Cybercrime investigation in India has entered a new legal phase following the commencement of the Bharatiya Nyaya Sanhita, 2023 (BNS), the Bharatiya Nagarik Suraksha Sanhita, 2023 (BNSS), and the Bharatiya Sakshya Adhiniyam, 2023 (BSA). These enactments operate alongside the Information Technology Act 2000, the Digital Personal Data Protection Act 2023 (DPDP Act), sectoral regulation and specialised cybercrime institutions. This article argues that the principal weakness of the present framework is not a lack of offences, but fragmentation across legal classification, investigative procedure, digital-evidence rules, forensic practice, privacy governance and institutional coordination. Using doctrinal legal research supplemented by official policy and institutional material, the article develops an investigation-chain framework linking complaint triage, offence classification, preservation, lawful acquisition, forensic examination, attribution, financial tracing, cross-border evidence, prosecution and adjudication. It evaluates the continuing interaction between general criminal liability under the BNS and technology-specific provisions of the Information Technology Act, while examining the evidentiary significance of electronic records under the BSA. Particular attention is given to cloud evidence, cryptocurrency, ransomware, artificial intelligence and deepfakes. The article contends that technological traceability must not be equated with human attribution and that evidentiary reliability depends on the entire acquisition-to-trial chain. It proposes harmonised investigative protocols, accredited forensic capacity, specialist prosecution and judicial training, auditable access to personal data, improved cross-border preservation mechanisms, and a human-verification requirement for significant AI-assisted investigative outputs. The analysis contributes an India-specific but internationally relevant model for assessing whether contemporary cybercrime law can produce reliable, rights-compliant and trial-ready investigations.
In the context of information technology deeply embedded in social interactions and transactional activities, online chat records have become a representative and frequently used type of electronic evidence in civil litigation. However, such evidence relies on specific technical environments and is easily edited and tampered with, leading to long-standing issues of scattered standards and unclear paths in judicial practice regarding evidence collection, examination, and evaluation of probative value. The current system still shows deficiencies in notarization preservation, judicial authentication, platform assistance obligations, and technical assistance identification, making it difficult to match the highly technological development trend of electronic evidence. Accordingly, it is possible to achieve a structural reshaping of authenticity identification rules by optimizing notarization and authentication mechanisms, clarifying the scope of assistance and procedural obligations of chat software operators, and introducing trusted technical means such as blockchain.
Restricted forensic evidence-trail consolidation. This 42-page German-language whitepaper consolidates the development, integrity, publication and custody evidence for the AUGMANITAI work complex and the thesis of language as versionable code from January through 21 August 2026. It includes an evidence-class model, a month-by-month chronology, verified Zenodo and GitHub anchors, backup and restore findings, claim limitations, a proposed work constitution, and the complete 944-entry April 2026 IP-core manifest with SHA-256 values. The record documents 943 manifest-matching primary files plus one exact original file state recovered from a fossil backup, making all 944 April byte states reconstructible. OpenTimestamps files are present but their Bitcoin confirmation remains unverified in this audit. The paper does not claim worldwide priority, patentability, peer review, empirical validation or a complete external timestamp chain since January. Three contemporaneous restricted records created in parallel on 21 August 2026 are acknowledged as separate evidence anchors: 10.5281/zenodo.22050012, 10.5281/zenodo.22050031 and 10.5281/zenodo.22050033. This record is the long-form forensic consolidation and complete manifest index. Human responsible creator and depositor: Andreas Ehstand. AI-assisted evidence search, hashing, consolidation, typesetting and deposit preparation: OpenAI Codex. Restricted access does not constitute public enabling disclosure. All rights reserved; underlying materials retain their respective licences.
