arXiv subjects

Sreeram Kannan

Publications and source records attributed to Sreeram Kannan.

At least 19 recordsLinked to original sources

arXiv2026

BigDipper: Sharded Censorship Resistant Data Availability for Leader-Based BFT

Leader-based Byzantine-fault-tolerant (BFT) protocols provide low latency and simple communication structure, but they give the leader short-term control over transaction inclusion. A malicious leader can keep the protocol live while delaying or excluding time-sensitive transactions such as auction bids, oracle updates, liquidations, and bridge messages. Existing responses often build a fixed censorship-resistance, hiding, or ordering mechanism into the protocol path, forcing all transactions to pay for the same protection level. name follows the end-to-end principle: the consensus layer exposes inclusion primitives rather than hardcoding stronger policies. Higher-layer protocols can then choose their own submission strategies and resources, whether through replication, erasure coding, or other mechanisms, to obtain the censorship-resistance, hiding, ordering, or execution guarantees they need. At the core of BigDipper is censorship-resistant data availability, or DA-CR, which certifies available replica-contributed mini-blocks for use by leader-based consensus. A central design goal is that data remains sharded on the consensus critical path: validators do not reconstruct or execute the full payload before voting, but instead check commitments, availability evidence, and the DA-CR inclusion rule. We define DA-CR guarantees for data-tampering resistance, honest mini-block inclusion, and residual leader influence. We then give concrete constructions based on erasure coding and linear commitments, analyze client-tunable transaction submission, and instantiate BigDipper inside HotStuff-2.

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arXiv2026

EigenAI: Deterministic Inference, Verifiable Results

EigenAI is a verifiable AI platform built on top of the EigenLayer restaking ecosystem. At a high level, it combines a deterministic large-language model (LLM) inference engine with a cryptoeconomically secured optimistic re-execution protocol so that every inference result can be publicly audited, reproduced, and, if necessary, economically enforced. An untrusted operator runs inference on a fixed GPU architecture, signs and encrypts the request and response, and publishes the encrypted log to EigenDA. During a challenge window, any watcher may request re-execution through EigenVerify; the result is then deterministically recomputed inside a trusted execution environment (TEE) with a threshold-released decryption key, allowing a public challenge with private data. Because inference itself is bit-exact, verification reduces to a byte-equality check, and a single honest replica suffices to detect fraud. We show how this architecture yields sovereign agents -- prediction-market judges, trading bots, and scientific assistants -- that enjoy state-of-the-art performance while inheriting security from Ethereum's validator base.

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arXiv2025

Bitcoin-Enhanced Proof-of-Stake Security: Possibilities and Impossibilities

Bitcoin is the most secure blockchain in the world, supported by the immense hash power of its Proof-of-Work miners. Proof-of-Stake chains are energy-efficient, have fast finality but face several security issues: susceptibility to non-slashable long-range safety attacks, low liveness resilience and difficulty to bootstrap from low token valuation. We show that these security issues are inherent in any PoS chain without an external trusted source, and propose a new protocol, Babylon, where an off-the-shelf PoS protocol checkpoints onto Bitcoin to resolve these issues. An impossibility result justifies the optimality of Babylon. A use case of Babylon is to reduce the stake withdrawal delay: our experimental results show that this delay can be reduced from weeks in existing PoS chains to less than 5 hours using Babylon, at a transaction cost of less than 10K USD per annum for posting the checkpoints onto Bitcoin.

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arXiv2024

Unconditionally Safe Light Client

Blockchain applications often rely on lightweight clients to access and verify on-chain data efficiently without the need to run a resource-intensive full node. These light clients must maintain robust security to protect the blockchain's integrity for users of applications built upon it, achieving this with minimal resources and without significant latency. Moreover, different applications have varying security needs. This work focuses on addressing these two key requirements in the context of Proof-of-Stake (PoS) blockchains and identifying the fundamental cost-latency trade-offs to achieve tailored, optimal security for each light client. The key security guarantee of PoS blockchains is economic (implied by the "stake"). In this paper we formalize this cryptoeconomic security to light clients, ensuring that the cost of corrupting the data provided to light clients must outweigh the potential profit, thereby economically deterring malicious actors. We further introduce "insured" cryptoeconomic security to light clients, providing unconditional protection via the attribution of adversarial actions and the consequent slashing of stakes. The divisible and fungible nature of stake facilitates programmable security, allowing for customization of the security level and insurance amount according to the specific needs of different applications. We implemented the protocols in less than 1000 lines of Solidity and TypeScript code and evaluated their gas cost, latency, and the computational overhead. For example, for a transaction with value of \$32k, the light client can choose between zero cost with a latency of 5 hours or instant confirmation with an insurance cost of \$7.45. Thus, the client can select the optimal point on the latency-cost trade-off spectrum that best aligns with its needs. Light clients require negligible storage and face minimal computational costs,...

