Control of encrypted digital assets is traditionally equated with permanent possession of private keys, a model that precludes regulatory supervision, conditional delegation, and legally compliant transfer at the cryptographic layer. Existing remedies (multi-signature schemes, threshold signatures, smart contracts, custodial delegation) require persistent key exposure, on-chain state mutation, or trusted intermediaries. We introduce Condition-Triggered Dormant Authorization Paths (CT-DAP), a cryptographic asset control method built on destructible authorization factors and parameterized by a root-derivable framework satisfying deterministic key derivation, context-isolated capability generation, and authorization-bound revocation. Under CT-DAP, control rights are dormant authorization paths composed of user-held credentials and administrative factors held by independent custodians; a path remains cryptographically inactive until all factors are simultaneously available. Upon verification of predefined conditions (e.g., user consent, inheritance events, time-based triggers), the corresponding factor is released, activating the path. Revocation is achieved by destroying factors, rendering the path permanently unusable without altering the cryptographic root. We formalize the threat model, define security games for unauthorized control resistance, path isolation, and stateless revocation, and prove security under standard assumptions (AEAD security of AES-GCM-SIV, PRF security of HKDF, memory-hardness of Argon2id, collision resistance of SHA-256). We instantiate CT-DAP using the Atomic Cryptographic Entity Generative Framework (ACE-GF) and evaluate performance, demonstrating sub-second activation latency with configurable security-performance trade-offs.
Post-quantum signature schemes impose kilobyte-scale on-chain artifacts. Verifying them inside ZK circuits merely relocates the cost via expensive lattice arithmetic in prover circuits. We present ZK-ACE (Zero-Knowledge Authorization for Cryptographic Entities), which replaces transaction-carried signature objects with identity-bound ZK statements. Given a deterministic identity derivation primitive (DIDP) as a black box, the prover demonstrates in zero knowledge that an identity consistent with an on-chain commitment authorized the transaction; no signature object is produced or verified on-chain. We provide game-based definitions and reduction-based proofs for authorization soundness, replay resistance, substitution resistance, and cross-domain separation, under knowledge soundness, collision resistance, and DIDP recovery hardness. Structural data accounting shows an order-of-magnitude reduction in per-transaction authorization data versus direct PQC deployment. A reference implementation offers two backends: Circle STARK (341 active rows / 361 AIR constraint expressions, 14.5 ms prove, 1.1 ms verify, approx. 107 KB proofs, transparent setup, post-quantum-oriented) and Groth16/BN254 (2,155 R1CS constraints, 37.3 ms prove, 128-byte proofs). Both are roughly 500--2,300x smaller than in-circuit PQC signature verification. Under mandatory per-block STARK aggregation, per-transaction consensus-visible data is approx. 160 bytes.
AI agents that execute tasks via tool calls frequently hallucinate results - fabricating tool executions, misstating output counts, or presenting inferences as facts. Recent approaches to verifiable AI inference rely on zero-knowledge proofs, which provide cryptographic guarantees but impose minutes of proving time per query, making them impractical for interactive agents. We propose NabaOS, a lightweight verification framework inspired by Indian epistemology (Nyaya Shastra), which classifies every claim in an LLM response by its epistemic source (pramana): direct tool output (pratyaksha), inference (anumana), external testimony (shabda), absence (abhava), or ungrounded opinion. Our runtime generates HMAC-signed tool execution receipts that the LLM cannot forge, then cross-references claims against these receipts to detect hallucinations in real time. We evaluate on NyayaVerifyBench, a new benchmark of 1,800 agent response scenarios across four languages with injected hallucinations of six types. NabaOS detects 94.2% of fabricated tool references, 87.6% of count misstatements, and 91.3% of false absence claims, with <15ms verification overhead per response. For deep delegation (agents performing multi-step web tasks), our cross-checking protocol catches 78.4% of URL fabrications via independent re-fetching. We compare against five approaches: zkLLM (cryptographic proofs, 180s/query), TOPLOC (locality-sensitive hashing), SPEX (sampling-based proof of execution), tensor commitments, and self-consistency checking. NabaOS achieves the best cost-latency-coverage trade-off for interactive agents: 94.2% coverage at <15ms versus zkLLM's near-perfect coverage at 180,000ms. For interactive agents, practical receipt-based verification provides better cost-benefit than cryptographic proofs, and epistemic classification gives users actionable trust signals rather than binary judgments.