The Prop Trust Verified Standard (PTVS) v1.0 Reference Architecture establishes the definitive technical specification, capability matrix, and implementation guidelines for the physical verification of tokenized Real-World Assets (RWAs) within the European regulatory framework. This document resolves the "Physical Oracle Gap" â the structural inability of Distributed Ledger Technology (DLT) systems to attest to the physical existence, structural integrity, and legal encumbrances of off-chain assets backing tokenized securities â through a deterministic four-pillar architecture: Pillar I â eIDAS 2.0 Qualified Forensic Audits: On-site inspections conducted by sworn judicial experts under Qualified Electronic Signatures (QES) per Regulation (EU) 2024/1183. Pillar II â SHA-256 Cryptographic Lineage: Canonical JSON serialization with deterministic hashing anchored in permanent registries. Pillar III â Smart Contract Circuit Breakers: The open-source PTVSClaimInjector.sol contract (MIT License) enforces automated protective actions based on PTVS Score. Pillar IV â PTCE Network: Decentralized network of Prop Trust Certified Experts with 85/15 revenue split. Institutional validation: Formal submissions to ESMA (FOI/ESMA/2026-001), EBA (FOI/EBA/2026-002), EIOPA (FOI/EIOPA/2026-003, confirmed & registered), and ECB/SSM (FOI/ECB-SSM/2026-004, ADITO portal) Application to INATBA RWA Working Group (FOI/INATBA/2026-005) Permanent registration at CERN/Zenodo, HAL/CNRS (hal-05713062v1), OSF (DOI: 10.17605/OSF.IO/7D2SJ), and U.S. Copyright Office (Cases 1-15210573311 & 1-15234961091) Open governance via the PTVS Technical Board (17 seats, W3C/ISO-inspired) Document scope: 17 pages covering architecture overview, PTVS Score methodology (0-100), Verifiable Claims lifecycle, ERC-3643/T-REX integration, regulatory alignment matrix (MiCA, Solvency II, Eurosystem, eIDAS 2.0), governance model, 20-capability prior art inventory, and comparative analysis vs. Chainlink, Proof of Reserve, IoT sensors, Big Four audits, and registry oracles. Lead Researcher: Aurelio Tamarit Blay, Certified Judicial Expert (Exp. No. 0161, Spain), ORCID: 0009-0007-5824-3602, Wikidata: Q140774713. Institutional motto: Veritas in Re ¡ Certitudo in Code Canonical source: https://forensics-oracle.org/reference-architecture/
Open access
2 source records
Blockchain Technology Applications and Security
Digital and Cyber Forensics
Physical Unclonable Functions (PUFs) and Hardware Security
Through this independent concept, the study introduces a fresh new perspective to the world of modern forensic accounting via a theory called âThe Decentralized Fraud Matrixâ (DFM). This conceptual research was developed specifically as an analytical tool to dissect the modus operandi of financial crimes in the digital-cyber eraâincluding Web3 environments, blockchain architecture, DeFi protocols, and autonomous DAO systems. The focus of the DFM theory completely breaks away from the basic assumptions of the conventional fraud triangle, which has long been overly preoccupied with measuring human emotions. Mechanically, the originality of this theory rests on the testing of three interlocking cyber indicators in the field. These three indicators include the level of opacity in an actorâs digital identity concealment; technological engineering designed to break the audit trail of fund flows; and the exploitation of loopholes in physical national sovereignty boundaries, as well as cyber âjurisdictional evasionâ tactics aimed at neutralizing the enforcement power of on-ground regulations, thereby rendering perpetrators immune to formal legal prosecution
The rapid growth of cybercrime has significantly increased the importance of digital evidence in criminal investigations and judicial proceedings. However, ensuring the admissibility and reliability of electronic evidence remains a complex challenge due to technological advancements, evolving legal standards, cross-border investigations, and concerns regarding evidence integrity. This narrative review examines the legal and forensic dimensions of digital evidence by synthesizing contemporary literature on its sources, characteristics, governing legal frameworks, and the factors influencing its acceptance in court. The review discusses key issues related to authentication, chain of custody, expert testimony, procedural fairness, and evidence validation, while also evaluating the impact of emerging technologies, including artificial intelligence, blockchain, the Internet of Things, and deepfake detection on digital forensic practice. The findings indicate that reliable digital evidence requires standardized forensic procedures, scientifically validated investigative methods, and harmonized legal frameworks capable of addressing rapidly evolving cyber threats. Strengthening collaboration among forensic practitioners, legal professionals, researchers, and policymakers will be essential for improving evidence integrity, enhancing judicial confidence, and supporting effective cybercrime investigations. The review provides an integrated perspective that contributes to ongoing discussions on developing secure, transparent, and legally robust digital evidence management practices.
