Zhongming Yao, Jun Pang, Chenxu Wang, Qian Ma · 6 authors
Blockchain data is temporal. Every transaction carries a timestamp and the chain imposes a total order, so on-chain data forms per-source time-series streams. However, existing systems support only basic lookups on blocks and transactions, and cannot answer time-series queries such as time-range retrieval and windowed aggregation. Offloading queries off-chain restores expressiveness, but the off-chain query layer is untrusted, so results must be verifiable. To this end, we propose VeriTS, the first verifiable time-series query framework for blockchain systems. It supports efficient range and aggregation queries without altering blockchain storage structures. VeriTS maintains an off-chain query layer that represents each stream through an authenticated aggregate interval tree. The tree serves as the query index and as the authenticated data structure at once, so a windowed aggregate is answered by folding a logarithmic number of node aggregates. VeriTS verifies completeness through a minimum covering set and soundness through aggregate folding. It extends both guarantees to an approximate path over model segments, redefining completeness and soundness under bounded error. Miners validate a model's residual rather than replay its computation, so even an adversarial encoder can inflate proof size and answer width but never correctness. Experiments offer evidence that on windowed aggregation, VeriTS improves verification efficiency by more than two orders of magnitude over per-record proofs. Range-query proofs shrink by up to 14.5x.
Canonical reference map for the PRE-GHR publication series. Records every record in the series with its concept DOI, version history, and relational links; declares numbering conventions and known gaps; establishes citation and versioning standards. This map is itself a PRE-GHR series record. v33 (2026-08-28). Two changes. 1. PRE-GHR XXXIX v5.0 registered (version DOI 10.5281/zenodo.22145426; concept DOI 10.5281/zenodo.21889278 unchanged). v5.0 is the release version closing all six objections of an adversarial pre-submission review, one revision ticket each: Theorem 4 unilateralized with the converse demoted to an observation under an explicit complete-erasure assumption (R01); ledger counts restricted to lower witnesses, the ordering claim made conditional on a fixed normalization and full retention (R02); an explicit two-sided finite-sample bound replacing an expectation-only argument (R03); four empirical mappings corrected — schema-field disjointness separated from retained-trace intersection, join error reported two-sided with the earlier “directionally safe, never over-counting” claim withdrawn, overlap-error direction governed by an error budget, retention ratio restated in matched units (R04); measure-relative notation throughout (R05); subject classification reassessed and Related Work rebuilt (R06). This is the first subject-classification reversal recorded in this map: cs.MA is withdrawn as unsupported by the technical content — the formalism contains no agent population, strategic interaction, or equilibrium claim — and replaced by cs.CR primary with a cs.DB cross-list; Related Work now separates the lineage the paper inherits from (linked timestamping and distributed witnesses, split-view detection and the undefined gossip layer, existence-not-authenticity timestamping, provenance and lineage, record linkage, trace semantics, measure and order) from adjacent recent lines cited for comparison only, assigning priority to the sources where the paper's constructions proved to be rediscoveries. Two gaps are declared inherited rather than closed: the hash-chain anchor has no consistency-proof comparison mechanism, and the anchor-propagation layer is undefined in the source standard as well. 2. The AI-collaboration attribution note (drafted 2026-08-20, previously unpublished as a local v32.1 revision) is merged into this version. It records that papers in the series are drafted with AI assistance, that the author block is platform-plus-model double-written from XL v1.3 onward, and how the platform-only author line of earlier versions is to be read. On merge, the coverage clause of the writing-model statement was narrowed under red-pen review (2026-08-28): the claim's width is aligned to the strength of its evidence. The complement of the recorded provider-fallback events establishes that no fallback leg entered a paper-writing session; it does not establish per-paper model attribution for the entire series. The statement is therefore scoped to the drafting sessions of the pre-v1.3 papers named in the per-paper note, and the narrowing itself is recorded in the revision history so that the difference between the unpublished local note and this published version is auditable. Delivery-fingerprint discipline updated this day. A PDF's md5 is a build-instance fingerprint, not a content fingerprint: pdflatex writes /CreationDate and /ID on every build, so the same source compiled twice differs in md5 while the typeset content is identical (measured: 68 differing bytes, all inside that region). Deliverables in this series now carry file md5, a content fingerprint with the extractor and version named, page count and byte count, produced under a reproducible build with the embedded date pinned. Record count unchanged: 39 records (27 series-internal).