This Systematization of Knowledge (SoK) provides a comprehensive historical analysis of Maximal Extractable Value (MEV) in blockchain systems, tracing its conceptual evolution through three distinct eras. We organize the fragmented literature on MEV into a unified chronological framework, beginning with Era~I (August 2014 - August 2020), which introduced Miner Extractable Value from pmcgoohan's seminal Reddit warning through the ``Dark Forest'' recognition, covering Proof-of-Work systems with public mempools and Priority Gas Auctions. Era~II (August 2020 - April 2024) marks the generalization to Maximal Extractable Value, encompassing formal taxonomies, Realized Extractable Value, Proposer-Builder Separation, the Ethereum Merge, MEV-Boost, and the integration of non-atomic and CEX-DEX arbitrage. Era~III (April 2024, present) addresses the frontier of Cross-Chain MEV, beginning with early studies on Layer-2 ecosystems, where value extraction spans multiple blockchains, rollups, bridges, and sequencers. We present a conceptual taxonomy distinguishing potential from realized extractable value, and single-domain from cross-domain phenomena. Our systematization identifies mitigations that emerged in response to each era, highlights measurement challenges, and proposes a research agenda for standardized metrics, detection benchmarks, and cross-chain infrastructure design.
Transaction processing systems underpin modern commerce, finance, and critical infrastructure, yet their security has never been studied across the full evolutionary arc of these systems. Over five decades, transaction processing has progressed through four distinct generations, from centralized databases, to distributed databases, to blockchain and distributed ledger technologies (DLTs), finally to multi-context systems that span cyber-physical components under real-time constraints. Each generation has introduced new transaction types and new classes of vulnerabilities, yet security research remains fragmented by domain, and the foundational ACID transaction model has not been revisited to reflect the demands of contemporary systems. We classify 163 papers on transaction security by evolutionary generation, security focus, and relevant Common Weakness Enumeration (CWE) entries, and distill a curated set of 41 high-impact or seminal papers spanning all four generations. We make three principal contributions. First, we develop a four-generation evolutionary taxonomy that contextualizes each work within the broader trajectory of transaction processing. Second, we map each paper's security focus to CWE identifiers, providing a systems-oriented vocabulary for analyzing transaction-specific threats across otherwise siloed domains. Third, we demonstrate that the classical ACID properties are insufficient for modern transactional systems and introduce RANCID, extending ACID with Real-timeness (R) and N-many Contexts (N), as a property set for reasoning about the security and correctness of systems that must coordinate across heterogeneous contexts under timing constraints. Our systematization exposes a pronounced bias toward DLT security research at the expense of broader transactional security and identifies concrete open problems for the next generation of transaction processing systems.
Sushanth Ambati, Kainat Adeel, Jack Myers, Nikolay Ivanov
Self-Sovereign Digital Identity (SSDI) enables individuals to control their own identity assertions and data, rather than relying on centralized or federated systems prone to large-scale data breaches. By eliminating centralized databases maintained by service providers and identity brokers, SSDIs offer enhanced security and privacy. However, adoption remains slow, and research in this area lacks systematization and uniformity. To address these gaps, we present a comprehensive systematization of knowledge on self-sovereign digital identities, with a primary focus on identifying the challenges that impede real-world adoption. We survey 80 academic and non-academic sources and identify six major challenges: (i) binding a single identity to one individual or organization, (ii) the absence of mature cryptographic and communication protocols, (iii) significant usability barriers, (iv) regulatory and oversight gaps, (v) bootstrapping to critical-mass adoption, and (vi) dependence on a permissionless, decentralized, yet singular infrastructure that may expose unforeseen vulnerabilities over time. We then analyze 47 scientific publications and find that the vast majority focus on blockchain-based solutions rather than generalized SSDI architectures. Additionally, we catalog 12 real-world, production-grade SSDI applications. Our evaluation of these solutions reveals that self-sovereignty is, in practice, a spectrum rather than a binary property. Finally, we explore the frontiers of SSDI by identifying major trends, open problems, and opportunities for future research. We hope this systematization will help advance the shift from centralized to self-sovereign digital identities in a disciplined and impactful way.
