Blockchain Papers

Yuxin Xia, Ziyang Ji, Jie Zhang, Wanxin Li · 7 authors

Abstract Non-Fungible Token (NFT) creators use digital signatures to ensure the ownership, authenticity, integrity, and nonrepudiation of their digital works. However, if the private key is compromised, an attacker can generate unauthorized NFTs by using the creator’s private key to issue valid signatures. These valid but unauthorized signatures will be accepted in the NFT market and cannot be revoked. Even if the NFT creators update their private-public key pairs, they cannot deny the NFTs generated by the attacker. To mitigate these risks, we propose revocable signature by introducing commitment mechanism and an Auxiliary Embedded Key ( AEK ) into the signature, while the regular verification process does not involve this AEK . If a valid but unauthorized signature is detected and needs to be revoked, AEK will be disclosed to perform the revocation operation. To illustrate the application of revocable signatures in NFT, we design and implement a revocable Elliptic Curve Digital Signature Algorithm (ECDSA) scheme with provable security. Experimental evaluations on the FIPS-recommended elliptic curves show that the performance of revocable ECDSA is comparable to the basic ECDSA, with additional 0.0303 s (P-256 curve) and 0.15 USD gas fee in Remix VM for revoking a signature.

STEFANUS SAPUTRA

pada level bytecode.Penelitian ini mengimplementasikan metode deteksi malicious smart contract berbasis opcode menggunakan Graph Neural Network (GNN) dengan representasi Control Flow Graph (CFG) pada dataset Forta Network yang terdiri dari 139.600 kontrak dengan rasio ketidakseimbangan kelas 936:1 (149 malicious berbanding 139.451 benign).Setiap smart contract direpresentasikan sebagai CFG di mana basic block menjadi node berfitur 22 dimensi yang terdiri dari frekuensi 14 sensitive opcode dan representasi one-hot tipe instruksi exit, sedangkan hubungan antar blok direpresentasikan sebagai edge dengan lima kategori.Graf yang

Krishna Bajpai

No abstract is available for this record.

Raghavendra Sai Akkinapragada

This paper presents the post-quantum integrity architecture designed and being implemented within Smart Astro among the rst known platforms delivering Deterministic Engine Computed, AI-Narrated, Blockchain-Veried life guidance at scale. Smart Astro operates across 18 active life-decision intents and over 40 auspicious-timing categories, serving users globally through a real-time, pay-per-question model. Classical asymmetric cryptography underpinning current blockchain infrastructure is vulnerable to Shor's algorithm on fault-tolerant quantum computers. Smart Astro addresses this proactively by integrating NIST-standardised post-quantum cryptographic primitives FIPS 203 (ML-KEM), FIPS 204 (ML-DSA), and FIPS 205 (SLH-DSA) alongside a Solana-anchored proof chain already live in production. The central architectural contribution is a layered separation of concerns: the deterministic engine computes a structured, repeatable output; cryptographic proof generation runs asynchronously outside the delivery path; the AI narration layer is isolated from the proof chain; and only a commitment hash is written to the Solana ledger, with the full postquantum signature bundle stored o-chain. Every paid answer carries an independently veriable SA-PROOF identier with zero personally identiable information (PII) on-chain. A hybrid ML-DSA + SLH-DSA signature scheme provides defence in depth against both lattice cryptanalysis and harvest-now-decrypt-later adversaries. This architecture establishes a replicable standard for cryptographic integrity in AI-narrated deterministic knowledge systems.

Yihang Wu

As Generative AI (AIGC) achieves pixel-level realism and real-time interaction; traditional digital authentication paradigms face an unprecedented "authenticity crisis". Software-level defenses and static biometrics are increasingly vulnerable to high-fidelity AI-generated threats. This paper proposes a novel Human-Source Authentication architecture, termed Testing, Inspection, and Certification (TIC), which shifts the verification focus from logical correctness to the real-time presence of a physical entity. At the core of TIC is the Pulse-Eye Handshake (PEH) protocol, which integrates hardware-level Physical Unclonable Functions (PUF) with the physiological dynamics of the human pupillary light reflex (PLR). By leveraging a "Hardware Trinity" comprising smart glasses, a smartwatch, and a smart ring, the protocol constructs a deterministic "Shield of Time". This shield exploits the significant latency gap between the computational overhead of AI inference (typically >300ms) and the intrinsic physical limits of the human autonomic nervous system (8.3-35ms). Furthermore, architecture employs recursive zero-knowledge proof (ZKP) based on the Nova protocol to ensure multi-modal verification without compromising user privacy. Security analysis demonstrates that the proposed system remains robust against sophisticated modeling attacks and AI-driven acceleration. Our work provides a foundational anchor for digital sovereignty and system resilience in the era of pervasive AI and Industry 5.0.

