SoftwareApplication
name: PETsChallenge
description: Code underlying: Privacy-Preserving Membership Queries for Federated Anomaly Detection
description: Privacy-Preserving Feature Extraction for Detection of Anomalous Financial Transactions ------------------------------------------------------------------------ This repository holds the code written by the PPMLHuskies for the 2nd Place solution in the PETs Prize Challenge, Track A. Description The task is to predict probabilities for anomalous transactions, from a synthetic database of international transactions, and several synthetic databases of banking account information. We provide two solutions. One solution, our centralized approach, found in `solution_centralized.py`, uses the transactions database (PNS) and the banking database with no privacy protections. The second solution, which provides robust privacy gurantees outlined in our report, follows a federated architecture, found in `solution_federated.py` and model.py. In this approach, PNS data resides in one client, banking data is divided up accross other clients, and an aggregator handles all the communication between any clients. We have built in privacy protections so that clients and the aggregator learn minimal information about each other, while engaging in communication to detect anomalous transactions in PNS. The way in which we conduct training and inference in both the centralized and the federated architectures is fundamentally the same (other than the privacy protections in the latter). Several new features are engineered from the given PNS data. Then a model is trained on those features from PNS. Next, during inference, a check is made to determine if attributes from a PNS transaction match with the banking data, or if the associated account in the banking data is flagged. If any of these attributes are amiss, we give it a value of 1, and a 0 otherwise. Lastly, we take the maximum of the inferred probabilities from the PNS model, and the result from the Banking data validation, which is used as our final prediction for the probability that the transaction is anomalous. The difference betwee…
code repository: https://data.4tu.nl/v3/datasets/2851fb4d-9c4f-498d-9ade-446399e45e08.git
author: https://orcid.org/0000-0002-3979-9740
author: https://orcid.org/0000-0001-7486-6016
author: https://orcid.org/0009-0008-4442-2762
author: https://orcid.org/0000-0001-7748-8161
author: Person
  given name: Dean
  family name: Kelley
  name: Dean Kelley
author: https://orcid.org/0000-0001-8932-4703
author: https://orcid.org/0000-0001-7917-0771
author: https://orcid.org/0000-0002-8298-6250
keywords: federated learning
keywords: secure computation
keywords: privacy enhancing technologies
keywords: Anomaly detection
keywords: cryptography
keywords: elliptic curves
keywords: private membership queries
programming language: HTML
programming language: Markdown
programming language: Other
programming language: Python
programming language: Rust
programming language: Shell
programming language: YAML
license:
  creative work: creativeWork
    name: Apache-2.0
reference publication: popets-2024-0074

https://orcid.org/0000-0002-3979-9740
type: Person
given name: Jelle
family name: Vos
name: Jelle Vos

https://orcid.org/0000-0001-7486-6016
type: Person
given name: Sikha
family name: Pentyala
name: Sikha Pentyala

https://orcid.org/0009-0008-4442-2762
type: Person
given name: Steven
family name: Golob
name: Steven Golob

https://orcid.org/0000-0001-7748-8161
type: Person
given name: Ricardo José Menezes
family name: Maia
name: Ricardo José Menezes Maia

https://orcid.org/0000-0001-8932-4703
type: Person
given name: Zekeriya
family name: Erkin
name: Zekeriya Erkin

https://orcid.org/0000-0001-7917-0771
type: Person
given name: Martine
family name: De Cock
name: Martine De Cock

https://orcid.org/0000-0002-8298-6250
type: Person
given name: Anderson
family name: Nascimento
name: Anderson Nascimento

popets-2024-0074
type: ScholarlyArticle
name: Privacy-Preserving Membership Queries for Federated Anomaly Detection
pagination: 186-201
url: https://doi.org/10.56553%2Fpopets-2024-0074