Cross-chain token standards enable fungible tokens that exist across multiple blockchains with a unified total supply model. This paper presents a comprehensive comparative analysis of five leading cross-chain token standards and frameworks: the xERC20 standard (implementing ERC-7281), the Omnichain Fungible Token (OFT) standard, the Native Token Transfers (NTT) framework, the Cross-Chain Token (CCT) standard, and the SuperchainERC20 standard (implementing ERC-7802). We examine each standard's distinguishing properties and technical design, including architecture, message-passing mechanisms, interoperability scope, chain compatibility, and security features. Our analysis reveals that while all these standards share the goal of seamless cross-chain fungibility, they differ significantly in implementation approach, trust model, and target ecosystem.
Mahafujul Alam, Julie B. Heynssens, Bertrand Francis Cambou
In the current information age, asymmetrical cryptography is widely used to protect information and financial transactions such as cryptocurrencies. The loss of private keys can have catastrophic consequences; therefore, effective MFA schemes are needed. In this paper, we focus on generating ephemeral keys to protect private keys. We propose a novel bit-truncation method in which the most significant bits (MSBs) of response values derived from facial features in a template-less biometric scheme are removed, significantly improving both accuracy and security. A statistical analysis is presented to optimize an MFA comprising at least three factors: template-less biometrics, an SRAM PUF-based token, and passwords. The results show a reduction in both false-reject and false-acceptance rates, and the generation of error-free ephemeral keys.
The Ethereum ecosystem, which secures over $381 billion in assets, fundamentally relies on client APIs as the sole interface between users and the blockchain. However, these critical APIs suffer from widespread implementation inconsistencies, which can lead to financial discrepancies, degraded user experiences, and threats to network reliability. Despite this criticality, existing testing approaches remain manual and incomplete: they require extensive domain expertise, struggle to keep pace with Ethereum’s rapid evolution, and fail to distinguish genuine bugs from acceptable implementation variations. We present APIDiffer , the first specification-guided differential testing framework designed to automatically detect API inconsistencies across Ethereum’s diverse client ecosystem. APIDiffer transforms API specifications into comprehensive test suites through two key innovations: (1) specification-guided test input generation that creates both syntactically valid and invalid requests enriched with real-time blockchain data, and (2) specification-aware false positive filtering that leverages large language models to distinguish genuine bugs from acceptable variations. Our evaluation across all 11 major Ethereum clients reveals the pervasiveness of API bugs in production systems. APIDiffer uncovered 72 bugs, with 90.28% already confirmed or fixed by developers, including one critical error in the official specifications themselves. Beyond these raw numbers, APIDiffer achieves up to 89.67% higher code coverage than existing tools and reduces false positive rates by 37.38%. The Ethereum community’s response validates our impact: developers have integrated our test cases, expressed interest in adopting our methodology, and escalated one bug to the official Ethereum Project Management meeting. By making APIDiffer open-source, we enable continuous validation of Ethereum client API implementations, thereby strengthening the foundational integrity of the entire Ethereum ecosystem.
Justin Wang, Andreas Bigger, Xiaohai Xu, Jiahao Lin · 8 authors
Smart contracts on public blockchains now manage large amounts of value, and vulnerabilities in these systems can lead to substantial losses. As AI agents become more capable at reading, writing, and running code, it is natural to ask how well they can already navigate this landscape, both in ways that improve security and in ways that might increase risk. We introduce EVMbench, an evaluation that measures the ability of agents to detect, patch, and exploit smart contract vulnerabilities. EVMbench draws on 117 curated vulnerabilities from 40 repositories and, in the most realistic setting, uses programmatic grading based on tests and blockchain state under a local Ethereum execution environment. We evaluate a range of frontier agents and find that they are capable of discovering and exploiting vulnerabilities end-to-end against live blockchain instances. We release code, tasks, and tooling to support continued measurement of these capabilities and future work on security.
