Peter Sebastian Nordholt

2papers

2 Papers

CRJul 1, 2020
Cross-Layer Deanonymization Methods in the Lightning Protocol

Matteo Romiti, Friedhelm Victor, Pedro Moreno-Sanchez et al.

Bitcoin (BTC) pseudonyms (layer 1) can effectively be deanonymized using heuristic clustering techniques. However, while performing transactions off-chain (layer 2) in the Lightning Network (LN) seems to enhance privacy, a systematic analysis of the anonymity and privacy leakages due to the interaction between the two layers is missing. We present clustering heuristics that group BTC addresses, based on their interaction with the LN, as well as LN nodes, based on shared naming and hosting information. We also present linking heuristics that link 45.97% of all LN nodes to 29.61% BTC addresses interacting with the LN. These links allow us to attribute information (e.g., aliases, IP addresses) to 21.19% of the BTC addresses contributing to their deanonymization. Further, these deanonymization results suggest that the security and privacy of LN payments are weaker than commonly believed, with LN users being at the mercy of as few as five actors that control 36 nodes and over 33% of the total capacity. Overall, this is the first paper to present a method for linking LN nodes with BTC addresses across layers and to discuss privacy and security implications.

CRFeb 14, 2012
A New Approach to Practical Active-Secure Two-Party Computation

Jesper Buus Nielsen, Peter Sebastian Nordholt, Claudio Orlandi et al.

We propose a new approach to practical two-party computation secure against an active adversary. All prior practical protocols were based on Yao's garbled circuits. We use an OT-based approach and get efficiency via OT extension in the random oracle model. To get a practical protocol we introduce a number of novel techniques for relating the outputs and inputs of OTs in a larger construction. We also report on an implementation of this approach, that shows that our protocol is more efficient than any previous one: For big enough circuits, we can evaluate more than 20000 Boolean gates per second. As an example, evaluating one oblivious AES encryption (~34000 gates) takes 64 seconds, but when repeating the task 27 times it only takes less than 3 seconds per instance.