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arXiv2024

STAKESURE: Proof of Stake Mechanisms with Strong Cryptoeconomic Safety

As of July 15, 2023, Ethererum, which is a Proof-of-Stake (PoS) blockchain [1] has around 410 Billion USD in total assets on chain (popularly referred to as total-value-locked, TVL) but has only 33 Billion USD worth of ETH staked in securing the underlying consensus of the chain [2]. A preliminary analysis might suggest that as the amount staked is far less (11x less) than the value secured, the Ethereum blockchain is insecure and "over-leveraged" in a purely cryptoeconomic sense. In this work, we investigate how Ethereum, or, more generally, any PoS blockchain can be made secure despite this apparent imbalance. Towards that end, we attempt to formalize a model for analyzing the cryptoeconomic safety of PoS blockchain, which separately analyzes the cost-of-corruption, the cost incurred by an attacker, and the profit-from-corruption, the profit gained by an attacker. We derive sharper bounds on profit-from-corruption, as well as new confirmation rules that significantly decrease this upper-bound. We evaluate cost-of-corruption and profit-from-corruption only from the perspective of attacking safety. Finally, we present a new "insurance" mechanism, STAKESURE, for allocating the slashed funds in a PoS system, that has several highly desirable properties: solving common information problem in existing blockchains, creating a mechanism for provably safe bridging, and providing the first sharp solution for automatically adjusting how much economic security is sufficient in a PoS system. Finally, we show that the system satisfies a notion of strong cryptoeconomic safety, which guarantees that no honest transactor ever loses money, and creates a closed system of Karma, which not only ensures that the attacker suffers a loss of funds but also that the harmed parties are sufficiently compensated.

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arXiv2024

Travelers: A scalable fair ordering BFT system

Many blockchain platform are subject to maximal value extraction (MEV), and users on the platform are losing money while sending transactions because the transaction order can be manipulated to extract value from them. Consensus protocols have been augmented with different notion of fair ordering in order to counter the problem. Out of all practical protocols, the most efficient BFT consensus requires $O(nTL + n^2T)$ communication complexity, where $n$ is number node, $T$ is number of transactions and $L$ is average transaction size. In this work, we propose a new system of BFT fair ordering protocols, Travelers, that substantially reduce the communication complexity. The proposed system of protocols satisfy a new notion of fair ordering, called probabilistic fair ordering, which is an extension to some existing notions of fairness. The new notion allows a small probability of error $ε$, that adversary can insert some transactions at any location in a block, but for the remaining $1-ε$ the a modified version of ordering linearizability holds. Our mechanism neither require a dissemination network nor direct submissions to all consensus nodes. The key innovation comes from a routing protocol, that is both flexible and efficient. We construct a protocol with $O(c\log({n})TL + n^2)$ communication complexity with $ε= 1/n^c$ for some system parameter $c\ge 1$.

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arXiv2023

TrustBoost: Boosting Trust among Interoperable Blockchains

Currently there exist many blockchains with weak trust guarantees, limiting applications and participation. Existing solutions to boost the trust using a stronger blockchain, e.g., via checkpointing, requires the weaker blockchain to give up sovereignty. In this paper, we propose a family of protocols in which multiple blockchains interact to create a combined ledger with boosted trust. We show that even if several of the interacting blockchains cease to provide security guarantees, the combined ledger continues to be secure - our TrustBoost protocols achieve the optimal threshold of tolerating the insecure blockchains. This optimality, along with the necessity of blockchain interactions, is formally shown within the classic shared memory model, tackling the long standing open challenge of solving consensus in the presence of both Byzantine objects and processes. Furthermore, our proposed construction of TrustBoost simply operates via smart contracts and require no change to the underlying consensus protocols of the participating blockchains, a form of ``consensus on top of consensus''. The protocols are lightweight and can be used on specific (e.g., high value) transactions; we demonstrate the practicality by implementing and deploying TrustBoost as cross-chain smart contracts in the Cosmos ecosystem using approximately 3,000 lines of Rust code, made available as open source. Our evaluation shows that using 10 Cosmos chains in a local testnet, TrustBoost has a gas cost of roughly $2 with a latency of 2 minutes per request, which is in line with the cost on a high security chain such as Bitcoin or Ethereum

