publications
2025
- Towards a Non-Binary View of IPv6 AdoptionSulyab Thottungal Valapu and John Heidemann2025
Twelve years have passed since World IPv6 Launch Day, but what is the current state of IPv6 deployment? Prior work has examined IPv6 status as a binary: can you use IPv6, or not? As deployment increases we must consider a more nuanced, non-binary perspective on IPv6: how much and often can a user or a service use IPv6? We consider this question as a client, server, and cloud provider. Considering the client’s perspective, we observe user traffic. We see that the fraction of IPv6 traffic a user sends varies greatly, both across users and day-by-day, with a standard deviation of over 15%. We show this variation occurs for two main reasons. First, IPv6 traffic is primarily human-generated, thus showing diurnal patterns. Second, some services are IPv6-forward and others IPv6-laggards, so as users do different things their fraction of IPv6 varies. We look at server-side IPv6 adoption in two ways. First, we expand analysis of web services to examine how many are only partially IPv6 enabled due to their reliance on IPv4-only resources. Our findings reveal that only 12.5% of top 100k websites qualify as fully IPv6-ready. Finally, we examine cloud support for IPv6. Although all clouds and CDNs support IPv6, we find that tenant deployment rates vary significantly across providers. We find that ease of enabling IPv6 in the cloud is correlated with tenant IPv6 adoption rates, and recommend best practices for cloud providers to improve IPv6 adoption. Our results suggest IPv6 deployment is growing, but many services lag, presenting a potential for improvement.
- Crowd-SFT: Crowdsourcing for LLM AlignmentAlex Sotiropoulos, Sulyab Thottungal Valapu, Linus Lei, and 2 more authorsIn 2025 IEEE International Conference on Decentralized Applications and Infrastructures (DAPPS), 2025Poster
Large Language Models (LLMs) increasingly rely on Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF) to align model responses with human preferences. While RLHF employs a reinforcement learning approach with a separate reward model, SFT uses human-curated datasets for supervised learning. Both approaches traditionally depend on small, vetted groups of annotators, making them costly, prone to bias, and limited in scalability. We propose an open, crowd-sourced fine-tuning framework that addresses these limitations by enabling broader feedback collection for SFT without extensive annotator training. Our framework promotes incentive fairness via a point-based reward system correlated with Shapley values and guides model convergence through iterative model updates. Our multi-model selection framework demonstrates up to a 55% reduction in target distance over single-model selection, enabling subsequent experiments that validate our point-based reward mechanism’s close alignment with Shapley values (a well-established method for attributing individual contributions) thereby supporting fair and scalable participation.
- Geofeed Adoption and AuthenticationDipsy Desai, Kicho Yu, and Sulyab Thottungal ValapuIn 2025 IEEE Network Operations and Management Symposium (NOMS), 2025
IP Geofeed is a recently proposed informational standard that allows network operators to publish the geographical location of deployed IPv4 and IPv6 prefixes. In this work we study the adoption of IP geofeed, assess deployment of geofeed at Regional Internet Registry and Autonomous System levels, and analyze adherence to RFC 8805 and RFC 9092 in deployed geofeeds. We evaluate the authentication mechanism proposed in RFC 9092 and find that it lacks key features from a security perspective. We propose a novel approach to simplify the authentication of geofeeds and assess its efficiency using different benchmarks. Our findings highlight the challenges in current geofeed adoption and the potential for improving both security and scalability in geofeed validation processes.
