arXiv subjects

Anwitaman Datta

Publications and source records attributed to Anwitaman Datta.

At least 19 recordsLinked to original sources

arXiv2026

How international are international computing conferences? -- An exploration with systems research conferences

In recent years, Asia's rapid growth in research output has been reshaping the computing research landscape. What was once a two-block system (America and Europe) is evolving into a multipolar world with three major hubs: America, Europe, and Asia. To study these pivotal changes and evaluate international diversity, we have analyzed the past 13 years of 13 international systems research conferences: ASPLOS, NSDI, OSDI, SIGCOMM, ATC, EuroSys, ICDCS, Middleware, SoCC, CCGRID, IC2E, IEEE Cloud and EuroPar. Our analysis focuses on accepted papers and participation in the Program Committee, grouping the results by region (America, Europe, and Asia). Surprisingly, we find a pronounced historical imbalance in international diversity among top-tier systems conferences (ASPLOS, OSDI, NSDI, SIGCOMM). While most other conferences have progressively reflected Asia's growing research presence over the past decades, this group has shown a noticeable adjustment only in the recent four years. We also identify persistent rigidities in how program committee (PC) diversity adapts to shifts in accepted paper origins, with a consistent under-representation of researchers from Asian organizations in many PCs.

cs.OH↗

arXiv2024

Collateral Portfolio Optimization in Crypto-Backed Stablecoins

Stablecoins - crypto tokens whose value is pegged to a real-world asset such as the US Dollar - are an important component of the DeFi ecosystem as they mitigate the impact of token price volatility. In crypto-backed stablecoins, the peg is founded on the guarantee that in case of system shutdown, each stablecoin can be exchanged for a basket of other crypto tokens worth approximately its nominal value. However, price fluctuations that affect the collateral tokens may cause this guarantee to be invalidated. In this work, we investigate the impact of the collateral portfolio's composition on the resilience to this type of catastrophic event. For stablecoins whose developers maintain a significant portion of the collateral (e.g., MakerDAO's Dai), we propose two portfolio optimization methods, based on convex optimization and (semi)variance minimization, that account for the correlation between the various token prices. We compare the optimal portfolios to the historical evolution of Dai's collateral portfolio, and to aid reproducibility, we have made our data and code publicly available.

cs.CR↗

arXiv2024

On Scaling LT-Coded Blockchains in Heterogeneous Networks and their Vulnerabilities to DoS Threats

Coded blockchains have acquired prominence as a promising solution to reduce storage costs and facilitate scalability. Within this class, Luby Transform (LT) coded blockchains are an appealing choice for scalability owing to the availability of a wide range of low-complexity decoders. In the first part of this work, we identify that traditional LT decoders like Belief Propagation and On-the-Fly Gaussian Elimination may not be optimal for heterogeneous networks with nodes that have varying computational and download capabilities. To address this, we introduce a family of hybrid decoders for LT codes and propose optimal operating regimes for them to recover the blockchain at the lowest decoding cost. While LT coded blockchain architecture has been studied from the aspects of storage savings and scalability, not much is known in terms of its security vulnerabilities. Pointing at this research gap, in the second part, we present novel denial-of-service threats on LT coded blockchains that target nodes with specific decoding capabilities, preventing them from joining the network. Our proposed threats are non-oblivious in nature, wherein adversaries gain access to the archived blocks, and choose to execute their attack on a subset of them based on underlying coding scheme. We show that our optimized threats can achieve the same level of damage as that of blind attacks, however, with limited amount of resources. Overall, this is the first work of its kind that opens up new questions on designing coded blockchains to jointly provide storage savings, scalability and also resilience to optimized threats.

cs.IT↗

arXiv2024

Machine unlearning through fine-grained model parameters perturbation

Machine unlearning techniques, which involve retracting data records and reducing influence of said data on trained models, help with the user privacy protection objective but incur significant computational costs. Weight perturbation-based unlearning is a general approach, but it typically involves globally modifying the parameters. We propose fine-grained Top-K and Random-k parameters perturbed inexact machine unlearning strategies that address the privacy needs while keeping the computational costs tractable. In order to demonstrate the efficacy of our strategies we also tackle the challenge of evaluating the effectiveness of machine unlearning by considering the model's generalization performance across both unlearning and remaining data. To better assess the unlearning effect and model generalization, we propose novel metrics, namely, the forgetting rate and memory retention rate. However, for inexact machine unlearning, current metrics are inadequate in quantifying the degree of forgetting that occurs after unlearning strategies are applied. To address this, we introduce SPD-GAN, which subtly perturbs the distribution of data targeted for unlearning. Then, we evaluate the degree of unlearning by measuring the performance difference of the models on the perturbed unlearning data before and after the unlearning process. By implementing these innovative techniques and metrics, we achieve computationally efficacious privacy protection in machine learning applications without significant sacrifice of model performance. Furthermore, this approach provides a novel method for evaluating the degree of unlearning.

