publications

2024

1. ACM IMC ’24

   Poster: Diverging Branches - How different are RPKI Trees across RIRs?

   Gouda, Deepak, and Testart, Cecilia

   In Proceedings of the 2024 ACM on Internet Measurement Conference 2024

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   Resource Public Key Infrastructure (RPKI) is a critical component in securing the inter-domain routing infrastructure today. More than 50% of the routed IPv4 and IPv6 prefixes are covered by RPKI Route Origin Authorizations (ROAs). ROAs are cryptographically verifi- able records of the Autonomous System (AS) authorized to originate routes to a set of prefixes. Network operators are increasingly rely- ing on RPKI to validate routing information and reduce the spread of BGP hijacks and misconfigurations. RPKI infrastructure has five root authorities maintained by the five Regional Internet Registries (RIRs). Each root authority independently implements its RPKI in- frastructure, choosing how to manage certificate production from its self-signed root of trust certificate. In this poster, we study the different designs of RPKI infrastructure across the five roots and how these differences impact the characteristics of the RPKI Cer- tificate repository, such as scalability and compute requirements. We discover that some RPKI repositories are computationally more expensive than others due to their design.

2. TPRC ’24

   Identifying Current Barriers in RPKI Adoption

   Testart, Cecilia, Wolff, Josephine,  Gouda, Deepak, and Fontugne, Romain

   The Research Conference on Communications, Information & Internet Policy Sep 2024

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3. HOTMOBILE ’24

   Demo: On-Device Video Analysis with LLMs

   Jaganathan, Vishnu,  Gouda, Deepak, Arora, Kriti, Aggarwal, Mohit, and Zhang, Chao

   In Proceedings of the 25th International Workshop on Mobile Computing Systems and Applications Feb 2024

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   We present a new on-device pipeline that efficiently summarizes lecture videos and provides relevant answers directly from a smartphone. We utilize widely accessible tools like OCR and Vosk speech-to-text, coupled with powerful large language models (LLMs), to identify crucial sentences and generate summaries. By harnessing the capabilities of LLMs and the computational power of mobile devices, we fine-tune and quantize BERT and GPT-2 to achieve efficient lecture video summarization and question answering on consumer-grade smartphones like the Pixel 8 Pro. Notably, this approach eliminates the need for cloud APIs, ensuring enhanced user privacy and minimal mobile data usage.https://www.youtube.com/shorts/zwGdONlKays

2023

1. arXiv

   Parameter-free version of Adaptive Gradient Methods for Strongly-Convex Functions

   Gouda, Deepak, Naveed, Hassan, and Kamath, Salil

   arXiv pre-print Jun 2023

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   The optimal learning rate for adaptive gradient methods applied to λ-strongly convex functions relies on the parameters λ and learning rate η. In this paper, we adapt a universal algorithm along the lines of Metagrad, to get rid of this dependence on λ and η. The main idea is to concurrently run multiple experts and combine their predictions to a master algorithm. This master enjoys O(d log T) regret bounds.

2021

1. COMSNETS

   Design and Validation of BlockEval, A Blockchain Simulator

   Gouda, Deepak, Jolly, Shashwat, and Kapoor, Kalpesh

   In 2021 International Conference on COMmunication Systems NETworkS (COMSNETS) Jun 2021

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   Blockchain technology is finding its application in a wide range of areas. Due to the highly decentralized and distributed nature of blockchain technology, it is essential to understand the behaviour of a system before its actual deployment. In this paper, we introduce the design and architecture of our blockchain simulator, BlockEval, which mimics the behaviour of concurrent operations that occur in a real-life blockchain system. We have established the correctness of our simulator by comparing it with an independent model that is built from the real Bitcoin-transactions data using deep-learning techniques. Unlike existing validation procedures which do not scale to large topologies, our method is scalable and efficient in terms of time and compute-infrastructure requirements. The observations made using our simulator are found to match with the results obtained from the model trained on real Bitcoin-transactions data.

2020

1. Springer

   Construction of Confidence Interval for a Univariate Stock Price Signal Predicted Through Long Short Term Memory Network

   De, Shankhajyoti, Dey, Arabin Kumar, and Gouda, Deepak

   Annals of Data Science Jul 2020

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   In this paper, we show an innovative way to construct bootstrap confidence interval of a signal estimated based on a univariate LSTM model. We take three different types of bootstrap methods for dependent set up. We prescribe some useful suggestions to select the optimal block length while performing the bootstrapping of the sample. We also propose a benchmark to compare the confidence interval measured through different bootstrap strategies. We illustrate the experimental results through some stock price data set.