PRE-GHR XXXIX v5.0 (2026-08-28) — release version closing all six objections of an adversarial pre-submission review. PRE-GHR XXXVIII gave the series its first formal definition of the irreducible governance residue δ0μ(P) := inf over admissible P' of ||residue(P')|| and proved a bit-level lower bound. This paper builds the property theory. The mathematics is a domain-order theory: every inequality follows from how the admissible domain D(P) behaves under enlargement or composition of protocols. We prove monotonicity of δ0μ in the erasure surface, an exact composition law δ0μ(P1 ∘ P2) = δ0μ(P1) + δ0μ(P2) − |T(P1) ∩ T(P2)| under explicit hypotheses (segment autonomy, joint attainability, cross-segment cleanliness), and positivity δ0μ(P) > 0 whenever T(P) is non-empty as a purely normative fact, with Landauer's principle confined to the physical interpretation. We then ask what a running system's audit ledgers can measure. The answer is stratified: the ordering structure is measurable in principle — conditional on a fixed normalization and full retention — while the absolute value is stated relative to a fixed code-point measure, and the aggregate-to-single-protocol bridge remains open. No interception statistic is claimed to equal δ0μ; where the wall cannot be built, the gap is marked, not papered over. Changes in v5.0 (six revision tickets, R01–R06, each closing one reviewer objection): R01 — Theorem 4 unilateralized: the safe direction (T(P) non-empty ⇒ δ0μ > 0) remains a theorem; the converse is demoted to Observation 4.1 under an explicit complete-erasure assumption. A witness-reading remark records that δ0μ is a minimum witness cardinality in the sense of why-provenance, inherited and not claimed as new. R02 — ledger counts restricted to lower witnesses only: the ordering claim is measurable solely under a fixed normalization and full retention, stated as an explicit condition rather than an implicit assumption. R03 — the uniform-sampling remark now carries an explicit finite-sample bound (Hoeffding's inequality in its standard form), two-sided: “holds in expectation” is no longer used as if it held for a sample. R04 — four empirical mappings corrected: schema-field disjointness is separated from retained-trace intersection; the approximate join reports both false-negative and false-positive error (the earlier “directionally safe, never over-counting” claim is withdrawn); the overlap-error direction is declared two-sided and governed by an error budget rather than assumed away; and the retention ratio is restated as an interception-event ratio in matched units. R05 — measure-relative notation throughout: bits and code points are two measures on one trace universe, so every ordering claim is stated at a fixed μ and changing μ defines a new quantity rather than restating the old one. R06 — subject classification reassessed and Related Work rebuilt. The paper contains no multiagent model and no coordinated-interaction result; the earlier cs.MA classification is withdrawn as unsupported by the technical content, and the classification adopted here is cs.CR primary with cs.DB cross-list. Related Work now separates the lineage the paper inherits from — linked timestamping and distributed witnesses (Haber & Stornetta 1991; Bayer, Haber & Stornetta 1993), split-view detection and the undefined gossip layer (Certificate Transparency, RFC 6962 / RFC 9162), existence-not-authenticity timestamping (OpenTimestamps), provenance and lineage (W3C PROV; Buneman, Khanna & Tan 2001; Cui, Widom & Wiener 2000), record linkage (Fellegi & Sunter 1969), trace semantics (Hoare 1978; Brookes, Hoare & Roscoe 1984), and measure and order (Halmos; Davey & Priestley) — from adjacent recent lines cited for comparison only. Where a construction of this paper rediscovers an existing one, priority is assigned to the source and no originality is claimed. Honesty notes. Citations to Hoeffding, Fellegi & Sunter, Halmos, Davey & Priestley and the CSP literature are made at the level of the standard statement of each framework only, pending full-text verification. Two candidate references were deliberately excluded because their primary sources could not be verified. Two gaps are inherited rather than closed: the hash-chain anchor has no consistency-proof comparison mechanism, and the anchor-propagation (gossip) layer is undefined in the source standard as well. Open questions Q5.1, Q5.3 and Q5.4 remain declared open.