Nikita Tiwari, Pradeep Kumar Biswal, Prakash Ranjan

The rapid development of the Internet of Medical Things (IoMT) has also facilitated real-time monitoring of healthcare, yet creates major issues of security, privacy, and interoperability, particularly in terms of emerging threats of quantum computing. This paper introduces a quantum-resilient blockchain paradigm, which combines post-quantum cryptography (PQC), Zero Knowledge Proofs (ZKPs), and Fast Healthcare Interoperability Resources (FHIR) into secure and interoperable healthcare data management. Key encapsulation is performed using lattice-based algorithms, including Kyber and NTRU, and Dilithium and Falcon are algorithms used to secure digital signatures against quantum attacks. An authentication system which is based on a ZKP, and role-based access control allows privatizing access to electronic health records without exposing sensitive data. The framework is deployed on a PBFT-based permissioned blockchain and tested in simulated IoMT settings and has low latency, high throughput, and efficient cryptographic performance. In general, the suggested system will provide a reconfigurable, secure, and future-oriented method to safeguard the healthcare information against quantum threats without compromising the interoperability of the heterogeneous systems.

Hyeonbum Lee, Seunghun Paik, Hyunjung Son, Jae Hong Seo

An inner product argument (IPA) is a cryptographic proof system that serves as a fundamental building block for various applications, such as zero knowledge proofs and verifiable computation. Bulletproofs (IEEE S&P 2018), a well-known IPA under the discrete logarithm (DL) assumption, features a short, logarithmically-sized proof, making it suitable for blockchain applications. However, its major drawback is the linear verifier cost (O(N)), which presents a significant bottleneck in settings like verifiable computation. To address this, recent advancements have successfully reduced the verification complexity to square-root order (O(√N)) under the same assumption (e.g., Asiacrypt 2022, IEEE TIFS). In thiswork, we propose Cougar, a novel IPAthat breaks this square-root barrier to achieve an unprecedented cubic-root verifier complexity (O(3√N)), while strictly maintaining the compact logarithmic proof size (O(logN)) characteristic of Bulletproofs. To achieve this, Cougar introduces a generalized two-tier commitment framework combined with adisjoint interpolationstrategy for efficient consistency checks. We implemented Cougar in Rust and performed a comprehensive benchmarking against Bulletproofs and Leopard (IEEE TIFS). Our evaluation demonstrates that while Cougar incurs a moderate increase in prover overhead, its verification time scales significantly better for large instances. Concretely, for a witness size ofN= 220, Cougar achieves a 50× verification speed-up over Bulletproofs and exhibits a superior asymptotic growth rate compared to existing sublinear IPAs.

Oleksandr Kuznetsov, Yulia Khavikova, Valerii Bushkov, Dmytro Shchytov · 5 authors

Zero-knowledge succinct non-interactive arguments of knowledge (zk-SNARKs) have emerged as a critical technology for privacy-preserving computation and blockchain applications. However, systematic performance analysis of practical implementations remains limited, hindering informed technology adoption decisions. This study presents a comprehensive benchmarking analysis of the Groth16 protocol implementation using the widely-adopted Circom-snarkjs framework. We developed an automated benchmarking platform that systematically measures performance across seven representative circuit types with varying computational complexity (1-11 R1CS constraints). Our methodology ensures reproducible measurements through controlled experimental design with statistical validation. The platform captures detailed metrics for all three phases of the Groth16 protocol: witness generation, proof creation, and verification. Results from 35 independent measurements reveal several important findings. Witness generation demonstrates consistent performance across circuit types, averaging 57.6±12.1 milliseconds. Proof generation times range from 832 to 1,147 milliseconds, showing non-linear scaling with circuit complexity. Verification times remain relatively stable (741-884 milliseconds), confirming Groth16's theoretical constant-time verification advantage. All measurements achieved 100% success rate with complete proof validation. Notably, circuit structure significantly impacts performance beyond simple constraint counting. Comparison-based circuits achieve 13.22 constraints per second efficiency, substantially outperforming arithmetic circuits (1.02-4.36 constraints/second). This finding provides actionable guidance for circuit design optimization. The study contributes an open-source benchmarking framework for reproducible zk-SNARK research and provides empirical performance data for technology adoption decisions. Our findings support the practical deployment of Groth16 for applications requiring efficient zero-knowledge proofs while highlighting optimization opportunities for circuit designers.

