@misc{Borggren2020,
abstract = {Copyright {\textcopyright} 2020, arXiv, All rights reserved. Monero is a popular crypto-currency which focuses on privacy. The blockchain uses cryptographic techniques to obscure transaction values as well as a ‘ring confidential transaction' which seeks to hide a real transaction among a variable number of spoofed transactions. We have developed training sets of simulated blockchains of 10 and 50 agents, for which we have control over the ground truth and keys, in order to test these claims. We featurize Monero transactions by characterizing the local structure of the public-facing blockchains and use labels obtained from the simulations to perform machine learning. Machine Learning of our features on the simulated blockchain shows that the technique can be used to aide in identifying individuals and groups, although it did not successfully reveal the hidden transaction values. We apply the technique on the real Monero blockchain to identify ShapeShift transactions, a cryptocurrency exchange that has leaked information through their API providing labels for themselves and their users.},
author = {Borggren, N. and Kim, H.-Y. and Yao, L. and Koplik, G.},
booktitle = {arXiv},
title = {{Simulated blockchains for machine learning traceability and transaction values in the Monero network}},
year = {2020}
}
@misc{Borggren2020a,
abstract = {Copyright {\textcopyright} 2020, arXiv, All rights reserved. A variety of correlations are detected in the Monero blockchain. The joint distribution of the time- since-last-transaction between elements of pairs of RingCTs is enhanced in comparison with the product of the marginal distributions. Similarly there is an enhancement in the joint distribution of the hour timestamps between the same pairs. Lastly, we find another enhancement when the correlation is measured between the hour timestamps of the transaction itself and the elements of the RingCTs. We calculate some adjustments to the probabilities of which input in a RingCT is real, providing an additional heuristic to denoising the Monero blockchain.},
author = {Borggren, N. and Yao, L.},
booktitle = {arXiv},
title = {{Correlations of multi-input Monero transactions}},
year = {2020}
}

@misc{MiersZcash,
title={{Blockchain Privacy; Equal Parts Theory and Practice}},
url={https://zfnd.org/blockchain-privacy-equal-parts-theory-and-practice/},
year = {2023}
}

@article{hagberg2020networkx,
  title={Networkx: Network analysis with python},
  author={Hagberg, Aric and Conway, Drew},
  journal={URL: https://networkx. github. io},
  year={2020}
}

@misc{jack,
title={An Empirical Analysis of Monero's Ring Signature Resilience to Artificially Intelligent Attacks },
url={https://github.com/MAGICGrants/Monero-Fund/issues/15#issuecomment-1086122008},
year={2022}}

@article{tauzin2021giotto,
  title={giotto-tda: A topological data analysis toolkit for machine learning and data exploration},
  author={Tauzin, Guillaume and Lupo, Umberto and Tunstall, Lewis and P{\'e}rez, Julian Burella and Caorsi, Matteo and Medina-Mardones, Anibal M and Dassatti, Alberto and Hess, Kathryn},
  journal={The Journal of Machine Learning Research},
  volume={22},
  number={1},
  pages={1834--1839},
  year={2021},
  publisher={JMLRORG}
}

@article{flamary2021pot,
  title={Pot: Python optimal transport},
  author={Flamary, R{\'e}mi and Courty, Nicolas and Gramfort, Alexandre and Alaya, Mokhtar Z and Boisbunon, Aur{\'e}lie and Chambon, Stanislas and Chapel, Laetitia and Corenflos, Adrien and Fatras, Kilian and Fournier, Nemo and others},
  journal={The Journal of Machine Learning Research},
  volume={22},
  number={1},
  pages={3571--3578},
  year={2021},
  publisher={JMLRORG}
}

@article{patil2010pymc,
  title={PyMC: Bayesian stochastic modelling in Python},
  author={Patil, Anand and Huard, David and Fonnesbeck, Christopher J},
  journal={Journal of statistical software},
  volume={35},
  number={4},
  pages={1},
  year={2010},
  publisher={Europe PMC Funders}
}

@inproceedings{ranshous2017exchange,
  title={Exchange pattern mining in the bitcoin transaction directed hypergraph},
  author={Ranshous, Stephen and Joslyn, Cliff A and Kreyling, Sean and Nowak, Kathleen and Samatova, Nagiza F and West, Curtis L and Winters, Samuel},
  booktitle={Financial Cryptography and Data Security: FC 2017 International Workshops, WAHC, BITCOIN, VOTING, WTSC, and TA, Sliema, Malta, April 7, 2017, Revised Selected Papers 21},
  pages={248--263},
  year={2017},
  organization={Springer}
}

@article{monaco2015time,
  title={Time intervals as a Behavioral Biometric},
  author={Monaco, John Vincent},
  journal={PhD diss., PhD thesis, Pace University},
  year={2015}
}

@inproceedings{monaco2015identifying,
  title={Identifying bitcoin users by transaction behavior},
  author={Monaco, John V},
  booktitle={Biometric and surveillance technology for human and activity identification XII},
  volume={9457},
  pages={25--39},
  year={2015},
  organization={SPIE}
}

@book{rabadan2019topological,
  title={Topological Data Analysis for Genomics and Evolution: Topology in Biology},
  author={Rabadan, R. and Blumberg, A.J.},
  isbn={9781107159549},
  lccn={2019002342},
  year={2019},
  publisher={Cambridge University Press}
}
@article{carlsson2009topology,
  title={Topology and data},
  author={Carlsson, Gunnar},
  journal={Bulletin of the American Mathematical Society},
  volume={46},
  number={2},
  pages={255--308},
  year={2009}
}
@article{egger2022defeating,
  title={On Defeating Graph Analysis of Anonymous Transactions},
  author={Egger, Christoph and Lai, Russell WF and Ronge, Viktoria and Woo, Ivy KY and Yin, Hoover HF},
  journal={Cryptology ePrint Archive},
  year={2022}
}
@article{vijayakumaran2021analysis,
  title={Analysis of cryptonote transaction graphs using the dulmage-mendelsohn decomposition},
  author={Vijayakumaran, Saravanan},
  journal={Cryptology ePrint Archive},
  year={2021}
}

