arXiv subjects

Srinivasan Raghuraman

Publications and source records attributed to Srinivasan Raghuraman.

5 recordsLinked to original sources

arXiv2025

DTL: Data Tumbling Layer. A Composable Unlinkability for Smart Contracts

We propose Data Tumbling Layer (DTL), a cryptographic scheme for non-interactive data tumbling. The core concept is to enable users to commit to specific data and subsequently re-use to the encrypted version of these data across different applications while removing the link to the previous data commit action. We define the following security and privacy notions for DTL: (i) no one-more redemption: a malicious user cannot redeem and use the same data more than the number of times they have committed the data; (ii) theft prevention: a malicious user cannot use data that has not been committed by them; (iii) non-slanderabilty: a malicious user cannot prevent an honest user from using their previously committed data; and (iv) unlinkability: a malicious user cannot link tainted data from an honest user to the corresponding data after it has been tumbled. To showcase the practicality of DTL, we use DTL to realize applications for (a) unlinkable fixed-amount payments; (b) unlinkable and confidential payments for variable amounts; (c) unlinkable weighted voting protocol. Finally, we implemented and evaluated all the proposed applications. For the unlinkable and confidential payment application, a user can initiate such a transaction in less than $1.5$s on a personal laptop. In terms of on-chain verification, the gas cost is less than $1.8$ million.

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arXiv2023

Privacy-Preserving Financial Anomaly Detection via Federated Learning & Multi-Party Computation

One of the main goals of financial institutions (FIs) today is combating fraud and financial crime. To this end, FIs use sophisticated machine-learning models trained using data collected from their customers. The output of machine learning models may be manually reviewed for critical use cases, e.g., determining the likelihood of a transaction being anomalous and the subsequent course of action. While advanced machine learning models greatly aid an FI in anomaly detection, model performance could be significantly improved using additional customer data from other FIs. In practice, however, an FI may not have appropriate consent from customers to share their data with other FIs. Additionally, data privacy regulations may prohibit FIs from sharing clients' sensitive data in certain geographies. Combining customer data to jointly train highly accurate anomaly detection models is therefore challenging for FIs in operational settings. In this paper, we describe a privacy-preserving framework that allows FIs to jointly train highly accurate anomaly detection models. The framework combines the concept of federated learning with efficient multi-party computation and noisy aggregates inspired by differential privacy. The presented framework was submitted as a winning entry to the financial crime detection track of the US/UK PETs Challenge. The challenge considered an architecture where banks hold customer data and execute transactions through a central network. We show that our solution enables the network to train a highly accurate anomaly detection model while preserving privacy of customer data. Experimental results demonstrate that use of additional customer data using the proposed approach results in improvement of our anomaly detection model's AUPRC from 0.6 to 0.7. We discuss how our framework, can be generalized to other similar scenarios.

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arXiv2023

Privacy-Enhancing Technologies for Financial Data Sharing

Today, financial institutions (FIs) store and share consumers' financial data for various reasons such as offering loans, processing payments, and protecting against fraud and financial crime. Such sharing of sensitive data have been subject to data breaches in the past decade. While some regulations (e.g., GDPR, FCRA, and CCPA) help to prevent institutions from freely sharing clients' sensitive information, some regulations (e.g., BSA 1970) require FIs to share certain financial data with government agencies to combat financial crime. This creates an inherent tension between the privacy and the integrity of financial transactions. In the past decade, significant progress has been made in building efficient privacy-enhancing technologies that allow computer systems and networks to validate encrypted data automatically. In this paper, we investigate some of these technologies to identify the benefits and limitations of each, in particular, for use in data sharing among FIs. As a case study, we look into the emerging area of Central Bank Digital Currencies (CBDCs) and how privacy-enhancing technologies can be integrated into the CBDC architecture. Our study, however, is not limited to CBDCs and can be applied to other financial scenarios with tokenized bank deposits such as cross-border payments, real-time settlements, and card payments.

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arXiv2021

Universal Payment Channels: An Interoperability Platform for Digital Currencies

With the innovation of distributed ledger technology (DLT), often known as blockchain technology, there has been significant growth of digital tokens in the form of cryptocurrencies, stablecoins, and central bank digital currencies. As the number of DLT networks increases, each with varying design characteristics, the likelihood that transacting parties are on the same network decreases. Thus, it is crucial to facilitate payments that are universal across networks, scalable to massive loads, and highly available. We envision a future payment network that may be built on top of DLT networks without being subject to their limitations on interoperability, scalability, and availability faced by DLT payment solutions today. Specifically, we propose a hub-and-spoke payment route, referred to here as Universal Payment Channels (UPC), that can be used to support digital token transfers of funds across different networks through payment channels. We further discuss the potential use cases of the UPC technology to support, and not complicate, an already robust digital payment ecosystem. Finally, through the paper, we share some future directions of the UPC technology.

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arXiv2020

Towards a Two-Tier Hierarchical Infrastructure: An Offline Payment System for Central Bank Digital Currencies

Digital payments traditionally rely on online communications with several intermediaries such as banks, payment networks, and payment processors in order to authorize and process payment transactions. While these communication networks are designed to be highly available with continuous uptime, there may be times when an end-user experiences little or no access to network connectivity. The growing interest in digital forms of payments has led central banks around the world to explore the possibility of issuing a new type of central-bank money, known as central bank digital currency (CBDC). To facilitate the secure issuance and transfer of CBDC, we envision a CBDC design under a two-tier hierarchical trust infrastructure, which is implemented using public-key cryptography with the central bank as the root certificate authority for generating digital signatures, and other financial institutions as intermediate certificate authorities. One important design feature for CBDC that can be developed under this hierarchical trust infrastructure is an offline capability to create secure point-to-point offline payments through the use of authorized hardware. An offline capability for CBDC as digital cash can create a resilient payment system for consumers and businesses to transact in any situation. We propose an offline payment system (OPS) protocol for CBDC that allows a user to make digital payments to another user while both users are temporarily offline and unable to connect to payment intermediaries (or even the Internet). OPS can be used to instantly complete a transaction involving any form of digital currency over a point-to-point channel without communicating with any payment intermediary, achieving virtually unbounded throughput and real-time transaction latency.

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