Due to their increasingly large volumes, outsourcing of trajectory storage and querying to third-party service providers has become attractive. However, in such outsourced environments, service providers may return incorrect, e.g., incomplete, tampered, or invalid query results, making verifiability of query results an important consideration. Existing hybrid-storage blockchains offer limited support for trajectory data, lacking authenticated data structures (ADS) that enable efficient verification. For example, ADSs designed for queries on one-dimensional data are unsuitable for queries on multidimensional trajectory data, while ADSs tailored for discrete data may yield incomplete results when applied to continuous trajectory data. We propose the first framework for verifiable trajectory range queries in hybrid-storage blockchains, called VTRQ. It features two efficient ADSs: (i) a spatial ADS for road networks that leverages hierarchical organization to aggregate trajectory, edge, and node hashes, thus reducing redundant computations and improving spatial verification efficiency; and (ii) a temporal ADS based on interval trees, which indexes only the start and end times of trajectories, thereby enabling pruning and efficient temporal verification. By separating spatial and temporal indexing, the method reduces the need for data comparison, enhancing both query and verification efficiency. To aggregate spatial and temporal query results, VTRQ provides a spatio-temporal edge aggregation mechanism that combines temporal verification of spatial nodes, spatial intersection computation, and temporal intersection analysis to achieve spatio-temporal filtering.
Replicated databases and permissioned blockchain systems rely on Byzantine Fault-Tolerant (BFT) consensus to maintain a globally consistent order of transactions across distributed replicas. These protocols preserve safety even under asynchrony, as they commit a transaction only after agreement among a strong quorum of replicas. During network partitions, however, when no strong quorum is reachable, they lose liveness and cannot make useful progress. In this paper, we present Cassandra, a consensus protocol that enables partial progress without sacrificing safety. Cassandra achieves this through a two-tier certification framework that decouples availability from commitment, allowing each partition to extend its own chain and reconcile these chains once the network is restored. To support this, Cassandra introduces a pacemaker that advances views without requiring a strong quorum and calibrates each replica's timeout off the critical path. Our evaluation results show that Cassandra remains competitive with state-of-the-art BFT protocols under stable conditions, sustaining 900K TPS at 16 replicas and 480K TPS at 104 replicas, with latency ranging from 0.31s at 16 replicas to 0.75s at 104 replicas. Under severe partitions, Cassandra maintains non-zero speculative throughput through PoA-backed progress, preserving work that can be reconciled once connectivity is restored.
The literature on blockchain-based databases is divided into permissioned blockchains and permissionless account-based blockchains. However, the former is not fully decentralized, and the latter suffers from challenges in scalability and practicality. We propose SpendableStore, a hybrid on/off-chain database that operates on top of permissionless UTXO-based blockchains as a novel approach to the problem of data decentralization. Our design integrates atomic data units into individual UTXOs to create a new blockchain concept called Spendable Data Objects that perform traditional CRUD operations. The integrity, immutability, and ownership of these Spendable Data Objects are safeguarded directly by the blockchain peer nodes, thus constraining the power of database administrators to achieve true data decentralization. We further support database transactions and propose an isolation mechanism called Future Now Snapshot Isolation to reason about transactional correctness in SpendableStore. We performed experiments on a major blockchain's Mainnet and observed up to 16x better throughput compared to a state-of-the-art blockchain-based database.
Zhengqing Liu, Alberto Sonnino, Igor Zablotchi, Eleftherios Kokoris-Kogias · 5 authors
Modern blockchains rely on a modular architecture that decouples consensus from execution. Recent advances in consensus algorithms have shifted the bottleneck to the execution layer, which must deterministically follow the consensus order and handle increasingly complex, compute-intensive smart contracts. We identify that single-node validators cannot keep up, motivating the need for a scale-out design. We design Remora, a scale-out smart contract execution engine. Remora adopts an efficient asymmetric architecture with centralized transaction dispatching and distributed execution, and depends on an object versioning scheme with a strict ownership model to guarantee deterministic scale-out execution. Remora achieves up to 3x throughput improvement compared to state-of-the-art deterministic execution schemes, scales up to 250k TPS, matching modern consensus performance, and reduces latency by up to 5ms. We also show that Remora elastically adapts to bursty workloads and dynamic access patterns using real-world traces. Remora's main performance benefits come from a novel stateless-stateful separation during smart contract execution, which overlaps the execution of state-independent tasks with consensus, and a new locality-aware and load-balanced scheduling scheme.