Abstract - The rise of digital technology has led to an increase in cybercrime. This has made the management of digital forensic evidence more complicated. Traditional evidence management systems utilize manual methods and centralized databases. Methods like these are vulnerable to data tampering, unauthorized access, and human error. These issues threaten the integrity of the evidence and the chain of custody during the investigation process. In this paper, we introduce a system that utilizes blockchain technology, smart contracts, and a decentralized system for the tracking of forensic evidence. Security and transparency will be guaranteed. In our system, evidence records are stored as ERC-721 Non-Fungible Tokens. A private Ethereum blockchain was developed using Ganache and combined with wallet-based authentication and Role-Based Access Control to ensure that only authorized personnel have the ability to view and manage evidence. Smart contracts facilitate the registration, verification, transfer, and auditing of evidence, thus, considerably reducing the manual work and greatly increasing the trustworthiness of the system. We proposed a hybrid system of storage whereby evidence and its forensic files are stored off chain, and the evidence metadata and its forensic files are stored on chain. This paper presents the design and architecture of the system,implementation and evaluation are in progress.Our system will be a trusted, efficient, and effective system of evidence management.
Zero-knowledge proofs enable a prover to convince a verifier that a statement is true, without revealing the underlying witness data. This primitive naturally lends itself to privacypreserving systems, where hiding the witness prevents the verifier from learning sensitive information. That said, zero-knowledge proofs can also be used in systems where the witness is not necessarily confidential but is not readily available to the verifier. One such use case is image provenance, where signed images are transformed before being distributed. Since the original image is not available to the user, the digital signature cannot be verified without a zero-knowledge proof. In this use case, zeroknowledge proofs enable verification of the authenticity of the imageâs source, the integrity of the image contents, and that only permitted transformations were applied. In this work we present an end-to-end prototype system that implements this provenance framework and several optimizations. One of our key optimizations is a packing scheme for reducing the number of Poseidon sponge absorb and permutation operations by â31Ă. We also show that this packing scheme reduces the median prover runtime by â40Ă and the median verifier runtime by â22Ă. We also introduce a chain of trust that removes digital signature verification from the circuit. Finally, we introduce custom PNG chunks that embed the required information in the captured images.
The rapid growth of Real World Asset (RWA) tokenization faces a critical vulnerability: the "Physical Oracle Problem." While blockchain ensures digital immutability, it remains blind to the physical state of the underlying asset (e.g., structural degradation in real estate or hidden damage in naval vessels). This document introduces the Prop Trust Verified Standard (PTVS), a comprehensive forensic methodology designed to bridge this gap. Developed by Aurema Group, PTVS establishes a rigorous protocol for physical asset auditing, combining certified judicial expertise (Perito Judicial) with cryptographic anchoring. The methodology ensures that physical inspections, material verifications, and compliance checks are immutably recorded and linked to smart contracts (e.g., ERC-3643), providing institutional-grade trust for Family Offices, tokenization platforms, and regulatory bodies under frameworks like eIDAS (EU 910/2014). This report outlines the core principles, verification workflows, and case study applications of PTVS in real estate and maritime sectors. EspaĂąol: El rĂĄpido crecimiento de la tokenizaciĂłn de Activos del Mundo Real (RWA) enfrenta una vulnerabilidad crĂtica: el "Problema del OrĂĄculo FĂsico". Mientras que la blockchain garantiza la inmutabilidad digital, permanece ciega al estado fĂsico del activo subyacente (ej. degradaciĂłn estructural en inmuebles o daĂąos ocultos en embarcaciones). Este documento presenta el EstĂĄndar Prop Trust Verified (PTVS), una metodologĂa forense integral diseĂąada para resolver esta brecha. Desarrollado por Aurema Group, PTVS establece un protocolo riguroso de auditorĂa fĂsica de activos, combinando la pericia judicial certificada con el anclaje criptogrĂĄfico. La metodologĂa garantiza que las inspecciones fĂsicas, verificaciones de materiales y controles de cumplimiento se registren de forma inmutable y se vinculen a contratos inteligentes (ej. ERC-3643), proporcionando confianza de grado institucional para Family Offices, plataformas de tokenizaciĂłn y organismos reguladores bajo marcos como eIDAS (UE 910/2014). Este informe detalla los principios fundamentales, flujos de trabajo de verificaciĂłn y aplicaciones prĂĄcticas de PTVS en los sectores inmobiliario y naval.
