HYPA: Efficient Detection of Path Anomalies in Time Series Data on Networks

The unsupervised detection of anomalies in time series data has important applications in user behavioral modeling, fraud detection, and cybersecurity. Anomaly detection has, in fact, been extensively studied in categorical sequences. However, we often have access to time series data that represent paths through networks. Examples include transaction sequences in financial networks, click streams of users in networks of cross-referenced documents, or travel itineraries in transportation networks. To reliably detect anomalies, we must account for the fact that such data contain a large number of independent observations of paths constrained by a graph topology. Moreover, the heterogeneity of real systems rules out frequency-based anomaly detection techniques, which do not account for highly skewed edge and degree statistics. To address this problem, we introduce HYPA, a novel framework for the unsupervised detection of anomalies in large corpora of variable-length temporal paths in a graph. HYPA provides an efficient analytical method to detect paths with anomalous frequencies that result from nodes being traversed in unexpected chronological order.Comment: 11 pages with 8 figures and supplementary material. To appear at SIAM Data Mining (SDM 2020

Settlement finality as a public good in large-value payment systems

Target is a real time gross settlement (RTGS) large value payment network operated by European central banks that eliminates systemic risk. Euro1 is a privately operated delayed net settlement (DNS) network that reduces substantially systemic risk but does not eliminate it. This difference makes RTGS networks more expensive to users even if both networks had the same unit operating costs. This provides an incentive for users to shift payments to the more risky network in normal times and back to Target in times of financial market disruption. The estimated extra cost to a DNS network from posting collateral sufficient to cover all exposures (and eliminate systemic risk) is from 15 to 42 cents per transaction. If full cost recovery on an RTGS system were reduced by this amount, user collateral costs — but not risks — would be equalized between networks. Full collateralization on DNS networks would equalize both user costs and risks.payments, settlement, public good

Settlement finality as a public good in large-value payment systems

Target is a real time gross settlement (RTGS) large value payment network operated by European central banks that eliminates systemic risk. Euro1 is a privately operated delayed net settlement (DNS) network that reduces substantially systemic risk but does not eliminate it. This difference makes RTGS networks more expensive to users even if both networks had the same unit operating costs. This provides an incentive for users to shift payments to the more risky network in normal times and back to Target in times of financial market disruption. The estimated extra cost to a DNS network from posting collateral sufficient to cover all exposures (and eliminate systemic risk) is from 15 to 42 cents per transaction. If full cost recovery on an RTGS system were reduced by this amount, user collateral costs but not risks would be equalized between networks. Full collateralization on DNS networks equalizes both user costs and risks. JEL Classification: E58, G15, H23, H41payments, public good, settlement

RACED: Routing in Payment Channel Networks Using Distributed Hash Tables

The Bitcoin scalability problem has led to the development of off-chain financial mechanisms such as payment channel networks (PCNs) which help users process transactions of varying amounts, including micro-payment transactions, without writing each transaction to the blockchain. Since PCNs only allow path-based transactions, effective, secure routing protocols that find a path between a sender and receiver are fundamental to PCN operations. In this paper, we propose RACED, a routing protocol that leverages the idea of Distributed Hash Tables (DHTs) to route transactions in PCNs in a fast and secure way. Our experiments on real-world transaction datasets show that RACED gives an average transaction success ratio of 98.74%, an average pathfinding time of 31.242 seconds, which is $1.65*10^3$, $1.8*10^3$, and $4*10^2$ times faster than three other recent routing protocols that offer comparable security/privacy properties. We rigorously analyze and prove the security of RACED in the Universal Composability framework.Comment: A short version of this work has been accepted to the 19th ACM ASIA Conference on Computer and Communications Security (ACM ASIACCS 2024

Multilateral Transparency for Security Markets Through DLT

For decades, changing technology and policy choices have worked to fragment securities markets, rendering them so dark that neither ownership nor real-time price of securities are generally visible to all parties multilaterally. The policies in the U.S. National Market System and the EU Market in Financial Instruments Directive— together with universal adoption of the indirect holding system— have pushed Western securities markets into a corner from which escape to full transparency has seemed either impossible or prohibitively expensive. Although the reader has a right to skepticism given the exaggerated promises surrounding blockchain in recent years, we demonstrate in this paper that distributed ledger technology (DLT) contains the potential to convert fragmented securities markets back to multilateral transparency. Leading markets generally lack transparency in two ways that derive from their basic structure: (1) multiple platforms on which trades in the same security are matched have separate bid/ask queues and are not consolidated in real time (fragmented pricing), and (2) highspeed transfers of securities are enabled by placing ownership of the securities in financial institutions, thus preventing transparent ownership (depository or street name ownership). The distributed nature of DLT allows multiple copies of the same pricing queue to be held simultaneously by a large number of order-matching platforms, curing the problem of fragmented pricing. This same distributed nature of DLT would allow the issuers of securities to be nodes in a DLT network, returning control over securities ownership and transfer to those issuers and thus, restoring transparent ownership through direct holding with the issuer. A serious objection to DLT is that its latency is very high—with each Bitcoin blockchain transaction taking up to ten minutes. To remedy this, we first propose a private network without cumbersome proof-of-work cryptography. Second, we introduce into our model the quickly evolving technology of “lightning networks,” which are advanced two-layer off-chain networks conducting high-speed transacting with only periodic memorialization in the permanent DLT network. Against the background of existing securities trading and settlement, this Article demonstrates that a DLT network could bring multilateral transparency and thus represent the next step in evolution for markets in their current configuration

Locally Differentially Private Embedding Models in Distributed Fraud Prevention Systems

Global financial crime activity is driving demand for machine learning solutions in fraud prevention. However, prevention systems are commonly serviced to financial institutions in isolation, and few provisions exist for data sharing due to fears of unintentional leaks and adversarial attacks. Collaborative learning advances in finance are rare, and it is hard to find real-world insights derived from privacy-preserving data processing systems. In this paper, we present a collaborative deep learning framework for fraud prevention, designed from a privacy standpoint, and awarded at the recent PETs Prize Challenges. We leverage latent embedded representations of varied-length transaction sequences, along with local differential privacy, in order to construct a data release mechanism which can securely inform externally hosted fraud and anomaly detection models. We assess our contribution on two distributed data sets donated by large payment networks, and demonstrate robustness to popular inference-time attacks, along with utility-privacy trade-offs analogous to published work in alternative application domains