Knob tuning plays a critical role in improving the performance of permissioned blockchains. However, efficient tuning remains challenging due to the architectural complexity of blockchains and the semantic gap between knob-specific logic and the numerical optimization requirements of tuning tools. In addition, configuration changes are often coupled across different stages of the transaction pipeline, making their performance impact difficult to isolate and predict. Since each trial requires deployment and distributed benchmarking, ineffective exploration incurs substantial cost. These challenges motivate BCTuner, a Large Language Model (LLM)-guided framework that combines knowledge-guided reasoning with structured search. BCTuner organizes multi-source tuning knowledge to support LLM-based reasoning over knob semantics, constraints, and deployment context. It formulates tuning as a Monte Carlo Tree Search (MCTS) process over structured action trajectories, where configurations are incrementally constructed, validated, evaluated, and refined rather than generated in one step. BCTuner further applies adaptive pruning to discard infeasible or low-potential branches before system evaluation. We evaluate BCTuner on Hyperledger Fabric and ChainMaker under diverse workloads and network settings. Experimental results show that BCTuner achieves up to 211.38% throughput improvement over default configurations and outperforms the state-of-the-art blockchain tuning method by up to 20% in performance, while requiring up to 8x fewer interactions with the blockchain system.
Data is critical for the operation of any organization and needs to be protected, especially against attacks that compromise the state of the database. In this paper, we explore an approach based on Byzantine-fault tolerant replicated state machines, built on top of a deterministic extension of PostgreSQL. Each replica deterministically executes transactions recorded in a shared log/blockchain. Our focus is on creating a practical system that is designed for efficient and quick detection of corruption, as well as quick repair concurrent with execution of transactions. We also present a performance study showing the efficiency and practicality of our approach. We believe our work lays the foundations for the practical use of the BFT replicated state machine approach in the context of databases.
This study presents a structured dataset of blockchain-registered artificial intelligence agents under the ERC-8004 standard on Ethereum. The dataset integrates on-chain identity records, minting transactions, transfer events, reputation summaries, and individual feedback records, together with resolved off-chain metadata where available. Data were collected from Ethereum mainnet using Web3 RPC queries and processed into tabular form to enable reproducible analysis. The dataset covers 10,000 agents within a defined block range and includes both event-level records and aggregated summaries. It enables empirical research on agent identity formation, reputation systems, service exposure, and early-stage decentralized AI ecosystems. This resource supports studies in blockchain analytics, decentralized trust infrastructure, and the emerging agentic economy.
Luigi Crisci, Lorenz Schuler, Herbert Jordan, Bernhard Scholz
The Ethereum state database uses Merkle Patricia Trie (MPT), which suffers from large witness proof sizes and high storage overhead. Verkle Tries have been proposed as a replacement, offering witness proofs below 150 bytes through vector commitments and Inner Product Argument aggregation. However, deploying a Verkle Trie in a high-throughput, short block-time blockchain such as Sonic, which produces a block every 300 milliseconds, introduces substantial engineering challenges related to storage efficiency, commitment computation costs, and the need to serve both live and historical state queries in real time. We present SonicDB S6, a production-grade Rust Verkle Trie database for the Sonic blockchain, which leverages its non-forking property to enable aggressive storage optimizations. Occupancy-aware node specializations, selected via an $\mathcal{O}(k n^2)$ dynamic program, reduce live storage by 97.8\%. Delta nodes that record only changed slots reduce archive storage by 95\%. Batched updates, multi-threaded commitment computation, and homomorphic Pedersen caching yield $3.2\times$ higher throughput than a persistent Geth Verkle baseline while sustaining production block-rate performance.
Optimizing asset exchanges on blockchain-driven platforms poses a novel and challenging graph query optimization problem. In this model, assets represent vertices and exchanges form edges, recasting the graph query task as a routing problem over a large-scale, dynamic graph. However, the existing solutions fail to solve the problem efficiently due to the non-linear nature of the edge weights defined by a concave swap function. To address the challenge, we propose PRIME, a two-stage iterative graph algorithm designed for the Token Graph Routing Problem (TGRP). The first stage employs a pruned graph search to efficiently identify a set of high-potential routing paths. The second stage formulates the allocation task as a strongly convex optimization problem, which we solve using our novel Adaptive Sign Gradient Method (ASGM) with a linear convergence rate. Extensive experiments on real-world Ethereum data confirm PRIME's advantages over industry baselines. PRIME consistently outperforms the widely-used Uniswap routing algorithm, achieving up to 8.42 basis points (bps) better execution prices on large trades while reducing computation up to 96.7%. The practicality of PRIME is further validated by its deployment in hedge fund production environments, demonstrating its viability as a scalable graph query processing solution for high-frequency decentralized markets.