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arXiv2023

SAKSHI: Decentralized AI Platforms

Large AI models (e.g., Dall-E, GPT4) have electrified the scientific, technological and societal landscape through their superhuman capabilities. These services are offered largely in a traditional web2.0 format (e.g., OpenAI's GPT4 service). As more large AI models proliferate (personalizing and specializing to a variety of domains), there is a tremendous need to have a neutral trust-free platform that allows the hosting of AI models, clients receiving AI services efficiently, yet in a trust-free, incentive compatible, Byzantine behavior resistant manner. In this paper we propose SAKSHI, a trust-free decentralized platform specifically suited for AI services. The key design principles of SAKSHI are the separation of the data path (where AI query and service is managed) and the control path (where routers and compute and storage hosts are managed) from the transaction path (where the metering and billing of services are managed over a blockchain). This separation is enabled by a "proof of inference" layer which provides cryptographic resistance against a variety of misbehaviors, including poor AI service, nonpayment for service, copying of AI models. This is joint work between multiple universities (Princeton University, University of Illinois at Urbana-Champaign, Tsinghua University, HKUST) and two startup companies (Witness Chain and Eigen Layer).

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arXiv2023

Goldfish: Peer selection using Matrix completion in unstructured P2P network

Peer-to-peer (P2P) networks underlie a variety of decentralized paradigms including blockchains, distributed file storage and decentralized domain name systems. A central primitive in P2P networks is the peer selection algorithm, which decides how a node should select a fixed number of neighbors to connect with. In this paper, we consider the design of a peer-selection algorithm for unstructured P2P networks with the goal of minimizing the broadcast latency. We propose Goldfish, a novel solution that dynamically decides the neighbor set by exploiting the past experiences as well as exploring new neighbors. The key technical contributions come from bringing ideas of matrix completion for estimating message delivery times for every possible message for every peer ever connected, and a streaming algorithm to efficiently perform the estimation while achieving good performance. The matrix completion interpolates the delivery times to all virtual connections in order to select the best combination of neighbors. Goldfish employs a streaming algorithm that only uses a short recent memory to finish matrix interpolation. When the number of publishing source is equal to a node's maximal number of connections, Goldfish found the global optimal solution with 92.7% probability by exploring every node only once. In more complex situations where nodes are publishing based on exponential distribution and adjusting connection in real time, we compare Goldfish with a baseline peer selection system, and show Goldfish saves approximately 14.5% less time under real world geolocation and propagation latency.

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arXiv2023

Improving Federated Learning Personalization via Model Agnostic Meta Learning

Federated Learning (FL) refers to learning a high quality global model based on decentralized data storage, without ever copying the raw data. A natural scenario arises with data created on mobile phones by the activity of their users. Given the typical data heterogeneity in such situations, it is natural to ask how can the global model be personalized for every such device, individually. In this work, we point out that the setting of Model Agnostic Meta Learning (MAML), where one optimizes for a fast, gradient-based, few-shot adaptation to a heterogeneous distribution of tasks, has a number of similarities with the objective of personalization for FL. We present FL as a natural source of practical applications for MAML algorithms, and make the following observations. 1) The popular FL algorithm, Federated Averaging, can be interpreted as a meta learning algorithm. 2) Careful fine-tuning can yield a global model with higher accuracy, which is at the same time easier to personalize. However, solely optimizing for the global model accuracy yields a weaker personalization result. 3) A model trained using a standard datacenter optimization method is much harder to personalize, compared to one trained using Federated Averaging, supporting the first claim. These results raise new questions for FL, MAML, and broader ML research.

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arXiv2023

HQAlign: Aligning nanopore reads for SV detection using current-level modeling

Motivation: Detection of structural variants (SV) from the alignment of sample DNA reads to the reference genome is an important problem in understanding human diseases. Long reads that can span repeat regions, along with an accurate alignment of these long reads play an important role in identifying novel SVs. Long read sequencers such as nanopore sequencing can address this problem by providing very long reads but with high error rates, making accurate alignment challenging. Many errors induced by nanopore sequencing have a bias because of the physics of the sequencing process and proper utilization of these error characteristics can play an important role in designing a robust aligner for SV detection problems. In this paper, we design and evaluate HQAlign, an aligner for SV detection using nanopore sequenced reads. The key ideas of HQAlign include (i) using basecalled nanopore reads along with the nanopore physics to improve alignments for SVs (ii) incorporating SV specific changes to the alignment pipeline (iii) adapting these into existing state-of-the-art long read aligner pipeline, minimap2 (v2.24), for efficient alignments. Results: We show that HQAlign captures about 4%-6% complementary SVs across different datasets which are missed by minimap2 alignments while having a standalone performance at par with minimap2 for real nanopore reads data. For the common SV calls between HQAlign and minimap2, HQAlign improves the start and the end breakpoint accuracy for about 10%-50% of SVs across different datasets. Moreover, HQAlign improves the alignment rate to 89.35% from minimap2 85.64% for nanopore reads alignment to recent telomere-to-telomere CHM13 assembly, and it improves to 86.65% from 83.48% for nanopore reads alignment to GRCh37 human genome.

