, the balances can be directly measured by conducting multiple fake payments to probe the precise value of the balance. Such a method, while effective, creates many fake invoices and incurs high cost when used for discovering balances for multiple users. We present a novel non-intrusive balance tomography (NIBT) method, which infers the channel balances by performing legal transactions between two pre-created LN nodes. NIBT iteratively reduces the balance ranges and uses an efficient balance inference algorithm to find the optimal payment in each iteration to cut off the maximum balance ranges. Experimental results show that NIBT can accurately infer about 92% of all covered balances with an extremely low cost.