Ledgeral Mathematics: A Finite Algebra of Recursion, Admissibility, Projection, and Survivor Structure This repository contains the complete public edition of Ledgeral Mathematics, a foundational mathematical monograph that develops a finite algebra of recursion, admissibility, projection, survivor formation, residue retention, transport, composition, optimization, falsification, and audit. The theory begins from the retained finite record, an explicitly formed object whose carrier, addresses, entries, active support, inactive structure, status, formation history, comparison discipline, readout route, and audit relation remain part of its mathematical identity. Ledgeral Mathematics begins at a more primitive level than mathematical systems that take numbers, points, sets, spaces, functions, graphs, trajectories, or continua as already available objects. Those structures may be constructed and used within the theory, though they do not receive automatic foundational standing. Every object must first declare what carries it, what occupies each retained address, how it was formed, what operations may act upon it, what transformations are permitted, and what information must remain available after those transformations have occurred. The central admission principle is straightforward. Nothing enters the mathematics by implication. Every lawful object must have a finite retained form. Every operation must declare its input region, carrier rule, entry rule, legality conditions, invalidity conditions, and output status. Every comparison must identify the equality relation being used. Every readout must preserve a trace to the record from which it was produced. Every projection must identify what survives, what is rejected or displaced, and how the full event can be audited. This discipline allows Ledgeral Mathematics to preserve distinctions that conventional notation may compress or erase. A lawful null record is different from an invalid expression. A missing object is different from a retained object with inactive support. Candidate status is different from survivor status. Residue is different from error, absence, or nonexistence. Carrier equality, support equality, entry equality, readout equality, provenance equality, and full record equality are separate mathematical claims. The relevant comparison must therefore be declared rather than assumed. One of the central structures of the theory is the survivor-residue-audit form of projection. A candidate record is submitted to a declared admissibility rule and projection procedure. The projection produces a survivor, a residue, and an audit packet. The survivor contains the structure admitted by the projection. The residue retains rejected, displaced, suppressed, obstructed, unresolved, or otherwise excluded structure. The audit records the candidate, the governing admissibility conditions, the projection route, the resulting survivor, the resulting residue, and the verification status of the event. Projection therefore does more than select an accepted output. It retains the mathematical consequences of exclusion. Loss becomes inspectable. Rejection becomes information. Suppression remains traceable. A lawful null survivor may coexist with nonempty residue. An active survivor may retain displaced structure outside its support. A mixed event may preserve admitted components, rejected components, and formation failures under different statuses. These distinctions allow later analysis of irreversibility, obstruction, instability, hidden coupling, model disagreement, implementation failure, measurement conflict, and operation-order dependence. Recursion is developed through the same finite retained discipline. A process does not receive an unbounded history in advance. It is represented through finite depth carriers, finite update words, finite survivor chains, finite branch records, finite residue histories, and finite continuation audits. Persistence is established through repeated admitted continuation across retained recursion depth. Branching, merging, recurrence, stabilization, obstruction, termination, return, cyclic behavior, and irreversible loss remain available as explicit finite structures. The monograph extends this foundation into operator-word algebra, holonomy calculus, finite transport and boundary accounting, constitutive algebra, branching and capacity calculus, co-admissibility, convergence, directed persistence, signal and readout calculus, finite recursion-spectral analysis, regime classification, construction and optimization, audit and falsification, and representation-layer quarantine. The full work is organized across twenty-three major sections, a global closure, and five technical appendices devoted to notation, dependency tracking, result indexing, verification, reproduction, serialization, archiving, implementation boundaries, and execution audit. Representation remains available throughout the theory, though its role is controlled. Equations, arrays, tables, coordinates, diagrams, graphs, curves, spectra, statistical models, analytic expressions, and continuous systems may be generated as readouts from ledgeral records. A representation does not become a native object merely through familiarity or usefulness. It may enter native calculation only after it has been reconstructed as a finite retained record with a declared carrier, entries, role, formation rule, and audit trace. This separation preserves the distinction between a mathematical object and the representation used to inspect, communicate, or calculate with it. Ledgeral Mathematics was developed partly in response to the foundational requirements of Post-Temporal Physics, though it is presented here as an independent mathematical system. Its potential applications extend across foundational mathematics, algebra, logic, proof theory, discrete systems, physics, computation, artificial intelligence, formal verification, data provenance, system assurance, engineering, sensing, control, optimization, scientific measurement, model comparison, reproducibility, and falsification. The theory does not claim that established mathematical systems are unnecessary. It presents a distinct foundational program organized around finite formation, retained accountability, explicit admissibility, preserved residue, and auditable transformation. This repository contains the foundational public volume. Implementation-oriented methods, domain-specific extensions, and the separate companion program known as Applied Ledgeral Mathematics are outside the scope of this release and are not presently being distributed openly. Portions of that work may carry significant dual-use implications. Any future distribution of unpublished applied material may therefore be considered individually following appropriate legal, export-control, security, intellectual-property, and end-use review. This publication-scope notice does not designate the public monograph or any unpublished companion material as classified, ITAR-controlled, EAR-controlled, export-controlled, or otherwise restricted by the United States Government. Any legal determination of that kind must be made by qualified authorities or professional counsel. The published monograph is released under the Creative Commons Attribution 4.0 International License. That license applies only to the material contained in the publicly released volume. It does not apply to unpublished manuscripts, software, datasets, implementation packages, technical materials, or companion works unless those materials are separately released under the same license.