cs.LG↗

arXiv2024

eCIL-MU: Embedding based Class Incremental Learning and Machine Unlearning

New categories may be introduced over time, or existing categories may need to be reclassified. Class incremental learning (CIL) is employed for the gradual acquisition of knowledge about new categories while preserving information about previously learned ones in such dynamic environments. It might also be necessary to also eliminate the influence of related categories on the model to adapt to reclassification. We thus introduce class-level machine unlearning (MU) within CIL. Typically, MU methods tend to be time-consuming and can potentially harm the model's performance. A continuous stream of unlearning requests could lead to catastrophic forgetting. To address these issues, we propose a non-destructive eCIL-MU framework based on embedding techniques to map data into vectors and then be stored in vector databases. Our approach exploits the overlap between CIL and MU tasks for acceleration. Experiments demonstrate the capability of achieving unlearning effectiveness and orders of magnitude (upto $\sim 278\times$) of acceleration.

cs.LG↗

arXiv2023

Perspectives of Global and Hong Kong's Media on China's Belt and Road Initiative

This study delves into the media analysis of China's ambitious Belt and Road Initiative (BRI), which, in a polarized world, and furthermore, owing to the very polarizing nature of the initiative itself, has received both strong criticisms and conversely positive coverage in media from across the world. In that context, Hong Kong's dynamic media environment, with a particular focus on its drastically changing press freedom before and after the implementation of the National Security Law is of further interest. Leveraging data science techniques, this study employs Global Database of Events, Language, and Tone (GDELT) to comprehensively collect and analyse (English) news articles on the BRI. Through sentiment analysis, we uncover patterns in media coverage over different periods from several countries across the globe, and delve further to investigate the the media situation in the Hong Kong region. This work thus provides valuable insights into how the Belt and Road Initiative has been portrayed in the media and its evolving reception on the global stage, with a specific emphasis on the unique media landscape of Hong Kong. In an era characterised by increasing globalisation and inter-connectivity, but also competition for influence, animosity and trade-wars, understanding the perceptions and coverage of such significant international projects is crucial. This work stands as an interdisciplinary endeavour merging geopolitical science and data science to uncover the intricate dynamics of media coverage in general, and with an added emphasis on Hong Kong.

cs.SI↗

arXiv2023

AMIR: Automated MisInformation Rebuttal -- A COVID-19 Vaccination Datasets based Recommendation System

Misinformation has emerged as a major societal threat in recent years in general; specifically in the context of the COVID-19 pandemic, it has wrecked havoc, for instance, by fuelling vaccine hesitancy. Cost-effective, scalable solutions for combating misinformation are the need of the hour. This work explored how existing information obtained from social media and augmented with more curated fact checked data repositories can be harnessed to facilitate automated rebuttal of misinformation at scale. While the ideas herein can be generalized and reapplied in the broader context of misinformation mitigation using a multitude of information sources and catering to the spectrum of social media platforms, this work serves as a proof of concept, and as such, it is confined in its scope to only rebuttal of tweets, and in the specific context of misinformation regarding COVID-19. It leverages two publicly available datasets, viz. FaCov (fact-checked articles) and misleading (social media Twitter) data on COVID-19 Vaccination.

cs.AI↗

arXiv2023

A Bonus-Malus Framework for Cyber Risk Insurance and Optimal Cybersecurity Provisioning

The cyber risk insurance market is at a nascent stage of its development, even as the magnitude of cyber losses is significant and the rate of cyber loss events is increasing. Existing cyber risk insurance products as well as academic studies have been focusing on classifying cyber loss events and developing models of these events, but little attention has been paid to proposing insurance risk transfer strategies that incentivise mitigation of cyber loss through adjusting the premium of the risk transfer product. To address this important gap, we develop a Bonus-Malus model for cyber risk insurance. Specifically, we propose a mathematical model of cyber risk insurance and cybersecurity provisioning supported with an efficient numerical algorithm based on dynamic programming. Through a numerical experiment, we demonstrate how a properly designed cyber risk insurance contract with a Bonus-Malus system can resolve the issue of moral hazard and benefit the insurer.

math.OC↗

arXiv2023

Blockchain-enabled Data Governance for Privacy-Preserved Sharing of Confidential Data