q-bio.GN↗

arXiv2022

Minotaur: Multi-Resource Blockchain Consensus

Resource-based consensus is the backbone of permissionless distributed ledger systems. The security of such protocols relies fundamentally on the level of resources actively engaged in the system. The variety of different resources (and related proof protocols, some times referred to as PoX in the literature) raises the fundamental question whether it is possible to utilize many of them in tandem and build multi-resource consensus protocols. The challenge in combining different resources is to achieve fungibility between them, in the sense that security would hold as long as the cumulative adversarial power across all resources is bounded. In this work, we put forth Minotaur, a multi-resource blockchain consensus protocol that combines proof-of-work (PoW) and proof-of-stake (PoS), and we prove it optimally fungible. At the core of our design, Minotaur operates in epochs while continuously sampling the active computational power to provide a fair exchange between the two resources, work and stake. Further, we demonstrate the ability of Minotaur to handle a higher degree of work fluctuation as compared to the Bitcoin blockchain; we also generalize Minotaur to any number of resources. We demonstrate the simplicity of Minotaur via implementing a full stack client in Rust (available open source). We use the client to test the robustness of Minotaur to variable mining power and combined work/stake attacks and demonstrate concrete empirical evidence towards the suitability of Minotaur to serve as the consensus layer of a real-world blockchain.

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arXiv2022

Optimal Bootstrapping of PoW Blockchains

Proof of Work (PoW) blockchains are susceptible to adversarial majority mining attacks in the early stages due to incipient participation and corresponding low net hash power. Bootstrapping ensures safety and liveness during the transient stage by protecting against a majority mining attack, allowing a PoW chain to grow the participation base and corresponding mining hash power. Liveness is especially important since a loss of liveness will lead to loss of honest mining rewards, decreasing honest participation, hence creating an undesired spiral; indeed existing bootstrapping mechanisms offer especially weak liveness guarantees. In this paper, we propose Advocate, a new bootstrapping methodology, which achieves two main results: (a) optimal liveness and low latency under a super-majority adversary for the Nakamoto longest chain protocol and (b) immediate black-box generalization to a variety of parallel-chain based scaling architectures, including OHIE and Prism. We demonstrate via a full-stack implementation the robustness of Advocate under a 90% adversarial majority.

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arXiv2022

Babylon: Reusing Bitcoin Mining to Enhance Proof-of-Stake Security

Bitcoin is the most secure blockchain in the world, supported by the immense hash power of its Proof-of-Work miners, but consumes huge amount of energy. Proof-of-Stake chains are energy-efficient, have fast finality and accountability, but face several fundamental security issues: susceptibility to non-slashable long-range safety attacks, non-slashable transaction censorship and stalling attacks and difficulty to bootstrap new PoS chains from low token valuation. We propose Babylon, a blockchain platform which combines the best of both worlds by reusing the immense Bitcoin hash power to enhance the security of PoS chains. Babylon provides a data-available timestamping service, securing PoS chains by allowing them to timestamp data-available block checkpoints, fraud proofs and censored transactions on Babylon. Babylon miners merge mine with Bitcoin and thus the platform has zero additional energy cost. The security of a Babylon-enhanced PoS protocol is formalized by a cryptoeconomic security theorem which shows slashable safety and liveness guarantees.

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arXiv2021

BFT Protocol Forensics

Byzantine fault-tolerant (BFT) protocols allow a group of replicas to come to a consensus even when some of the replicas are Byzantine faulty. There exist multiple BFT protocols to securely tolerate an optimal number of faults $t$ under different network settings. However, if the number of faults $f$ exceeds $t$ then security could be violated. In this paper we mathematically formalize the study of forensic support of BFT protocols: we aim to identify (with cryptographic integrity) as many of the malicious replicas as possible and in as a distributed manner as possible. Our main result is that forensic support of BFT protocols depends heavily on minor implementation details that do not affect the protocol's security or complexity. Focusing on popular BFT protocols (PBFT, HotStuff, Algorand) we exactly characterize their forensic support, showing that there exist minor variants of each protocol for which the forensic supports vary widely. We show strong forensic support capability of LibraBFT, the consensus protocol of Diem cryptocurrency; our lightweight forensic module implemented on a Diem client is open-sourced and is under active consideration for deployment in Diem. Finally, we show that all secure BFT protocols designed for $2t+1$ replicas communicating over a synchronous network forensic support are inherently nonexistent; this impossibility result holds for all BFT protocols and even if one has access to the states of all replicas (including Byzantine ones).

