Exploratory Study of Privacy Preserving Fraud Detection

Author:

Canillas Rémi1,Talbi Rania2,Bouchenak Sara2,Hasan Omar2,Brunie Lionel2,Sarrat Laurent1

Affiliation:

1. SIS ID, Lyon, France

2. INSA Lyon - LIRIS, Lyon, France

Funder

Agence Nationale de la Recherche

Publisher

ACM

Reference15 articles.

1. A. Williams. 2017 (accessed February 1 2018). Fraudsters Target UK Directors Through Companies House. The Financial Time. (2017 (accessed February 1 2018)). A. Williams. 2017 (accessed February 1 2018). Fraudsters Target UK Directors Through Companies House. The Financial Time. (2017 (accessed February 1 2018)).

2. B. Sullivan. 2017 (accessed February 1 2018). Identity Theft Hit an All-Time High in 2016. USA Today. (2017 (accessed February 1 2018)). B. Sullivan. 2017 (accessed February 1 2018). Identity Theft Hit an All-Time High in 2016. USA Today. (2017 (accessed February 1 2018)).

3. Machine Learning Classification over Encrypted Data

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1. Battle of Wits: To What Extent Can Fraudsters Disguise Their Tracks in International bypass Fraud?;Proceedings of the 19th ACM Asia Conference on Computer and Communications Security;2024-07

2. Locally Differentially Private Embedding Models in Distributed Fraud Prevention Systems;2023 IEEE International Conference on Data Mining Workshops (ICDMW);2023-12-04

3. Secret-Shared Joins with Multiplicity from Aggregation Trees;Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security;2022-11-07

4. Supplier Impersonation Fraud Detection using Bayesian Inference;2020 IEEE International Conference on Big Data and Smart Computing (BigComp);2020-02

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