Y.Y.N. Li

We introduce behavior-bound signatures (BBS), a signature framework in which each signature attests not only to signer authenticity but also to the satisfaction of a prescribed behavioral policy. Unlike traditional digital signatures—whose acceptance is determined by identity validity alone—BBS enforces compliance at the level of the verification predicate: a signature is accepted if and only if a zero-knowledge proof establishes that a residual function value δ(x)=∣ϕ(x)−τ∣\delta(x)=|\phi(x)-\tau|δ(x)=∣ϕ(x)−τ∣ lies below a threshold ε\varepsilonε. Thus, compliance safety is reduced to zero-knowledge soundness rather than to external monitoring or honest-majority assumptions. We formalize policy-soundness under chosen-message attacks (PS-CMA), extending EUF-CMA by requiring that no adversary can produce a valid signature for any message whose induced action violates the policy predicate. We prove that BBS achieves PS-CMA security under standard assumptions: binding of Pedersen commitments, collision resistance of Poseidon, and soundness of the underlying zero-knowledge proof system (e.g., Bulletproofs or PLONK). Our construction instantiates the policy predicate via a private structure function ϕ(x)\phi(x)ϕ(x) and enforces δ(x)<ε\delta(x)<\varepsilonδ(x)<ε through a zero-knowledge range constraint, while revealing no information about the private parameters. Sequential signatures compose into a hash-linked trajectory, enabling verifiable ordering and completeness of action sequences. We additionally define the Function Approximation Inversion Problem (FAIP) as a conjectured hardness property of the structure function, and emphasize that the security of BBS does not rely on this conjecture.

Rodica Doina Zmaranda, Attila-Imre Kovacs, Daniela Elena Popescu, Alexandrina Mirela Pater

Securing electronic health records (EHR) requires strong guarantees for confidentiality, integrity, access control, and auditability. Traditional centralized architectures rely on database-level protection and internal logging, which remain vulnerable to insider misuse and undetected data modification. This study proposes a practical hybrid architecture in which medical content is stored encrypted off-chain, while blockchain is used selectively as a governance and evidence layer. An Ethereum-based prototype was designed and implemented to support integrity anchoring of medical documents, patient-controlled consent management, and immutable audit trails for critical actions. In the implemented solution, the actual medical content is not stored on-chain. Instead, the blockchain stores only document-related metadata, cryptographic hashes, document references, and access-control information, while the sensitive medical data remains encrypted and stored off-chain. This design supports GDPR-oriented data minimization, since the immutable blockchain layer does not contain raw medical records or directly identifiable medical content. The prototype separates confidentiality from blockchain immutability. Medical document confidentiality is handled at the application and off-chain storage level, while the blockchain is used for integrity verification, consent management, and auditability. Encryption keys are not stored on-chain, which prevents the blockchain layer from becoming a repository of sensitive or directly exploitable medical information. Security mechanisms are integrated directly into application flows, including hash-based tamper detection and on-chain verification of access rights. The prototype is evaluated through realistic operational scenarios, analyzing security properties, performance, and transaction cost implications. Results show that, relative to a DB-only baseline, the hybrid approach provides structurally stronger support for integrity verification, traceability, and accountability without exposing sensitive medical data on-chain. The study also highlights practical limitations related to latency and costs in public blockchain environments, supporting a selective on-chain design focused on high-value operations.

