Digital payment systems have revolutionized financial transactions, offering unparalleled convenience and accessibility to users worldwide. However, the increasing popularity of these platforms has also attracted malicious actors seeking to exploit their vulnerabilities for financial gain. To address this challenge, robust and adaptable scam detection mechanisms are crucial for maintaining the trust and safety of digital payment ecosystems. This paper presents a comprehensive approach to scam detection, focusing on the Unified Payments Interface (UPI) in India, Google Pay (GPay) as a specific use case. The approach leverages Large Language Models (LLMs) to enhance scam classification accuracy and designs a digital assistant to aid human reviewers in identifying and mitigating fraudulent activities. The results demonstrate the potential of LLMs in augmenting existing machine learning models and improving the efficiency, accuracy, quality, and consistency of scam reviews, ultimately contributing to a safer and more secure digital payment landscape. Our evaluation of the Gemini Ultra model on curated transaction data showed a 93.33% accuracy in scam classification. Furthermore, the model demonstrated 89% accuracy in generating reasoning for these classifications. A promising fact, the model identified 32% new accurate reasons for suspected scams that human reviewers had not included in the review notes.

We design and implement LEGOStore, an erasure coding (EC) based linearizable data store over geo-distributed public cloud data centers (DCs). For such a data store, the confluence of the following factors opens up opportunities for EC to be latency-competitive with replication: (a) the necessity of communicating with remote DCs to tolerate entire DC failures and implement linearizability; and (b) the emergence of DCs near most large population centers. LEGOStore employs an optimization framework that, for a given object, carefully chooses among replication and EC, as well as among various DC placements to minimize overall costs. To handle workload dynamism, LEGOStore employs a novel agile reconfiguration protocol. Our evaluation using a LEGOStore prototype spanning 9 Google Cloud Platform DCs demonstrates the efficacy of our ideas. We observe cost savings ranging from moderate (5-20%) to significant (60%) over baselines representing the state of the art while meeting tail latency SLOs. Our reconfiguration protocol is able to transition key placements in 3 to 4 inter-DC RTTs (< 1s in our experiments), allowing for agile adaptation to dynamic conditions.

The configuration of a distributed storage system typically includes, among other parameters, the set of servers and their roles in the replication protocol. Although mechanisms for changing the configuration at runtime exist, it is usually left to system administrators to manually determine the "best" configuration and periodically reconfigure the system, often by trial and error. This paper describes a new workload-driven optimization framework that dynamically determines the optimal configuration at run-time. We focus on optimizing leader and quorum based replication schemes and divide the framework into three optimization tiers, dynamically optimizing different configuration aspects: 1) leader placement, 2) roles of different servers in the replication protocol, and 3) replica locations. We showcase our optimization framework by applying it to a large-scale distributed storage system used internally in Google and demonstrate that most client applications significantly benefit from using our framework, reducing average operation latency by up to 94%.

Recent architectural trends---cheap, fast solid-state storage, inexpensive DRAM, and multi-core CPUs---provide an opportunity to rethink the interface between applications and persistent storage. To leverage these advances, we propose a new system architecture called Hathi that provides an in-memory transactional heap made persistent using high-speed flash drives. With Hathi, programmers can make consistent concurrent updates to in-memory data structures that survive system failures.

In this paper we extend the state-of-the-art in utilizing background knowledge for supervised classification by exploiting the semantic relationships between terms explicated in Ontologies. Preliminary evaluations indicate that the new approach generally improves precision and recall, more so for hard to classify cases and reveals patterns indicating the usefulness of such background knowledge.

Dynamic finite versioning (DFV) schemes are an effective approach to concurrent transaction and query processing, where a finite number of consistent, but maybe slightly out-of-date, logical snapshots of the database can be dynamically derived for query access. In DFV, the storage overhead for keeping additional versions of changed data to support the logical snapshots and the amount of obsolescence faced by queries are two major performance issues. We analyze the performance of DFV, with emphasis on the trade-offs between the storage cost and obsolescence. We develop analytical models based on a renewal process approximation to evaluate the performance of DFV using M/spl ges/2 snapshots. Asymptotic closed form results for high query arrival rates are given for the case of two snapshots. Simulation is used to validate the analytical models and to evaluate the tradeoffs between various strategies for advancing snapshots when M>2. The results show that (1) the analytical models match closely with simulation; (2) storage cost and obsolescence are sensitive to the snapshot advancing strategies, and (3) usually, increasing the number of snapshots demonstrates a trade-off between storage overhead and query obsolescence. For cases with skewed accessor low update rates, a small increase in the number of snapshots beyond two can substantially reduce the obsolescence. Such a reduction in obsolescence is more significant as the coefficient of variation of the query length distribution becomes larger. Moreover, for very low update rates, a large number of snapshots can be used to reduce the obsolescence to almost zero without increasing the storage overhead.