In a traditional cloud storage system, users benefit from the convenience it provides but also take the risk of certain security and privacy issues. To ensure confidentiality while maintaining data sharing capabilities, the Ciphertext-Policy Attribute-based Encryption (CP-ABE) scheme can be used to achieve fine-grained access control in cloud services. However, existing approaches are impaired by three critical concerns: illegal authorization, key disclosure, and privacy leakage. To address these, we propose a blockchain-based data governance system that employs blockchain technology and attribute-based encryption to prevent privacy leakage and credential misuse. First, our ABE encryption system can handle multi-authority use cases while protecting identity privacy and hiding access policy, which also protects data sharing against corrupt authorities. Second, applying the Advanced Encryption Standard (AES) for data encryption makes the whole system efficient and responsive to real-world conditions. Furthermore, the encrypted data is stored in a decentralized storage system such as IPFS, which does not rely on any centralized service provider and is, therefore, resilient against single-point failures. Third, illegal authorization activity can be readily identified through the logged on-chain data. Besides the system design, we also provide security proofs to demonstrate the robustness of the proposed system.

cs.CR↗

arXiv2023

Misinformation Concierge: A Proof-of-Concept with Curated Twitter Dataset on COVID-19 Vaccination

We demonstrate the Misinformation Concierge, a proof-of-concept that provides actionable intelligence on misinformation prevalent in social media. Specifically, it uses language processing and machine learning tools to identify subtopics of discourse and discern non/misleading posts; presents statistical reports for policy-makers to understand the big picture of prevalent misinformation in a timely manner; and recommends rebuttal messages for specific pieces of misinformation, identified from within the corpus of data - providing means to intervene and counter misinformation promptly. The Misinformation Concierge proof-of-concept using a curated dataset is accessible at: https://demo-frontend-uy34.onrender.com/

cs.CL↗

arXiv2023

BlockChain I/O: Enabling Cross-Chain Commerce

Blockchain technology enables secure tokens transfers in digital marketplaces, and recent advances in this field provide other desirable properties such as efficiency, privacy, and price stability. However, these properties do not always generalize to a setting across multiple independent blockchains. Despite the growing number of existing blockchain platforms, there is a lack of an overarching framework whose components provide all of the necessary properties for practical cross-chain commerce. We present BlockChain I/O to provide such a framework. BlockChain I/O introduces entities called cross-chain services to relay information between different blockchains. The proposed design ensures that cross-chain services cannot violate transaction safety, and they are furthermore disincentivized from other types of misbehavior through an audit system. BlockChain I/O uses native stablecoins to mitigate price fluctuations, and a decentralized ID system to allow users to prove aspects of their identity without violating privacy. After presenting the core architecture of BlockChain I/O, we demonstrate how to use it to implement a cross-chain marketplace and discuss how its desirable properties continue to hold in the end-to-end system. Finally, we use experimental evaluations to demonstrate BlockChain I/O's practical performance.

cs.CR↗

arXiv2023

GovernR: Provenance and Confidentiality Guarantees In Research Data Repositories

We propose cryptographic protocols to incorporate time provenance guarantees while meeting confidentiality and controlled sharing needs for research data. We demonstrate the efficacy of these mechanisms by developing and benchmarking a practical tool, GovernR, which furthermore takes into usability issues and is compatible with a popular open-sourced research data storage platform, Dataverse. In doing so, we identify and provide a solution addressing an important gap (though applicable to only niche use cases) in practical research data management.

cs.CR↗

arXiv2023

PIEChain -- A Practical Blockchain Interoperability Framework

A plethora of different blockchain platforms have emerged in recent years, but many of them operate in silos. As such, there is a need for reliable cross-chain communication to enable blockchain interoperability. Blockchain interoperability is challenging because transactions can typically not be reverted - as such, if one transaction is committed then the protocol must ensure that all related transactions are committed as well. Existing interoperability approaches, e.g., Cosmos and Polkadot, are limited in the sense that they only support interoperability between their own subchains, or require intrusive changes to existing blockchains. To overcome this limitation, we propose PIEChain, a general, Kafka-based cross-chain communication framework. We utilize PIEChain for a practical case study: a cross-chain auction in which users who hold tokens on multiple chains bid for a ticket sold on another chain. PIEChain is the first publicly available, practical implementation of a general framework for cross-chain communication.

cs.CR↗

arXiv2023

CroCoDai: A Stablecoin for Cross-Chain Commerce

Decentralized Finance (DeFi), in which digital assets are exchanged without trusted intermediaries, has grown rapidly in value in recent years. The global DeFi ecosystem is fragmented into multiple blockchains, fueling the demand for cross-chain commerce. Existing approaches for cross-chain transactions, e.g., bridges and cross-chain deals, achieve atomicity by locking assets in escrow. However, locking up assets increases the financial risks for the participants, especially due to price fluctuations and the long latency of cross-chain transactions. Stablecoins, which are pegged to a non-volatile asset such as the US dollar, help mitigate the risk associated with price fluctuations. However, existing stablecoin designs are tied to individual blockchain platforms, and trusted parties or complex protocols are needed to exchange stablecoin tokens between blockchains. Our goal is to design a practical stablecoin for cross-chain commerce. Realizing this goal requires addressing two challenges. The first challenge is to support a large and growing number of blockchains efficiently. The second challenge is to be resilient to price fluctuations and blockchain platform failures. We present CroCoDai to address these challenges. We also present three prototype implementations of our stablecoin system, and show that it incurs small execution overhead.