Fiza Siyal, Antonella Guzzo, Fahed Alkhabbas, D. Saccà · 5 authors

We propose a novel blockchain-based traceability system that uniquely combines Physical Unclonable Functions (PUFs) and Non-Fungible Tokens (NFTs) to establish secure, tamper-evident, and verifiable digital identities for physical products. Unlike conventional approaches that rely solely on serial numbers or barcodes, our system uses embedded PUFs to generate a physically unclonable ID for each item, ensuring hardware-level authenticity. This PUF ID is cryptographically linked to an NFT minted on a public blockchain, encapsulating metadata such as product origin, manufacturing details, and certification status. Associating NFTs and certifications with PUF-tagged products not only provides transparent provenance but also enables decentralised validation of compliance and ethical standards. This methodology is distinct in its integration of immutable physical identity with blockchain-based digital certification, offering a robust solution for enhancing transparency, combating counterfeiting, and fostering trust across global supply chains. Furthermore, employing Edge and emerging 6G networks, our framework can execute PUF challenge–response and preliminary NFT minting directly at edge nodes. This establishes a real-time, low-latency architecture that reduces network congestion and gas costs for secure and sustainable supply chains.

Elizaveta Pertseva, Valentin Robert, Clark Barrett, James Parker

Efforts to verify Zero-Knowledge Proof circuit encodings have highlighted the challenge of proving the correctness of quantifier-free statements that make use of both bitvector and finite field operations. Existing verification workflows are either manual or rely on SMT solvers, which scale poorly on some classes of problems for reasons that include difficulties with conversion operators and challenges reasoning about inequalities. To address these limitations, we present a novel Lean tactic BitModEq that leverages range lemmas and case analysis to produce verified translations from finite fields to bitvectors. Our approach, combined with bit-blasting, outperforms state-of-the-art SMT solvers, solving 19% more ZKP arithmetization benchmarks.

Priya Patel, Nitesh Sureja

Ensuring transparency, security, and privacy in agricultural food supply chains is critical for maintaining consumer trust, regulatory compliance, and data integrity. Traditional centralized traceability systems suffer from several limitations, including data tampering risks, single-point failures, and potential privacy leakage. To address these challenges, this research proposes a privacy-preserving blockchain-based traceability framework that integrates the InterPlanetary File System (IPFS) with Zero-Knowledge Proofs (ZKPs). The framework leverages the Ethereum blockchain for immutable record-keeping, while zk-SNARK-based proofs enable compliance verification without revealing sensitive underlying data. A prototype was implemented using Solidity smart contracts and Python-based zk-SNARK circuits. Experimental evaluation across varying record sizes, from 50 to 200, demonstrates high security and efficiency, achieving 100% success in detecting simulated tampering attempts. Performance metrics indicate a highly scalable system with an average end-to-end latency of approximately 0.33 seconds, rapid proof generation times of approximately 0.0002 seconds, and near-constant verification times averaging 0.027 seconds. Furthermore, the system maintains a consistent simulated transaction cost of 20.40$ per proof, regardless of the total records processed. Overall, the proposed approach provides a robust, scalable, and computationally efficient solution for modern agri- food supply chains, successfully balancing data confidentiality with rigorous cryptographic integrity.

nithin nagineni

Zero-Knowledge Proofs (ZKPs) have emerged as a transformative cryptographic primitive enabling one party to prove the validity of a statement without revealing any underlying information. This property has made ZKPs a cornerstone in privacypreserving systems, particularly in blockchain, authentication protocols, and secure distributed computing. Despite rapid advancements in ZKP frameworks such as zk-SNARKs and zk-STARKs, the testing and validation of these systems remain a critical challenge. The complexity of arithmetic circuits, the probabilistic nature of proofs, and the potential for subtle vulnerabilities necessitate robust testing methodologies. This paper presents a comprehensive review of testing techniques for Zero-Knowledge Proof systems. It begins by outlining the fundamental properties of ZKPs, including completeness, soundness, and zero-knowledge, which form the basis for testing correctness and security. The study then explores existing literature on testing approaches, including formal verification, fuzz testing, constraint validation, and symbolic execution. Recent research highlights that vulnerabilities such as underconstrained circuits account for a significant portion of ZKP system failures, emphasizing the need for systematic testing strategies. Furthermore, this review categorizes testing techniques into functional, security, and performance testing, providing a structured understanding of their roles in ZKP validation. A comparative analysis of existing methods is also presented to evaluate their effectiveness, scalability, and limitations. The discussion identifies gaps in current research, particularly in automated testing frameworks and standardized benchmarking. The paper concludes by emphasizing the importance of integrating advanced testing methodologies into ZKP development pipelines to ensure reliability, scalability, and security. Future research directions include AI-driven testing, hybrid verification models, and improved tooling for circuit validation. This review aims to serve as a foundational reference for researchers and practitioners working on secure and efficient ZKP systems.

