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Z. Erkin

81 records found

Cryptography is used everywhere in our society, from simple internet searches to providing security and privacy for our banking systems. A key research area in this field is secure function evaluation, where multiple parties compute a function of their inputs, without revealing t ...

A new way of cooperative cycle detection against financial crime

Decentralised cycle detection using cross-institutional transactions

The act of masking the origin of illegal funds, to inject them into the economy in seemingly legal manners is called money laundering. Adversaries make use of money laundering to stay undetected when using illegally obtained money, from stealing, fraud, or other criminal activiti ...
This research looks at two open-source tools for differential privacy: Google's Differential Privacy Library and the OpenDP Library. The main aim of this study is to test them side-by-side and observe how they compared quantitatively. Specifically, the focus is on their impl ...
Differential Privacy (DP) has become one of the most used approaches to protect individual data. However, its implementation can vary significantly depending on the context we are using it. In this study, we aim to compare two such implementations of DP: Google's Differential Pri ...

Quantum SMPC: Rich in theory, limited in practice

A systematic review of quantum secure multi-party computation

Secure Multi-Party Computation (SMPC) is a widely-used cryptographic tool for privacy-preserving data analysis. The progress in the field of quantum computing has led to the development of Quantum SMPC (QSMPC), which promises informationtheoretic security based on physics laws. T ...
Secure multi-party computation (SMPC) is a cryptographic technique that enables multiple parties to work together on data without sharing their private information with each other. This paper investigates how two open-source frameworks, SecretFlow and FATE, implement SMPC and oth ...
Homomorphic Encryption (HE) enables computation directly on encrypted data, while offering strong cryptographic and privacy guarantees for data-driven sectors like healthcare, finance, and cloud computing. However, practical adoption of HE is severely limited by its computational ...
As financial institutions adopt more sophisticated Anti-Money Laundering (AML) techniques, such as the deployment of Graph Neural Networks (GNNs) to detect patterns, laundering behavior is likely to evolve. In this paper, we present a novel perturbation framework that models laun ...
There is an increasing need for financial institutions to be able to detect illicit activities such as money laundering. While these institutions currently rely on graph-based analytics or machine learning algorithms for such detection, inter-bank collaboration is hindered by pri ...
Financial institutions have a large responsibility when it comes to detecting and preventing financial crime. However, dedicated tools to aid in financial crime detection have more demand than supply. The combination of regulatory restrictions with regards to sharing client infor ...
Financial crime represents a growing issue which contemporary society is facing, especially in the form of money laundering, which aims to conceal the origin of illicit funds through a network of intermediate transactions. State of the art solutions for detection of money launder ...
To compute something securely is to do so in a way that does not reveal (some of) the inputs, intermediate values, or outputs, to certain predetermined parties. For example, a hospital might outsource the computation of patients’ medical analyses to the cloud without the cloud pr ...
In a world of increasing threats from monopolies and oligarchies, people are increasingly looking for ways to protect their privacy. Isolating oneself from the world may be tempting, but there is a collective benefit to the processing of sensitive data. For example, hospitals use ...

A Comparative Study of Threshold Multiparty Private Set Intersection Protocols

For Cyber Threat Intelligence Sharing in a Medical Setting

Within the field of \emph{cyber threat intelligence} (CTI), healthcare institutions are one of the most targeted organizations by cybercriminals. To mitigate future attacks on their digital infrastructures, healthcare institutions can collaborate and exchange security logs. These ...

Privacy Preserving Train Scheduling

Using homomorphic encryption to create train schedules

A substantial number of passengers in Europe rely on trains for transportation, facilitated by a network of high-speed international trains. However, the coordination of train schedules across multiple networks often poses challenges due to incompatible timings. The scheduling of ...
Ensuring the privacy of medical data in a meaningful manner is a complex task. This domain presents a plethora of unique challenges: high stakes, vast differences between possible use cases, long-established methods that limit the number of feasible solutions, and more. Consequen ...
Financial crime has seen a surge in complexity over the past decades as a result of digitisation. This required better tools for detecting financial crime, creating a cat-and-mouse game between law enforcement and criminals. Anti-money laundering (AML) is the collective term desc ...

3D mesh object watermarking

Improving robustness of feature vertex localisation by centre-of-volume

Digital modelling is becoming more prevalent in many applications. The underlying 3D mesh objects are therefore getting increasingly valuable, such that methods for ownership protection are required. Watermarking is a solution to this problem, yet the research combining watermark ...
In the digital era, XML data is fundamental for various applications, requiring robust methods to ensure data integrity and security. Traditional digital watermarking techniques face challenges due to XML's hierarchical structure. Zero-watermarking, which derives a watermark from ...

Watermarking time-series data using DWT

Adapting an existing audio technique to watermark non-medical time series

Data security has become more important over the last few years as data sharing over the world has become trivial. Data ownership therefore becomes critical as data can be very valuable and vulnerable to theft. Watermarking is a technique that can help data owners prove ownership ...