- Reward-Based Blockchain Infrastructure for 3D IC Supply Chain ProvenanceSulyab Thottungal Valapu, Aritri Saha, Bhaskar Krishnamachari, and 2 more authorsIn 2025 IEEE International Symposium on Hardware Oriented Security and Trust (HOST), 2025
In response to the growing demand for enhanced performance and power efficiency, the semiconductor industry has witnessed a paradigm shift toward heterogeneous integration, giving rise to 2.5D/3D chips. These chips incorporate diverse chiplets, manufactured globally and integrated into a single chip. Securing these complex 2.5D/3D integrated circuits (ICs) presents a formidable challenge due to inherent trust issues within the semiconductor supply chain. Chiplets produced in untrusted locations may be susceptible to tampering, introducing malicious circuits that could compromise sensitive information. This paper introduces an innovative approach that leverages blockchain technology to establish traceability for ICs and chiplets throughout the supply chain. Given that chiplet manufacturers are dispersed globally and may operate within different blockchain consortiums, ensuring the integrity of data within each blockchain ledger becomes imperative. To address this, we propose a novel dual-layer approach for establishing distributed trust across diverse blockchain ledgers. The lower layer comprises of a blockchain-based framework for IC supply chain provenance that enables transactions between blockchain instances run by different consortiums, making it possible to trace the complete provenance DAG of each IC. The upper layer implements a multi-chain reputation scheme that assigns reputation scores to entities while specifically accounting for highrisk transactions that cross-blockchain trust zones. This approach enhances the credibility of the blockchain data, mitigating potential risks associated with the use of multiple consortiums and ensuring a robust foundation for securing 2.5D/3D ICs in the evolving landscape of heterogeneous integration.
2023
- DARSAN: A Decentralized Review System Suitable for NFT MarketplacesSulyab Thottungal Valapu, Tamoghna Sarkar, Jared Coleman, and 5 more authorsIn Blockchain – ICBC 2023, 2023
We introduce DARSAN, a decentralized review system designed for Non-Fungible Token (NFT) marketplaces, to address the challenge of verifying the quality of highly resalable products with few verified buyers by incentivizing unbiased reviews. DARSAN works by iteratively selecting a group of reviewers (called “experts”) who are likely to both accurately predict the objective popularity and assess some subjective quality of the assets uniquely associated with NFTs. The system consists of a two-phased review process: a “pre-listing” phase where only experts can review the product, and a “pre-sale” phase where any reviewer on the system can review the product. Upon completion of the sale, DARSAN distributes incentives to the participants and selects the next generation of experts based on the performance of both experts and non-expert reviewers. We evaluate DARSAN through simulation and show that, once bootstrapped with an initial set of appropriately chosen experts, DARSAN favors honest reviewers and improves the quality of the expert pool over time without any external intervention even in the presence of potentially malicious participants.
@inproceedings{thottungal_valapu_darsan_2023, author = {Thottungal Valapu, Sulyab and Sarkar, Tamoghna and Coleman, Jared and Avyukt, Anusha and Embrechts, Hugo and Torfs, Dimitri and Minelli, Michele and Krishnamachari, Bhaskar}, editor = {Wang, Qin and Feng, Jun and Zhang, Liang-Jie}, title = {DARSAN: A Decentralized Review System Suitable for NFT Marketplaces}, booktitle = {Blockchain -- ICBC 2023}, year = {2023}, publisher = {Springer Nature Switzerland}, address = {Cham}, pages = {3--20}, isbn = {978-3-031-44920-8}, doi = {10.1007/978-3-031-44920-8_1}, url = {https://doi.org/10.1007/978-3-031-44920-8_1}, }
2022
- A Survey of Probabilistic Micropayment SchemesSulyab Thottungal Valapu and Bhaskar KrishnamachariMar 2022
Although the earliest electronic micropayment schemes date back to the mid-90s, recent years have witnessed a resurgence of research interest in the field due to the rising popularity of cryptocurrencies and the associated increase in transaction fees. Probabilistic micropayment schemes have shown particular theoretical promise due to their ability to aggregate payments beyond client-merchant pairs. In this paper, we review various probabilistic micropayment protocols proposed in both pre-cryptocurrency and post-cryptocurrency eras and provide an analysis of what the future of research in this field could look like.
@techreport{thottungal_valapu_survey_2022, title = {A {Survey} of {Probabilistic} {Micropayment} {Schemes}}, url = {https://blockchain.ieee.org/images/files/pdf/techbriefs-2022-q1/a-survey-of-probabilistic-micropayment-schemes.pdf}, author = {Thottungal Valapu, Sulyab and Krishnamachari, Bhaskar}, month = mar, year = {2022}, }