Hyunhum Cho, Ik Rae Jeong

The thriving Non-Fungible Token(NFT) market, despite its innovative redefinition of digital ownership, faces malicious attacks and challenges, notably from widespread wash trading. In this paper, we examine the underexplored relationship between NFT rarity and wash trading.We present a novel approach to NFT market dynamics, by establishing the first comprehensive formal framework for NFT trait systems, including essential definitions, a robust taxonomy, precise rarity calculation, and verifiable properties. Building upon this, we conduct an empirical analysis of NFT rarity and wash trading across 30 collections including 336,133 NFTs, 764,679 transactions, total volume of 14,394,949 in Ethereum. Our findings reveal the strong inverse correlation between a collection’s price-rarity coefficient and its wash trading volume, showing that the top 5 collections with the lowest price-rarity coefficients are overwhelmingly dominated by wash trading, averaging 89.29% of their total volume, in contrast to the vast majority of other collections which exhibited negligible volumes, consistently below 2%. Case studies further highlight the power of this price-rarity analysis as a novel anomaly detection tool: it exposed wash trading-induced distortions in CryptoPunks by detecting an outlier previously undetectable by existing graph-based approaches. We also demonstrate Rektguy’s remarkable resilience—absorbing 20.04% wash volume possibly due to strong rarity-price correlation. Our findings establish rarity as an intrinsic resilience factor against manipulation, fundamentally reshaping approaches to NFT market analysis and robust anomaly detection.

Chibuzor Udokwu, Seyed Amid Moeinzadeh Mirhosseini, Stefan Craß

Smart contracts are deployed and represented as bytecodes in blockchain networks, and these bytecodes are machine-readable codes. Only a small number of deployed smart contracts have their verified human-readable code publicly accessible to blockchain users. To improve the understandability of deployed smart contracts, we explored rule-based classification of smart contracts using iterative integration of fingerprints of relevant function interfaces and keywords. Our classification system included categories for standard contracts such as ERC20, ERC721, and ERC1155, and non-standard contracts like FinDApps, cross-chain, governance, and proxy. To do this, we first identified the core function fingerprints for all ERC token contracts. We then used an adapted header extractor tool to verify that these fingerprints occurred in all of the implemented functions within the bytecode. For the non-standard contracts, we took an iterative approach, identifying contract interfaces and relevant fingerprints for each specific category. To classify these contracts, we created a rule that required at least two occurrences of a relevant fingerprint keyword or interface. This rule was stricter for standard contracts: the 100% occurrence requirement ensures that we only identify compliant token contracts. For non-standard contracts, we required a minimum of two relevant fingerprint occurrences to prevent hash collisions and the unintentional use of keywords. After developing the classifier, we evaluated its performance on sample datasets. The classifier performed very well, achieving an F1 score of over 99% for standard contracts and a solid 93% for non-standard contracts. We also conducted a risk analysis to identify potential vulnerabilities that could reduce the classifier’s performance, including hash collisions, an incomplete rule set, manual verification bottlenecks, outdated data, and semantic misdirection or obfuscation of smart contract functions. To address these risks, we proposed several solutions: continuous monitoring, continuous data crawling, and extended rule refinement. The classifier’s modular design allows for these manual updates to be easily integrated. While semantic-based risks cannot be completely eliminated, symbolic execution can be used to verify the expected behavior of ERC token contract functions with a given set of inputs to identify malicious contracts. Lastly, we applied the classifier on contracts deployed Ethereum main network.