Dense retrieval services increasingly underpin semantic search, recommendation, and retrieval-augmented generation, yet clients typically receive only a top-$k$ list with no auditable evidence of how it was produced. We present V3DB, a verifiable, versioned vector-search service that enables audit-on-demand correctness checks for approximate nearest-neighbour (ANN) retrieval executed by a potentially untrusted service provider. V3DB commits to each corpus snapshot and standardises an IVF-PQ search pipeline into a fixed-shape, five-step query semantics. Given a public snapshot commitment and a query embedding, the service returns the top-$k$ payloads and, when challenged, produces a succinct zero-knowledge proof that the output is exactly the result of executing the published semantics on the committed snapshot -- without revealing the embedding corpus or private index contents. To make proving practical, V3DB avoids costly in-circuit sorting and random access by combining multiset equality/inclusion checks with lightweight boundary conditions. Our prototype implementation based on Plonky2 achieves up to $22\times$ faster proving and up to $40\%$ lower peak memory consumption than the circuit-only baseline, with millisecond-level verification time. Github Repo at https://github.com/TabibitoQZP/zk-IVF-PQ.
Blockchain provides a decentralized and tamper-resistant ledger for securely recording transactions across a network of untrusted nodes. While its transparency and integrity are beneficial, the substantial storage requirements for maintaining a complete transaction history present significant challenges. For example, Ethereum nodes require around 23TB of storage, with an annual growth rate of 4TB. Prior studies have employed various strategies to mitigate the storage challenges. Notably, COLE significantly reduces storage size and improves throughput by adopting a column-based design that incorporates a learned index, effectively eliminating data duplication in the storage layer. However, this approach has limitations in supporting chain reorganization during blockchain forks and state pruning to minimize storage overhead. In this paper, we propose COLE$^+$, an enhanced storage solution designed to address these limitations. COLE$^+$ incorporates a novel rewind-supported in-memory tree structure for handling chain reorganization, leveraging content-defined chunking (CDC) to maintain a consistent hash digest for each block. For on-disk storage, a new two-level Merkle Hash Tree (MHT) structure, called prunable version tree, is developed to facilitate efficient state pruning. Both theoretical and empirical analyses show the effectiveness of COLE$^+$ and its potential for practical application in real-world blockchain systems.
Transaction flow networks are crucial in detecting illicit activities such as wash trading, credit card fraud, cashback arbitrage fraud, and money laundering. \revise{Our collaborator, Grab, a leader in digital payments in Southeast Asia, faces increasingly sophisticated fraud patterns in its transaction flow networks. In industry settings such as Grab's fraud detection pipeline, identifying fraudulent activities heavily relies on detecting dense flows within transaction networks. Motivated by this practical foundation,} we propose the \emph{\(S\)-\(T\) densest flow} (\SDMF{}) query. Given a transaction flow network \( G \), a source set \( \Src \), a sink set \( \Dst \), and a size threshold \( k \), the query outputs subsets \( \Src' \subseteq \Src \) and \( \Dst' \subseteq \Dst \) such that the maximum flow from \( \Src' \) to \( \Dst' \) is densest, with \(|\Src' \cup \Dst'| \geq k\). Recognizing the NP-hardness of the \SDMF{} query, we develop an efficient divide-and-conquer algorithm, CONAN. \revise{Driven by industry needs for scalable and efficient solutions}, we introduce an approximate flow-peeling algorithm to optimize the performance of CONAN, enhancing its efficiency in processing large transaction networks. \revise{Our approach has been integrated into Grab's fraud detection scenario, resulting in significant improvements in identifying fraudulent activities.} Experiments show that CONAN outperforms baseline methods by up to three orders of magnitude in runtime and more effectively identifies the densest flows. We showcase CONAN's applications in fraud detection on transaction flow networks from our industry partner, Grab, and on non-fungible tokens (NFTs).
