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 describing these tools, as well as financial regulations designed to combat money laundering. The detection of fraud is done through data analysis. Typically, the financial data is a large set of transactions between accounts. Transactions have a sender, receiver, and an amount that is transferred from sender to receiver. This can be represented by a directed graph where nodes represent accounts and edges transactions between accounts. Methods for finding fraudulent transactions in transactional graphs are widely studied in both literature and industry. This usually assumes a centralised entity with full access to the data. At an international scale, the centralised approach is not suitable. Besides its massive scale posing a challenge, it is hard to imagine a global agreement on what capabilities such an entity should have. At the same time, organisations sharing financial data for analysis raises privacy concerns. We limit our scope to transactional cycles. These typically occur when criminals send money through a series of international bank accounts back to the original account. This lowers the chance of the money being traced back to its illicit source. We propose a privacy-preserving decentralised protocol which takes as input a parameter $\ell$ from any node and outputs all cycles of at most length $\ell$ containing the node. The protocol relies on the decisional Diffie-Hellman assumption, and we show the protocol is secure against a polynomially bounded adversary within our security model. The communication complexity of an instance is $O(n^{\ell})$, multiple instances can be ran in parallel. We implement the protocol and measure its performance on scale free graphs in terms of communication and computational costs. Lastly, we discuss our results and suggestions for future work.

Mirror anamorphosis is characterised by a distorted projection, where the combination of a mirror and a specific viewpoint lets the observer see the undistorted image. The use of this technique and complex perspective in general has gone from being niche to becoming mainstream. Raytracing poses itself as a solution to solving the math for such constructions, as it is capable of delivering accurate geometric calculations and a high degree of visual realism. There is a strong connection between the work needed to construct a mirror anamorphosis, and the computations done in raytracing. Raytracing has been a major topic of research with applications in high-quality image rendering. We propose an algorithm which combines raytracing for mirror anamorphosis with texture mapping. We first generate a set of points based on the desired quality, and triangulate them to a two-dimensional triangle mesh using Delaunay triangulation. Using raytracing, the mesh is projected onto the surface bouncing off the mirror. Surface intersections are recorded in a mapping with its respective texture coordinates. Based on this mesh, we do not have to execute our raytracing algorithm again if the projected image is changed, thus providing us with a significant speedup.