Chibuzor Udokwu, Stefan Craß

Digital product passports outline information about a product’s lifecycle, circularity, and sustainability-related data. Sustainability data contains claims about carbon footprint, recycled material composition, ethical sourcing of production materials, etc. Also, upcoming regulatory directives require companies to disclose this type of information. However, current sustainability reporting practices face challenges, such as greenwashing, where companies make incorrect claims that are difficult to verify. There is also a challenge of disclosing sensitive production information when other stakeholders, such as consumers or other economic operators, wish to verify sustainability claims independently. Zero-knowledge proofs (ZKPs) provide a cryptographic system for verifying statements without revealing sensitive information. The goal of this research paper is to explore ZKP cryptography, trust models, and implementation concepts for extending DPP capability in privacy-aware reporting and verification of sustainability claims in products. To achieve this goal, first, formal representations of sustainability claims are provided. Then, a data matrix and trust model for generating proofs are developed. An interaction sequence is provided to show different components for various proof generation and verification scenarios for sustainability claims. Lastly, the paper provides a circuit template for the proof generation of an example claim and a credential structure for their input data validation. The proposed approach is assessed using a scenario-based evaluation to check the performance metrics for data credential verification and proof generation for verifying material composition in a product.

Dimitrios Kasimatis, Ilias Politis, Nikolaos Pitropakis, Pavlos Papadopoulos · 5 authors

The rapid proliferation of Internet of Things (IoT) devices across various industries, including healthcare, smart cities, and industrial automation, has introduced significant security, authenticity, and traceability challenges within increasingly complex supply chains. Although existing approaches have utilised blockchain-based digital identity solutions to address some of these concerns, persistent issues of counterfeit products and inadequate lifecycle transparency highlight the need for more robust, hardware-anchored identification mechanisms. Our work presents a novel architecture that integrates Physically Unclonable Functions (PUFs) and blockchain-based Soulbound Tokens (SBTs) to establish secure and verifiable digital identities directly tied to the physical hardware of IoT devices. By employing cryptographic tools such as fuzzy extractors, Merkle trees, and zero-knowledge proofs, the proposed architecture ensures accurate lifecycle tracking through key operational stages, including manufacturing, procurement, provisioning, maintenance, and eventual disposal or recycling. Performance evaluations conducted on the Ethereum Sepolia testnet demonstrate reasonable computational overhead in terms of gas usage and transaction confirmation times. The findings reveal that this approach aligns with NIST Special Publication 800-161 guidelines, as well as emerging regulatory standards, notably the European Union’s Digital Product Passport initiative, and has significant implications for enhancing transparency, sustainability, and security across global IoT supply chains.

Yunbo Yang, Yu Cheng, Junkai Liang, Kailun Wang · 14 authors

Zero-knowledge Succinct Non-interactive Argument of Knowledge (zkSNARK) is a powerful cryptographic primitive that enables a prover to convince a verifier that something is true without leaking the private witness. Current zkSNARKs face significant computational costs in generating proofs, which restricts their use in areas like private payments, confidential smart contracts, and anonymous credentials. Private delegation offers a practical solution by outsourcing the heavy computation to powerful external workers without leaking any private information. In this work, we propose HyperSiniel, an efficient private delegation framework for general zkSNARKs that achieves a new feature called guaranteed output delivery (GOD). HyperSiniel is designed to be compatible with any universal zkSNARKs constructed from a polynomial interactive oracle proof (PIOP) and a polynomial commitment scheme (PCS). It enables a computationally limited delegator to outsource proof generation to several workers in a fully non-interactive and privacy-preserving manner. Compared to the most state-of-the-art frameworks (e.g., Siniel [NDSS'25]), HyperSiniel ensures that the delegator always receives a correct proof, regardless of malicious worker behavior. We implement HyperSiniel and compare the performance with Siniel across varying bandwidths and circuit sizes. Under low-bandwidth conditions (10MBps), HyperSiniel incurs only an additional 25% overhead compared with Siniel, while the total running time of HyperSiniel is almost identical to Siniel under high-bandwidth settings (1000MBps). These results show that the strong robustness guarantee of GOD in HyperSiniel comes almost for free, making it a practical and secure solution for real-world zkSNARK delegation.