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arXiv2021

DispersedLedger: High-Throughput Byzantine Consensus on Variable Bandwidth Networks

The success of blockchains has sparked interest in large-scale deployments of Byzantine fault tolerant (BFT) consensus protocols over wide area networks. A central feature of such networks is variable communication bandwidth across nodes and across time. We present DispersedLedger, an asynchronous BFT protocol that provides near-optimal throughput in the presence of such variable network bandwidth. The core idea of DispersedLedger is to enable nodes to propose, order, and agree on blocks of transactions without having to download their full content. By enabling nodes to agree on an ordered log of blocks, with a guarantee that each block is available within the network and unmalleable, DispersedLedger decouples bandwidth-intensive block downloads at different nodes, allowing each to make progress at its own pace. We build a full system prototype and evaluate it on real-world and emulated networks. Our results on a geo-distributed wide-area deployment across the Internet shows that DispersedLedger achieves 2x better throughput and 74% reduction in latency compared to HoneyBadger, the state-of-the-art asynchronous protocol.

cs.NI↗

arXiv2021

Securing Parallel-chain Protocols under Variable Mining Power

Several emerging PoW blockchain protocols rely on a "parallel-chain" architecture for scaling, where instead of a single chain, multiple chains are run in parallel and aggregated. A key requirement of practical PoW blockchains is to adapt to mining power variations over time. In this paper, we consider the design of provably secure parallel-chain protocols which can adapt to such mining power variations. The Bitcoin difficulty adjustment rule adjusts the difficulty target of block mining periodically to get a constant mean inter-block time. While superficially simple, the rule has proved itself to be sophisticated and successfully secure, both in practice and in theory. We show that natural adaptations of the Bitcoin adjustment rule to the parallel-chain case open the door to subtle, but catastrophic safety and liveness breaches. We uncover a meta-design principle that allow us to design variable mining difficulty protocols for three popular PoW blockchain proposals (Prism, OHIE, and Fruitchains) inside a common rubric. The principle has three components:(M1) a pivot chain, based on which blocks in all chains choose difficulty, (M2) a monotonicity condition for referencing pivot chain blocks and (M3) translating additional protocol aspects from using levels (depth) to using "difficulty levels". We show that protocols employing a subset of these principles may have catastrophic failures. The security of the designs is also proved using a common rubric - the key technical challenge involves analyzing the interaction between the pivot chain and the other chains, as well as bounding the sudden changes in difficulty target experienced in non-pivot chains. We empirically investigate the responsivity of the new mining difficulty rule via simulations based on historical Bitcoin data, and find that the protocol very effectively controls the forking rate across all the chains.

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arXiv2021

Blockchain CAP Theorem Allows User-Dependent Adaptivity and Finality

Longest-chain blockchain protocols, such as Bitcoin, guarantee liveness even when the number of actively participating users is variable, i.e., they are adaptive. However, they are not safe under network partitions, i.e., they do not guarantee finality. On the other hand, classical blockchain protocols, like PBFT, achieve finality but not adaptivity. Indeed, the CAP theorem in the context of blockchains asserts that no protocol can simultaneously offer both adaptivity and finality. We propose a new blockchain protocol, called the checkpointed longest chain, that offers individual users the choice between finality and adaptivity instead of imposing it at a system-wide level. This protocol's salient feature is that it supports two distinct confirmation rules: one that guarantees adaptivity and the other finality. The more optimistic adaptive rule always confirms blocks that are marked as finalized by the more conservative rule, and may possibly confirm more blocks during variable participation levels. Clients (users) make a local choice between the confirmation rules as per their personal preference, while miners follow a fixed block proposal rule that is consistent with both confirmation rules. The proposed protocol has the additional benefit of intrinsic validity: the finalized blocks always lie on a single blockchain, and therefore miners can attest to the validity of transactions while proposing blocks. Our protocol builds on the notion of a finality gadget, a popular technique for adding finality to longest-chain protocols.

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