Andrey Chursin, Lefteris Kokoris-Kogias, Alex Orlov, Alberto Sonnino · 5 authors
Log-Structured Merge-Trees (LSM-trees) dominate persistent key-value storage but suffer from high write amplification from 10x to 30x under random workloads due to repeated compaction. This overhead becomes prohibitive for large values with uniformly distributed keys, a workload common in content-addressable storage, deduplication systems, and blockchain validators. We present Tidehunter, a storage engine that eliminates value compaction by treating the Write-Ahead Log (WAL) as permanent storage rather than a temporary recovery buffer. Values are never overwritten; and small, lazily-flushed index tables map keys to WAL positions. Tidehunter introduces (a) lock-free writes that saturate NVMe drives through atomic allocation and parallel copying, (b) an optimistic index structure that exploits uniform key distributions for single-roundtrip lookups, and (c) epoch-based pruning that reclaims space without blocking writes. On a 1 TB dataset with 1 KB values, Tidehunter achieves 830K writes per second, that is 8.4x higher than RocksDB and 2.9x higher than BlobDB, while improving point queries by 1.7x and existence checks by 15.6x. We validate real-world impact by integrating Tidehunter into Sui, a high-throughput blockchain, where it maintains stable throughput and latency under loads that cause RocksDB-backed validators to collapse. Tidehunter is production-ready and is being deployed in production within Sui.
In primary-backup replication, consensus latency is bounded by the time for backup nodes to replay (re-execute) transactions proposed by the primary. In this work, we present Ira, a framework to accelerate backup replay by transmitting compact \emph{hints} alongside transaction batches. Our key insight is that the primary, having already executed transactions, possesses knowledge of future access patterns which is exactly the information needed for optimal replay. We use Ethereum for our case study and present a concrete protocol, Ira-L, within our framework to improve cache management of Ethereum block execution. The primaries implementing Ira-L provide hints that consist of the working set of keys used in an Ethereum block and one byte of metadata per key indicating the table to read from, and backups use these hints for efficient block replay. We evaluated Ira-L against the state-of-the-art Ethereum client reth over two weeks of Ethereum mainnet activity ($100,800$ blocks containing over $24$ million transactions). Our hints are compact, adding a median of $47$ KB compressed per block ($\sim5\%$ of block payload). We observe that the sequential hint generation and block execution imposes a $28.6\%$ wall-time overhead on the primary, though the direct cost from hints is $10.9\%$ of execution time; all of which can be pipelined and parallelized in production deployments. On the backup side, we observe that Ira-L achieves a median per-block speedup of $25\times$ over baseline reth. With $16$ prefetch threads, aggregate replay time drops from $6.5$ hours to $16$ minutes ($23.6\times$ wall-time speedup).
Modern blockchain applications benefit from the ability to specify sequencing constraints on the transactions that interact with them. This paper proposes a principled and axiomatically justified way of adding sequencing constraints on smart contract function calls that balances expressivity with the tractability of block production. Specifically, we propose a system in which contract developers are allowed to set an integer global priority for each of their calls, so long as that the call's chosen priority is no higher than the priority of any of its referenced calls. Block builders must then simply sequence transactions in priority order (from high to low priority), breaking ties however they would like. We show that this system is the unique system that satisfies five independent axioms.
Decentralized lending protocols, exemplified by Aave V3, have transformed financial intermediation by enabling permissionless, multi-chain borrowing and lending without intermediaries. Despite managing over $10 billion in total value locked, empirical research remains severely constrained by the lack of standardized, cross-chain event-level datasets. This paper introduces the first comprehensive, event-driven data infrastructure for Aave V3 spanning six major EVM-compatible chains (Ethereum, Arbitrum, Optimism, Polygon, Avalanche, and Base) from respective deployment blocks through October 2025. We collect and fully decode eight core event types -- Supply, Borrow, Withdraw, Repay, LiquidationCall, FlashLoan, ReserveDataUpdated, and MintedToTreasury -- producing over 50 million structured records enriched with block metadata and USD valuations. Using an open-source Python pipeline with dynamic batch sizing and automatic sharding (each file less than or equal to 1 million rows), we ensure strict chronological ordering and full reproducibility. The resulting publicly available dataset enables granular analysis of capital flows, interest rate dynamics, liquidation cascades, and cross-chain user behavior, providing a foundational resource for future studies on decentralized lending markets and systemic risk.
Jordan, Herbert, Jezek, Kamil, Subotic, Pavle, Scholz, Bernhard
The State Database of a blockchain stores account data and enables authentication. Modern blockchains use fast consensus protocols to avoid forking, improving throughput and finality. However, Ethereum's StateDB was designed for a forking chain that maintains multiple state versions. While newer blockchains adopt Ethereum's standard for DApp compatibility, they do not require multiple state versions, making legacy Ethereum databases inefficient for fast, non-forking blockchains. Moreover, existing StateDB implementations have been built on key-value stores (e.g., LevelDB), which make them less efficient. This paper introduces a novel state database that is a native database implementation and maintains Ethereum compatibility while being specialized for non-forking blockchains. Our database delivers ten times speedups and 99% space reductions for validators, and a threefold decrease in storage requirements for archive nodes.