Chechelnitsky, Igor

This work introduces the Adversarial Cost Model (ACM v1.0), a formal security framework unifying computational, economic, and physical attack costs in a single rational adversary model. Unlike traditional security models based purely on computational hardness, ACM evaluates real-world feasibility of attacks under post-quantum cryptography, behavioral authentication, zero-knowledge proof systems, and decentralized governance. The model formalizes adversarial actions through total cost functions combining time complexity, hardware requirements, capital liquidity, and physical laboratory constraints. Multiple critical attack classes are analyzed, including hybrid side-channel + Grover attacks, GAN-based behavioral cloning, flash-loan Sybil governance attacks, post-quantum brute-force exhaustion, and zero-knowledge proof forgery. The results demonstrate that many real-world system failures arise not from cryptographic weakness, but from mispriced economic atomicity and cost-free identity or governance acquisition. ACM provides a rationality threshold theorem formalizing when attacks become economically and physically irrational. The model directly informs secure system architecture design by enforcing multi-layer cost escalation across cryptographic, physical, behavioral, and governance layers. This work is intended for cryptography, blockchain security, adversarial machine learning, economic attack modeling, and post-quantum system design.

Chechelnitsky, Igor

This work introduces the Adversarial Cost Model (ACM v1.0), a formal security framework unifying computational, economic, and physical attack costs in a single rational adversary model. Unlike traditional security models based purely on computational hardness, ACM evaluates real-world feasibility of attacks under post-quantum cryptography, behavioral authentication, zero-knowledge proof systems, and decentralized governance. The model formalizes adversarial actions through total cost functions combining time complexity, hardware requirements, capital liquidity, and physical laboratory constraints. Multiple critical attack classes are analyzed, including hybrid side-channel + Grover attacks, GAN-based behavioral cloning, flash-loan Sybil governance attacks, post-quantum brute-force exhaustion, and zero-knowledge proof forgery. The results demonstrate that many real-world system failures arise not from cryptographic weakness, but from mispriced economic atomicity and cost-free identity or governance acquisition. ACM provides a rationality threshold theorem formalizing when attacks become economically and physically irrational. The model directly informs secure system architecture design by enforcing multi-layer cost escalation across cryptographic, physical, behavioral, and governance layers. This work is intended for cryptography, blockchain security, adversarial machine learning, economic attack modeling, and post-quantum system design.

Roe, Paul

Classical ledger systems, including proof-of-work (PoW) and proof-of-stake (PoS) blockchains;derive their security from the assumption that irreversible computation incurs a thermodynamic cost. This assumption, rooted in Landauer’s principle, implies that reversing orre-writing global state requires expenditure of significant physical energy, and therefore canbe made economically infeasible.In this paper, we introduce the RHEA–Λ Gate Family: a reversible multi–radix (2–3–5)logic primitive with a triangular, measure-preserving topology that embeds directly intoHamiltonian phase-space flows. Each gate includes an intrinsic symbolic (glyph/entropy)register enabling perfect, lossless history retention without information erasure. Whencomposed into circuits, Λ–gates form fully reversible, entropy-preserving state-transitionoperators capable of implementing arbitrary classical computations at asymptotically zeroenergy in adiabatic regimes.We show that any ledger whose security relies on computational irreversibility becomesvulnerable in a computational substrate that supports (i) strictly reversible evolution, (ii)zero–entropy symbolic memory, and (iii) multi-radix reversible hashing. In such substrates,the economic barrier that protects ledger history vanishes: all PoW functions become ther-modynamically free, PoS penalties become reversible, and Merkle-tree hashing no longerprovides unidirectional security. We formalize this result as an impossibility theorem forirreversible-cost security models, and we construct a reversible ledger architecture whose cor-rectness is maintained through Hamiltonian invariants rather than dissipative computationalcost.The Λ framework thereby provides both (a) a constructive alternative to irreversible ledgermechanisms and (b) the first proof that classical reversible computation, when extendedto higher radices with symbolic memory, nullifies the energy-based assumptions underlyingmodern blockchain security.

Geoffroy Couteau, Srinivas Devadas, Alexander Koch, Sacha Servan-Schreiber

No abstract is available for this record.

Rahees Ur Rehman, Gurjit Singh Bhathal

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.