Paper 2023/1259 Original: eprint.iacr.org/2023/1259 ↗
Nonlinear computations on FinTracer tags
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.
- History
- 2023-08-21: last updated
- 2023-08-21: published
- Short URL
- https://ia.cr/2023/1259
- License
- https://creativecommons.org/licenses/by/4.0/
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}
}