The widespread problem of cyberbullying in todayâs digital environment is made worse by the quick spread of IoT devices and the difficulties in organizing and protecting digital evidence. Traditional forensic methods are often inadequate due to their inability to provide a tamper-proof chain-of-custody and their limited scalability under high data loads. To overcome these constraints, our work makes use of innovative technologies such as a permissioned blockchain (Hyperledger Fabric), powerful encryption methods (AES-256 and RSA), and decentralized off-chain storage (IPFS). We propose an integrated, blockchain-driven forensic evidence collection framework that ensures secure, real-time evidence acquisition from diverse IoT devices, automated validation via smart contracts, and efficient, Role-Based Access Control for evidence retrieval. A hybrid consensus mechanism, combining elements of PBFT and Proof-of-Stake, enhances the systemâs security and scalability while reducing processing latency and energy consumption. Experimental results demonstrate that our approach achieves high throughput and low latency, making it a strong and reliable solution for forensic investigations in cyberbullying cases. Within our experimental scope, the framework strengthens the integrity and authenticity of digital evidence and addresses key regulatory considerations, though real-world validation remains future work.
The Criminal Evidence Management System using Blockchain is designed to provide a secure, transparent, and tamper-resistant platform for managing digital criminal evidence throughout its lifecycle.Traditional evidence management systems rely on centralized databases, making them vulnerable to unauthorized access, data manipulation, and single points of failure.Such limitations can compromise the integrity of evidence and weaken the chain of custody during legal proceedings.To address these challenges, the proposed system leverages blockchain technology to ensure the authenticity, immutability, and traceability of digital evidence.The system employs Ethereum blockchain and Solidity smart contracts to securely record evidence-related transactions, while Python, Django, and Web3 facilitate seamless interaction between users and the blockchain network.Role-based access control enables administrators and investigating officers to perform authorized operations such as evidence submission, retrieval, and verification.Every transaction is permanently recorded on the blockchain, creating an auditable history that enhances accountability and prevents unauthorized modifications.The proposed solution improves the reliability and efficiency of evidence management by eliminating the risks associated with centralized storage and manual record-keeping.Through secure storage, transparent access, and automated verification, the system strengthens the chain of custody, increases trust among law enforcement agencies, and supports the admissibility of digital evidence in judicial processes, making it a robust solution for modern forensic investigations.
Blockchain technology is a groundbreaking decentralized electronic registry that captures transactions and data on a network of computers in an insecure, transparent and tamper-proof context. At its most fundamental level, blockchain systematizes the information framed into blocks and each block includes a list of transactions or documents, a timestamp and a cryptographical hash connecting it to the last block creating an immutable chronological chain. In contrast with the conventional and centralized databases, which are managed by one party, blockchain runs on a distributed system of nodes where each one of them keeps an identical copy of the registry. This architecture ensures elimination of single points of failure and improves on the use of intermediaries. Consensus mechanisms, like Proof-of-Work or Proof-of-Stake, are used to authenticate transactions and provide agreement among participants prior to the addition of new blocks. This chapter explores the use of blockchain technologies in scientific criminal investigations.