Traditional financial institutions face inefficiencies that can be addressed by distributed ledger technology. However, a primary barrier to adoption is the privacy concerns surrounding publicly available transaction data. Existing private protocols for distributed ledger that focus on the Ring-CT model are not suitable for adoption for financial institutions. We propose a post-quantum, lattice-based transaction scheme for encrypted ledgers which better aligns with institutions' requirements for confidentiality and audit-ability. The construction leverages various zero-knowledge proof techniques, and introduces a new method for equating two commitment messages, without the capability to open one of the commitment during the re-commitment. Subsequently, we build a publicly verifiable transaction scheme that is efficient for single or multi-assets, by introducing a new compact range-proof. We then provide a security analysis of it. The techniques used and the proofs constructed could be of independent interest.
Central Bank Digital Currency (CBDCs) are becoming a new digital financial tool aimed at financial inclusion, increased monetary stability, and improved efficiency of payment systems, as they are issued by central banks. One of the most important aspects is that the CBDC must offer secure offline payment methods to users, allowing them to retain cash-like access without violating Anti-Money Laundering and Counter-terrorism Financing (AML/CFT) rules. The offline CBDC ecosystems will provide financial inclusion, empower underserved communities, and ensure equitable access to digital payments, even in connectivity-poor remote locations. With the rapid growth of Internet of Things (IoT) devices in our everyday lives, they are capable of performing secure digital transactions. Integrating offline CBDC payment with IoT devices enables seamless, automated payment without internet connectivity. However, IoT devices face special challenges due to their resource-constrained nature. This makes it difficult to include features such as double-spending prevention, privacy preservation, low-computation operation, and digital identity management. The work proposes a privacy-preserving offline CBDC model with integrated secure elements (SEs), zero-knowledge proofs (ZKPs), and intermittent synchronisation to conduct offline payments on IoT hardware. The proposed model is based on recent improvements in offline CBDC prototypes, regulations and cryptographic design choices such as hybrid architecture that involves using combination of online and offline payment in IoT devices using secure hardware with lightweight zero-knowledge proof cryptographic algorithm.
The increasingly complex Web3 ecosystem and decentralized finance (DeFi) landscape demand ever higher levels of technical expertise and financial literacy from participants. The Intent-Centric paradigm in DeFi has thus emerged in response, which allows users to focus on their trading intents rather than the underlying execution details. However, existing approaches, including Typed-intent design and LLM-driven solver, trade off expressiveness, trust, privacy, and composability. We present OMNIINTENT, a language-runtime co-design that reconciles these requirements. OMNIINTENT introduces ICL, a domain-specific Intent-Centric Language for precise yet flexible specification of triggers, actions, and runtime constraints; a Trusted Execution Environment (TEE)-based compiler that compiles intents into signed, state-bound transactions inside an enclave; and an execution optimizer that constructs transaction dependency graphs for safe parallel batch submission and a mempool-aware feasibility checker that predicts execution outcomes. Our full-stack prototype processes diverse DeFi scenarios, achieving 89.6% intent coverage, up to 7.3x throughput speedup via parallel execution, and feasibility-prediction accuracy up to 99.2% with low latency.
Blockchains are diverse in the way they handle communications between their nodes to disseminate information, mitigate attacks, and agree on the next block. While security vulnerabilities have been identified, they rely on an attack custom-made for a specific blockchain communication protocol. To our knowledge, the vulnerabilities of multiple blockchain communication protocols to adversarial conditions have never been compared. In this paper, we compare empirically the vulnerabilities of the communication protocols of five modern in-production blockchains, Algorand, Aptos, Avalanche, Redbelly and Solana, when attacked in five different ways. We conclude that Algorand is vulnerable to packet loss attacks, Aptos is vulnerable to targeted load attacks and leader isolation attacks, Avalanche is vulnerable to transient failure attacks, Redbelly's performance is impacted by packet loss attacks and Solana is vulnerable to stopping attacks and leader isolation attacks. Our system is open source.
