Paper 2023/1259

Nonlinear computations on FinTracer tags

Michael Brand, RMIT University, AUSTRAC
Tania Churchill, Australian National University, AUSTRAC
Carsten Friedrich, AUSTRAC
Abstract

Recently, the FinTracer algorithm was introduced as a versatile framework for detecting economic crime typologies in a privacy-preserving fashion. Under the hood, FinTracer stores its data in a structure known as the ``FinTracer tag’’. One limitation of FinTracer tags, however, is that because their underlying cryptographic implementation relies on additive semi-homomorphic encryption, all the system's oblivious computations on tag data are linear in their input ciphertexts. This allows a FinTracer user to combine information from multiple tags in some ways, but not generically. In this paper, we describe an efficient method to perform general nonlinear computations on FinTracer tags, and show how this ability can be used to detect a wide range of complex crime typologies, as well as to extract many new types of information, while retaining all of FinTracer's original privacy guarantees.

Metadata
Available format(s)
PDF
Category
Applications
Publication info
Preprint.
Keywords
anti money launderingfinancial crimegraph analyticshomomorphic encryptionprivate graph analysis
Contact author(s)
michael brand @ rmit edu au
tania churchill @ anu edu au
carsten friedrich @ austrac gov au
History
2023-08-21: approved
2023-08-21: received
See all versions
Short URL
https://ia.cr/2023/1259
License
Creative Commons Attribution
CC BY

BibTeX

@misc{cryptoeprint:2023/1259,
      author = {Michael Brand and Tania Churchill and Carsten Friedrich},
      title = {Nonlinear computations on {FinTracer} tags},
      howpublished = {Cryptology {ePrint} Archive, Paper 2023/1259},
      year = {2023},
      url = {https://eprint.iacr.org/2023/1259}
}
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