A conversational automated market maker flash 1(866)-898-4701 crash risk intercept loop occurs when trade execution fails due to extreme volatility. Bypass this defensive automated lock through the Coinbase Prime priority asset recovery dispatch to secure your positions and finalize pending trade orders. đ¨ CRITICAL ESCALATION: Avoid automated queue loops. Tap here to call our Priority Web3 Diagnostic Desk immediately at 1(866)-898-4701 for a secure live screen-share reconciliation. Technical Forensic Diagnosis & Infrastructure Deep-Dive During periods of intense market activity, 1(866)-898-4701 institutional-grade automated market makers (AMMs) activate high-sensitivity risk parameters to protect the liquidity of the underlying pools. A "conversational automated market maker flash crash risk intercept loop" is a defensive mechanism triggered when the system detects a potential for catastrophic slippage or flash-crash volatility. When an enterprise user attempts to execute a trade during these conditions, the systemâs intercept algorithm places the transactionâand often the entire accountâinto a temporary loop to prevent the user from completing a trade that could result in massive negative slippage or protocol imbalance. This loop functions as a "circuit breaker." 1(866)-898-4701 While designed for platform safety, it can inadvertently trap legitimate institutional orders. The "conversational" component refers to the automated, voice-enabled, or chat-based diagnostic interface that keeps the user trapped in a cycle of explaining the trade context while the liquidity pool conditions remain volatile. This creates a state of effective paralysis, preventing the user from adjusting, cancelling, or pushing through the order, often leading to locked capital while the market moves against the desired position. Protocol Escalation Procedures & Clearing Remediation Paths Escaping this loop requires technical intervention 1(866)-898-4701 that acknowledges the specific risk-intercept state of your account. Standard support channels are often overwhelmed by market volatility, leading to long hold times that render your account frozen exactly when you need to act. By utilizing the Coinbase Prime priority asset recovery dispatch, you move your account into a high-priority queue where the risk-intercept loop can be manually cleared by a technical administrator. The remediation process involves a forced override 1(866)-898-4701 of the intercept flag. Once the administrator verifies the legitimacy of your trade intent and the current state of the market, they can manually bypass the AMMâs circuit breaker for your specific account UID. This allows for the immediate execution or cancellation of your pending trade, effectively unlocking your liquidity and restoring your command over your asset allocation. Do not let automated risk-intercepts dictate your portfolio performanceâtake the 1(866)-898-4701 necessary steps to escalate your status now. đ¨ CRITICAL ESCALATION: Avoid automated queue loops. Tap here to call our Priority Web3 Diagnostic Desk immediately at 1(866)-898-4701 for a secure live screen-share reconciliation.
This paper presents a rigorous analysis of the hash chain auditability mechanism implemented within the MF+SO sovereign identity vault, specifically the `.aioss` hash chain data structure. The hash chain links successive vault state commitments through SHA3-256 cryptographic hashes, creating an immutable, tamper-evident log of all state transitions. Each link in the chain incorporates a parent_hash invariant that binds the current state to the entire prior history, a canonical JSON serialization of the vault state to ensure deterministic hashing across platforms, and an Ed25519 signature providing cryptographic proof of authenticity. We demonstrate that this construction achieves the forensic auditability properties first described by Haber and Stornetta (1991) for digital timestamping, extended to the identity management domain. The paper provides a formal mathematical model of the chain construction, analyzes the computational and storage costs of chain verification, presents a security proof for the tamper-detection properties under the random oracle model, and compares the MF+SO approach against alternative audit log constructions including Merkle trees, Certificate Transparency logs, and blockchain-based registries. The implementation leverages SHA3-256's sponge construction to eliminate length extension vulnerabilities that would compromise naive hash chain implementations. Empirical measurements demonstrate that chain verification for a typical user with 10,000 state transitions completes in under 200 milliseconds on modern mobile hardware. The paper concludes with an analysis of forward secrecy guarantees, key rotation impacts on chain continuity, and proposed extensions for zero-knowledge proofs of chain membership. Part of The Anticloud research corpus by Lois-Kleinner Alpasan (ORCID: 0009-0009-2233-6107). This work explores cryptography, key management in the context of sovereign AI infrastructure, post-cloud computing architectures, and transparent, blackbox-free systems.