Federated Learning (FL) enables collaborative training of medical AI models across hospitals without centralizing patient data. However, the exchange of model updates exposes critical vulnerabilities: gradient inversion attacks can reconstruct patient information, Byzantine clients can poison the global model, and the \emph{Harvest Now, Decrypt Later} (HNDL) threat renders today's encrypted traffic vulnerable to future quantum adversaries.We introduce \textbf{ZKFL-PQ} (\emph{Zero-Knowledge Federated Learning, Post-Quantum}), a three-tiered cryptographic protocol that hybridizes (i) ML-KEM (FIPS~203) for quantum-resistant key encapsulation, (ii) lattice-based Zero-Knowledge Proofs for verifiable \emph{norm-constrained} gradient integrity, and (iii) BFV homomorphic encryption for privacy-preserving aggregation. We formalize the security model and prove correctness and zero-knowledge properties under the Module-LWE, Ring-LWE, and SIS assumptions \emph{in the classical random oracle model}. We evaluate ZKFL-PQ on synthetic medical imaging data across 5 federated clients over 10 training rounds. Our protocol achieves \textbf{100\% rejection of norm-violating updates} while maintaining model accuracy at 100\%, compared to a catastrophic drop to 23\% under standard FL. The computational overhead (factor $\sim$20$\times$) is analyzed and shown to be compatible with clinical research workflows operating on daily or weekly training cycles. We emphasize that the current defense guarantees rejection of large-norm malicious updates; robustness against subtle low-norm or directional poisoning remains future work.
Phat T. Tran-Truong, Vinh X. Q. Nguyen, Ha X. Son, Phien Nguyen-Ngoc · 6 authors
The proliferation of IoT and V2X systems generates unprecedented sensitive data at the network edge, demanding privacy-preserving architectures that enable secure sharing without exposing raw information. Contemporary solutions face a fundamental privacy-efficiency-trust trilemma: achieving strong privacy guarantees, computational efficiency for resource-constrained devices, and decentralized trust simultaneously remains intractable with single-paradigm approaches. This survey systematically analyzes 75 technical papers (2007--2025) through a novel three-dimensional taxonomy classifying architectures into Decentralized Computation, Cryptography-based, and Distributed Ledger approaches. Temporal analysis reveals dramatic acceleration during 2024--2025, with 48% of all papers published in this period -- Decentralized Computation dominates at 44% of contributions and 59% of 2025 publications. Comprehensive Security Threat Mapping and Technology Maturity Assessment demonstrate that mature solutions occupy narrow design regions excelling in one or two dimensions while compromising others, conclusively validating the trilemma hypothesis. We identify emerging hybrid architectures combining complementary paradigms as the essential path forward. Critical challenges including security guarantee composition across layers, multi-layer coordination overhead minimization, and post-quantum security integration must be addressed for practical deployment in next-generation intelligent transportation systems and IoT ecosystems.
In this paper, we analyze the finality of the Filecoin network, focusing on dynamic probabilistic guarantees of tipset permanence in the canonical chain. Our approach differs from static analyses that consider only the worst-case scenario; instead, we dynamically compute the error probability at each round using the live chain history, providing a more accurate and efficient assessment. We provide a practical algorithm that only requires visibility into the blocks produced by honest participants, which can be implemented by clients or off-chain applications without any change to Filecoin's consensus mechanisms.We demonstrate that, under typical operating conditions, the sought-after error probability of $2^{-30}$ is achievable in approximately 30 rounds, a 30x improvement over the 900 rounds that the network currently encodes as a fixed threshold. This finding immediately expedites transactions and enhances usability of the Filecoin network, while laying the foundation for further analysis of other DAG-structured blockchains.
