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Creators/Authors contains: "Misra Satyajayant"

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  1. Payment channel networks are a promising solution to the scalability challenge of blockchains and are designed for significantly increased transaction throughput compared to the layer one blockchain. Since payment channel networks are essentially decentralized peerto- peer networks, routing transactions is a fundamental challenge. Payment channel networks have some unique security and privacy requirements that make pathfinding challenging, for instance, network topology is not publicly known, and sender/receiver privacy should be preserved, in addition to providing atomicity guarantees for payments. In this paper, we present an efficient privacypreserving routing protocol, SPRITE, for payment channel networks that supports concurrent transactions. By finding paths offline and processing transactions online, SPRITE can process transactions in just two rounds, which is more efficient compared to prior work. We evaluate SPRITE’s performance using Lightning Network data and prove its security using the Universal Composability framework. In contrast to the current cutting-edge methods that achieve rapid transactions, our approach significantly reduces the message complexity of the system by 3 orders of magnitude while maintaining similar latencies. 
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    Free, publicly-accessible full text available July 1, 2025
  2. Free, publicly-accessible full text available July 1, 2025
  3. Pervasive Edge Computing (PEC), a recent addition to the edge computing paradigm, leverages the computing resources of end-user devices to execute computation tasks in close proximity to users. One of the primary challenges in the PEC environment is determining the appropriate servers for offloading computation tasks based on factors, such as computation latency, response quality, device reliability, and cost of service. Computation outsourcing in the PEC ecosystem requires additional security and privacy considerations. Finally, mechanisms need to be in place to guarantee fair payment for the executed service(s). We present 𝑃𝐸𝑃𝑃𝐸𝑅, a novel, privacy-preserving, and decentralized framework that addresses aforementioned challenges by utilizing blockchain technology and trusted execution environments (TEE). 𝑃𝐸𝑃𝑃𝐸𝑅 improves the performance of PEC by allocating resources among end-users efficiently and securely. It also provides the underpinnings for building a financial ecosystem at the pervasive edge. To evaluate the effectiveness of 𝑃𝐸𝑃𝑃𝐸𝑅, we developed and deployed a proof of concept implementation on the Ethereum blockchain, utilizing Intel SGX as the TEE technology. We propose a simple but highly effective remote attestation method that is particularly beneficial to PEC compared to the standard remote attestation method used today. Our extensive comparison experiment shows that 𝑃𝐸𝑃𝑃𝐸𝑅 is 1.23Γ— to 2.15Γ— faster than the current standard remote attestation procedure. In addition, we formally prove the security of our system using the universal composability (UC) framework. 
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    Free, publicly-accessible full text available July 1, 2025
  4. Free, publicly-accessible full text available July 1, 2025
  5. Free, publicly-accessible full text available December 15, 2024
  6. Machine Learning (ML) algorithms have shown quite promising applications in smart meter data analytics enabling intelligent energy management systems for the Advanced Metering Infrastructure (AMI). One of the major challenges in developing ML applications for the AMI is to preserve user privacy while allowing active end-users participation. This paper addresses this challenge and proposes Differential Privacy-enabled AMI with Federated Learning (DP-AMI-FL), framework for ML-based applications in the AMI. This framework provides two layers of privacy protection: first, it keeps the raw data of consumers hosting ML applications at edge devices (smart meters) with Federated Learning (FL), and second, it obfuscates the ML models using Differential Privacy (DP) to avoid privacy leakage threats on the models posed by various inference attacks. The framework is evaluated by analyzing its performance on a use case aimed to improve Short-Term Load Forecasting (STLF) for residential consumers having smart meters and home energy management systems. Extensive experiments demonstrate that the framework when used with Long Short-Term Memory (LSTM) recurrent neural network models, achieves high forecasting accuracy while preserving users data privacy. 
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