Digital forensics faces unprecedented challenges with the emergence of digital twins and metaverse technologies. This paper presents the first comparative analysis between blockchain-based and traditional database systems for managing digital twin evidence in forensic investigations. We conducted controlled experiments comparing the Ethereum blockchain with IPFS storage against traditional SQL databases for digital twin evidence management. Our findings reveal that while blockchain provides superior data integrity and immutability, crucial for forensic applications, traditional databases offer better performance consistency. The blockchain implementation showed faster average storage times but higher variability in retrieval operations. Both systems maintained forensic integrity through hash verification, though blockchain's immutable nature provides additional security guarantees essential for legal proceedings. This research contributes to the development of robust digital forensic methodologies for emerging technologies in the metaverse era.
The potential of agricultural data (AgData) to drive efficiency and sustainability is stifled by the "AgData Paradox": a pervasive lack of trust and interoperability that locks data in silos, despite its recognized value. This paper introduces AgriTrust, a federated semantic governance framework designed to resolve this paradox. AgriTrust integrates a multi-stakeholder governance model, built on pillars of Data Sovereignty, Transparent Data Contracts, Equitable Value Sharing, and Regulatory Compliance, with a semantic digital layer. This layer is realized through the AgriTrust Core Ontology, a formal OWL ontology that provides a shared vocabulary for tokenization, traceability, and certification, enabling true semantic interoperability across independent platforms. A key innovation is a blockchain-agnostic, multi-provider architecture that prevents vendor lock-in. The framework's viability is demonstrated through case studies across three critical Brazilian supply chains: coffee (for EUDR compliance), soy (for mass balance), and beef (for animal tracking). The results show that AgriTrust successfully enables verifiable provenance, automates compliance, and creates new revenue streams for data producers, thereby transforming data sharing from a trust-based dilemma into a governed, automated operation. This work provides a foundational blueprint for a more transparent, efficient, and equitable agricultural data economy.
Setchain has been proposed to increase blockchain scalability by relaxing the strict total order requirement among transactions. Setchain organizes elements into a sequence of sets, referred to as epochs, so that elements within each epoch are unordered. In this paper, we propose and evaluate three distinct Setchain algorithms, that leverage an underlying block-based ledger. Vanilla is a basic implementation that serves as a reference point. Compresschain aggregates elements into batches, and compresses these batches before appending them as epochs in the ledger. Hashchain converts batches into fixed-length hashes which are appended as epochs in the ledger. This requires Hashchain to use a distributed service to obtain the batch contents from its hash. To allow light clients to safely interact with only one server, the proposed algorithms maintain, as part of the Setchain, proofs for the epochs. An epoch-proof is the hash of the epoch, cryptographically signed by a server. A client can verify the correctness of an epoch with $f+1$ epoch-proofs (where $f$ is the maximum number of Byzantine servers assumed). All three Setchain algorithms are implemented on top of the CometBFT blockchain application platform. We conducted performance evaluations across various configurations, using clusters of four, seven, and ten servers. Our results show that the Setchain algorithms reach orders of magnitude higher throughput than the underlying blockchain, and achieve finality with latency below 4 seconds.
Hybrid storage solutions have emerged as potent strategies to alleviate the data storage bottlenecks prevalent in blockchain systems. These solutions harness off-chain Storage Services Providers (SPs) in conjunction with Authenticated Data Structures (ADS) to ensure data integrity and accuracy. Despite these advancements, the reliance on centralized SPs raises concerns about query correctness. Although ADS can verify the existence of individual query results, they fall short of preventing SPs from omitting valid results. In this paper, we delineate the fundamental distinctions between data search in blockchains and traditional database systems. Drawing upon these insights, we introduce BPI, a lightweight framework that enables efficient keyword queries and maintenance with low overhead. We propose "Articulated Search", a query pattern specifically designed for blockchain environments that enhances search efficiency while significantly reducing costs during data user updates. Furthermore, BPI employs a suite of validation models to ensure the inclusion of all valid content in search results while maintaining low overhead. Extensive experimental evaluations demonstrate that the BPI framework achieves outstanding scalability and performance in keyword searches within blockchain, surpassing EthMB+ and state of the art search databases commonly used in mainstream hybrid storage blockchains (HSB).