Yue Li, Lei Wang, Kaixuan Wang, Zhiqiang Yang · 7 authors
The rapid proliferation of autonomous AI agents is driving a shift toward agentic commerce, where agents are expected to autonomously invoke and pay for services. While blockchain-based payments offer a programmable foundation for such interactions, the recently proposed x402 standard fails to enforce end-to-end atomicity across service execution, payment, and result delivery. In this paper, we present A402, a trust-minimized payment architecture that securely binds cryptocurrency payments to service execution. A402 introduces Atomic Service Channels (ASCs), a new channel protocol that integrates service execution into payment channels, enabling real-time, high-frequency micropayments for agentic commerce. Within each ASC, A402 employs an atomic exchange protocol based on TEE-assisted adaptor signatures, ensuring that payments are finalized if and only if the requested service is correctly executed and the corresponding result is delivered. To further ensure privacy, A402 incorporates a TEE-based Liquidity Vault that privately manages the lifecycle of ASCs and aggregates their settlements into a single on-chain transaction, revealing only aggregated balances. We implement A402 and evaluate it against x402 with integrations on both Bitcoin and Ethereum. Our results show that A402 delivers orders-of-magnitude performance and on-chain cost improvements over x402 while providing trust-minimized security guarantees.
Smart contracts underpin high-value ecosystems such as decentralized finance (DeFi), yet recurring vulnerabilities continue to cause losses worth billions of dollars. Although numerous security analyzers that detect such flaws exist, real-world attacks remain frequent, raising the question of whether these tools are truly effective or simply under-used due to low developer trust. Prior benchmarks have evaluated analyzers on synthetic or vulnerable-only contract datasets, limiting their ability to measure false positives, false negatives, and usability factors that drive adoption. To close this gap, we present a mixed-methods study that combines large-scale benchmarking with practitioner insights. We evaluate six widely used analyzers (i.e., Confuzzius, Dlva, Mythril, Osiris, Oyente, and Slither) on 653 real-world smart contracts that cover three high-impact vulnerability classes from the OWASP Smart Contract Top Ten (i.e., reentrancy, suicidal contract termination, and integer arithmetic errors). Our results show substantial variation in accuracy (F1 = 31.2 to 94.6%), high false-positive rates (up to 32.6%), and runtimes exceeding 700 seconds per contract. We then survey 150 professional developers and auditors to understand how they use and perceive these tools. Our findings reveal that excessive false positives, vague explanations, and long analysis times are the main barriers to trust and adoption in practice. By linking measurable performance gaps to developer perceptions, we provide concrete recommendations for improving the precision, explainability, and usability of smart-contract security analyzers.
Decentralized financial platforms rely heavily on Web of Trust reputation systems to mitigate counterparty risk in the absence of centralized identity verification. However, these pseudonymous networks are inherently vulnerable to adversarial behaviors, such as Sybil attacks and camouflaged fraud, where malicious actors cultivate artificial reputations before executing exit scams. Traditional anomaly detection in this domain faces two critical limitations. First, reliance on naive statistical heuristics (e.g., flagging the lowest 5% of rated users) fails to distinguish between victims of bad-mouthing attacks and actual fraudsters. Second, standard Graph Neural Networks (GNNs) operate on the assumption of homophily and cannot effectively process the semantic inversion inherent in signed (trust vs. distrust) and directed (status) edges. We propose TAS-GNN (Topology-Aware Signed Graph Neural Network), a novel framework designed for feature-sparse signed networks like Bitcoin-Alpha. TAS-GNN integrates recursive Web-of-Trust labeling and a dual-channel message-passing architecture that separately models trust and distrust signals, fused through a Status-Aware Attention mechanism. Experiments demonstrate that TAS-GNN achieves state-of-the-art performance, significantly outperforming existing signed GNN baselines.
