Cryptocurrencies have emerged as a critical medium for digital financial transactions, driving widespread adoption while simultaneously exposing users to escalating fraud risks. The irreversible nature of cryptocurrency transactions, combined with the absence of consumer protection mechanisms, leaves users vulnerable to substantial financial losses and emotional distress. To address these vulnerabilities, we introduce Insured Cryptocurrency Transactions (ICT), a novel decentralized insurance...

This paper proposes Oryx, a system for efficiently detecting cycles in federated graphs where parts of the graph are held by different parties and are private. Cycle identification is an important building block in designing fraud detection algorithms that operate on confidential transaction data held by different financial institutions. Oryx allows detecting cycles of various length while keeping the topology of the graphs secret, and it does so efficiently. Oryx leverages the observation...

Decision trees are an important class of supervised learning algorithms. When multiple entities contribute data to train a decision tree (e.g. for fraud detection in the financial sector), data privacy concerns necessitate the use of a privacy-enhancing technology such as secure multi-party computation (MPC) in order to secure the underlying training data. Prior state-of-the-art (Hamada et al.) construct an MPC protocol for decision tree training with a communication of $\mathcal{O}(hmN\log...

Federated Learning (FL) is a data-minimization approach enabling collaborative model training across diverse clients with local data, avoiding direct data exchange. However, state-of-the-art FL solutions to identify fraudulent financial transactions exhibit a subset of the following limitations. They (1) lack a formal security definition and proof, (2) assume prior freezing of suspicious customers’ accounts by financial institutions (limiting the solutions’ adoption), (3) scale poorly,...

Graph convolutional networks (GCNs) are gaining popularity due to their powerful modelling capabilities. However, guaranteeing privacy is an issue when evaluating on inputs that contain users’ sensitive information such as financial transactions, medical records, etc. To address such privacy concerns, we design Entrada, a framework for securely evaluating GCNs that relies on the technique of secure multiparty computation (MPC). For efficiency and accuracy reasons, Entrada builds over the MPC...

Information sharing between financial institutions can uncover complex financial crimes such as money laundering and fraud. However, such information sharing is often not possible due to privacy and commercial considerations, and criminals can exploit this intelligence gap in order to hide their activities by distributing them between institutions, a form of the practice known as ``layering''. We describe an algorithm that allows financial intelligence analysts to trace the flow of funds...

Users of decentralized financial networks suffer from inventive security exploits. Identity-based fraud prevention methods are inapplicable in these networks, as they contradict their privacy-minded design philosophy. Novel mitigation strategies are therefore needed. Their rollout, however, may damage other desirable network properties. In this work, we introduce an evaluation framework for mitigation strategies in decentralized financial networks. This framework allows researchers and...

An "Authorised Push Payment" (APP) fraud refers to the case where fraudsters deceive a victim to make payments to bank accounts controlled by them. The total amount of money stolen via APP frauds is swiftly growing. Although regulators have provided guidelines to improve victims' protection, the guidelines are vague and the victims are not receiving sufficient protection. To facilitate victims' reimbursement, in this work, we propose a protocol called "Payment with Dispute Resolution" (PwDR)...

The advent of decentralized trading markets introduces a number of new challenges for consensus protocols. In addition to the 'usual' attacks - a subset of the validators trying to prevent disagreement -- there is now the possibility of financial fraud, which can abuse properties not normally considered critical in consensus protocols. We investigate the issues of attackers manipulating or exploiting the order in which transactions are scheduled in the blockchain. More concretely, we look...

Machinelearning(ML)istodaycommonlyemployedintheFinancialServicesSector(FSS) to create various models to predict a variety of conditions ranging from financial transactions fraud to outcomes of investments and also targeted marketing campaigns. The common ML technique used for the modeling is supervised learning using regression algorithms and usually involves large amounts of data that needs to be shared and prepared before the actual learning phase. Compliance with privacy laws and...

Bitcoin and similar blockchain systems have a limited transaction throughput because each transaction must be processed by all parties, on-chain. Payment channels relieve the blockchain by allowing parties to execute transactions off-chain while maintaining the on-chain security guarantees, i.e., no party can be cheated out of their funds. However, to maintain these guarantees all parties must follow blockchain updates ardently. To alleviate this issue, a channel party can hire a...

We consider the issue of securing dark pools/markets in the financial services sector. These markets currently are executed via trusted third parties, leading to potential fraud being able to be conducted by the market operators. We present a potential solution to this problem by using Multi-Party Computation to enable a trusted third party to be emulated in software. Our experiments show that whilst the standard market clearing mechanism of Continuous Double Auction in lit markets is not...

Collaboration between financial institutions helps to improve detection of fraud. However, exchange of relevant data between these institutions is often not possible due to privacy constraints and data confidentiality. An important example of relevant data for fraud detection is given by a transaction graph, where the nodes represent bank accounts and the links consist of the transactions between these accounts. Previous works show that features derived from such graphs, like PageRank, can...

By representing data in a unary way, the identity of the bits can be used as a printing pad to stain the data with the identity of its handlers. Passing data will identify its custodians, its pathway, and its bona fide. This technique will allow databases to recover from a massive breach as the thieves will be caught when trying to use this 'sticky data'. Heavily traveled data on networks will accumulate the 'fingerprints' of its holders, to allow for a forensic analysis of fraud attempts,...

The sweeping success of the original (2008) bitcoin protocol proves that digital currency has arrived. The mounting opposition from the financial establishment indicates an overshoot. We propose to tame bitcoin into bitcoin.BitMint: keeping the bitcoin excitement -- fitted into real world security, stability and fraud concerns. The basic idea is to excise the bitcoin money generation formula, and otherwise apply bitcoin essentially “as is” over digital coins which are redeemable by the...