cs.CR↗

arXiv2022

Misleading the Covid-19 vaccination discourse on Twitter: An exploratory study of infodemic around the pandemic

In this work, we collect a moderate-sized representative corpus of tweets (200,000 approx.) pertaining Covid-19 vaccination spanning over a period of seven months (September 2020 - March 2021). Following a Transfer Learning approach, we utilize the pre-trained Transformer-based XLNet model to classify tweets as Misleading or Non-Misleading and validate against a random subset of results manually. We build on this to study and contrast the characteristics of tweets in the corpus that are misleading in nature against non-misleading ones. This exploratory analysis enables us to design features (such as sentiments, hashtags, nouns, pronouns, etc) that can, in turn, be exploited for classifying tweets as (Non-)Misleading using various ML models in an explainable manner. Specifically, several ML models are employed for prediction, with up to 90% accuracy, and the importance of each feature is explained using SHAP Explainable AI (XAI) tool. While the thrust of this work is principally exploratory analysis in order to obtain insights on the online discourse on Covid-19 vaccination, we conclude the paper by outlining how these insights provide the foundations for a more actionable approach to mitigate misinformation. The curated dataset and code is made available (Github repository) so that the research community at large can reproduce, compare against, or build upon this work.

cs.CL↗

arXiv2022

QPQ 1DLT: A system for the rapid deployment of secure and efficient EVM-based blockchains

Limited scalability and transaction costs are, among others, some of the critical issues that hamper a wider adoption of distributed ledger technologies (DLT). That is particularly true for the Ethereum blockchain, which, so far, has been the ecosystem with the highest adoption rate. Quite a few solutions, especially on the Ethereum side of things, have been attempted in the last few years. Most of them adopt the approach to offload transactions from the blockchain mainnet, a.k.a. Level 1 (L1), to a separate network. Such systems are collectively known as Level 2 (L2) systems. While mitigating the scalability issue, the adoption of L2 introduces additional drawbacks: users have to trust that the L2 system has correctly performed transactions or, conversely, high computational power is required to prove transactions correctness. In addition, significant technical knowledge is needed to set up and manage such an L2 system. To tackle such limitations, we propose 1DLT: a novel system that enables rapid and trustless deployment of an Ethereum Virtual Machine based blockchain that overcomes those drawbacks.

cs.DC↗

arXiv2021

A Modular Framework for Centrality and Clustering in Complex Networks

The structure of many complex networks includes edge directionality and weights on top of their topology. Network analysis that can seamlessly consider combination of these properties are desirable. In this paper, we study two important such network analysis techniques, namely, centrality and clustering. An information-flow based model is adopted for clustering, which itself builds upon an information theoretic measure for computing centrality. Our principal contributions include a generalized model of Markov entropic centrality with the flexibility to tune the importance of node degrees, edge weights and directions, with a closed-form asymptotic analysis. It leads to a novel two-stage graph clustering algorithm. The centrality analysis helps reason about the suitability of our approach to cluster a given graph, and determine `query' nodes, around which to explore local community structures, leading to an agglomerative clustering mechanism. The entropic centrality computations are amortized by our clustering algorithm, making it computationally efficient: compared to prior approaches using Markov entropic centrality for clustering, our experiments demonstrate multiple orders of magnitude of speed-up. Our clustering algorithm naturally inherits the flexibility to accommodate edge directionality, as well as different interpretations and interplay between edge weights and node degrees. Overall, this paper thus not only makes significant theoretical and conceptual contributions, but also translates the findings into artifacts of practical relevance, yielding new, effective and scalable centrality computations and graph clustering algorithms, whose efficacy has been validated through extensive benchmarking experiments.

cs.SI↗

arXiv2019

A Blueprint for Interoperable Blockchains

Research in blockchain systems has mainly focused on improving security and bridging the performance gaps between blockchains and databases. Despite many promising results, we observe a worrying trend that the blockchain landscape is fragmented in which many systems exist in silos. Apart from a handful of general-purpose blockchains, such as Ethereum or Hyperledger Fabric, there are hundreds of others designed for specific applications and typically do not talk to each other. In this paper, we describe our vision of interoperable blockchains. We argue that supporting interaction among different blockchains requires overcoming challenges that go beyond data standardization. The underlying problem is to allow smart contracts running in different blockchains to communicate. We discuss three open problems: access control, general cross-chain transactions, and cross-chain communication. We describe partial solutions to some of these problems in the literature. Finally, we propose a novel design to overcome these challenges.

cs.DB↗