Yao Wu, Ziye Jia, Jingjing Zhao, Haoyang Wang · 6 authors
Unmanned aerial vehicle (UAV) networks are increasingly deployed for complex missions, including disaster response, intelligent logistics, and environmental monitoring. These missions generally require coordinated collaboration among multiple UAVs across distinct administrative domains. To support such cross-domain cooperation, service function chains (SFCs) are constructed, where complex workflows are decomposed into ordered service functions assigned to appropriate UAVs along the mission path. However, it is challenging to ensure secure, trustworthy, and low-latency cross-domain SFC orchestration in identity management, authentication, and resilience to node failures. To address these issues, this paper proposes a consortium blockchain-based trust architecture for cross-domain decentralized identity verification, auditable task execution, and dynamic service-aware orchestrator selection. The framework employs a hierarchical four-phase cross-domain authentication protocol covering the credential pre-verification, intra-domain execution, secure relay, and audit logging. The use case analysis confirms that the proposed framework achieves substantial reductions in authentication latency and significant improvements in system throughput against centralized and static schemes. The open challenges in scalability, adaptive trust assessment, interoperability, and energy efficiency are discussed, thereby providing directions for future researches on secure and efficient cross-domain UAV service orchestration.
Job-based smishing scams, where victims are recruited under the guise of remote job opportunities, represent a rapidly growing and understudied threat within the broader landscape of online fraud. In this paper, we present Anansi, the first scalable, end-to-end measurement pipeline designed to systematically engage with, analyze, and characterize job scams in the wild. Anansi combines large language models (LLMs), automated browser agents, and infrastructure fingerprinting tools to collect over 29,000 scam messages, interact with more than 1900 scammers, and extract behavioral, financial, and infrastructural signals at scale. We detail the operational workflows of scammers, uncover extensive reuse of message templates, domains, and cryptocurrency wallets, and identify the social engineering tactics used to defraud victims. Our analysis reveals millions of dollars in cryptocurrency losses, highlighting the use of deceptive techniques such as domain fronting and impersonation of well-known brands. Anansi demonstrates the feasibility and value of automating the engagement with scammers and the analysis of infrastructure, offering a new methodological foundation for studying large-scale fraud ecosystems.
Shuang Liang, Yang Hua, Linshan Jiang, Peishen Yan · 7 authors
In open Federated Learning (FL) environments where no central authority exists, ensuring collaboration fairness relies on decentralized reward settlement, yet the prohibitive cost of permissionless blockchains directly clashes with the high-frequency, iterative nature of model training. Existing solutions either compromise decentralization or suffer from scalability bottlenecks due to linear on-chain costs. To address this, we present SettleFL, a trustless and scalable reward settlement protocol designed to minimize total economic friction by offering a family of two interoperable protocols. Leveraging a shared domain-specific circuit architecture, SettleFL offers two interoperable strategies: (1) a Commit-and-Challenge variant that minimizes on-chain costs via optimistic execution and dispute-driven arbitration, and (2) a Commit-with-Proof variant that guarantees instant finality through per-round validity proofs. This design allows the protocol to flexibly adapt to varying latency and cost constraints while enforcing rational robustness without trusted coordination. We conduct extensive experiments combining real FL workloads and controlled simulations. Results show that SettleFL remains practical when scaling to 800 participants, achieving substantially lower gas cost.
Process attestation verifies human authorship by collecting behavioral biometric evidence, including keystroke dynamics, typing patterns, and editing behavior, during the creative process. However, the very data needed to prove authenticity can reveal intimate details about an author's cognitive state, health conditions, and identity, constituting sensitive biometric data under GDPR Article 9. We resolve this privacy-attestation paradox using zero-knowledge proofs. We present ZK-PoP, a construction that allows a verifier to confirm that (a) sequential work function chains were computed correctly, (b) behavioral feature vectors fall within human population distributions, and (c) content evolution is consistent with incremental human editing, all without learning the underlying behavioral data, exact timing, or intermediate content. Our construction uses Groth16 proofs over arithmetic circuits with Pedersen commitments and Bulletproof range proofs. We prove that ZK-PoP is computationally zero-knowledge, computationally sound, and achieves unlinkability across sessions. Evaluation shows proof generation in under 30 seconds for a 1-hour writing session, with 192-byte proofs verifiable in 8.2 ms, while incurring less than 5% accuracy loss in simulation at practical privacy levels (epsilon >= 1.0) compared to non-private baselines.