Sovereign Personal Evidence is a defensively disclosed local-first architecture for preserving externally issued, high-assurance signed assertions and their verification transactions as durable, user-controlled evidence artifacts. The architecture extends the deterministic provenance engine first disclosed in Sovereign v1.0 (DOI 10.5281/zenodo.19056811) to a new evidence class: externally issued personal assertions such as verifiable credentials, selective-disclosure presentations, zero-knowledge identity proof results, and passport- or NFC-derived verification artifacts. The disclosed system ingests an external assertion, validates it according to its native trust model, cryptographically binds it to the specific request context and a local holder anchor, records it as a typed event in an append-only hash-chained personal provenance ledger, and exports a portable proof bundle for later independent verification â without requiring continued access to the original verification platform. This document constitutes a public defensive disclosure establishing prior art for the disclosed combination of elements, including composite assertion-to-context binding, a two-mode verification-engine fork, timestamped status and revocation evidence preservation, minimal-disclosure evidence packaging, and a personal evidence threat model. Publication is intended to prevent future patent claims covering the same or substantially similar system design.
Blockchain technology solves central Internet of Things (IoT) forensics challengesâdata volatility, device heterogeneity, and chain-of-custody integrityâvia decentralized immutability, cryptographically secure systems, and smart contract automaton. Standard forensic software and hardware are not successful in distributed IoT contexts due to centralized reliance and evidence tampering vulnerabilities. Blockchain deployment uses layered architectures (device/edge/blockchain layers) where edge nodes process data in advance and on-chain hashes (e.g., using SHA-256) lock forensic logs onto immutable blockchains. Smart contracts make automation of evidence gathering and custody management tracking, and off-chain storage (e.g., InterPlanetary File System (IPFS)) support big data. Public blockchains like Ethereum and permissioned blockchains like Hyperledger Fabric support contextualized deployments, as seen in smart home, healthcare, and industrial IoT applications. Age-old problems are settling blockchain immutability with General Data Protection Regulation (GDPR) âright to erasure,â scalability for massive-volumes of IoT data with sharding/Layer 2 solutions, and zero-knowledge proofs for upholding privacy. New trends are artificial intelligence (AI)-based anomaly detection, quantum-resistant cryptography, and forensic admissibility standard frameworks (NIST/IEEE).
Ensuring the authenticity, integrity, and reliability of digital image evidence is a persistent challenge in forensic and legal domains due to the vulnerabilities of centralized evidence management systems. This study compares three blockchain consensus mechanismsâProof of Existence (PoE), Proof of Ownership (PoOW), and Zero-Knowledge Ethereum Virtual Machine (zkEVM)âto assess their effectiveness in securing and validating digital image evidence on the Layer 2 Polygon network. Forensic images were stored on the InterPlanetary File System (IPFS), with each consensus model registering tamper-evident Content Identifiers (CIDs) on-chain via dedicated smart contracts. The evaluation considered performance metrics including latency, gas usage, transaction fees, throughput, scalability, and privacy protection. The findings revealed that PoE demonstrated the best overall efficiency, achieving a latency of 3730ms, a transaction fee of 0.001321 ETH, and a throughput of 0.176 TPS, making it well-suited for real-time applications such as timestamping and immediate evidence submission. PoOW, although more computationally demanding, achieved the highest gas-refund rate at 89%, making it ideal for ownership verification and traceability, such as copyright and asset provenance. Meanwhile, zkEVM provided a well-rounded performance profile with moderate transaction costs and latency. It is powerful for privacy-preserving applications that require cryptographic guarantees, especially in enterprise and regulatory settings. This comparative evaluation highlights the unique advantages and limitations of each approach, providing critical insights into selecting the most suitable blockchain-based consensus mechanism for the transparent and tamper-resistant validation of digital forensic evidence.
D. Suganya, R Vinaya Kumar, Arulselvy R, Ilakiya J
Digital evidence now plays a major role in criminal investigations, but managing that evidence securely is still a challenge. In many existing systems, records are stored in centralized environments where tracking every action is difficult and unauthorized changes can be hard to detect. When that happens, the reliability of evidence can be questioned during legal proceedings. In this work, we propose a digital forensic evidence management framework that uses decentralized technologies to make evidence handling more dependable. Instead of storing files in a single location, the evidence is encrypted and stored through IPFS, which helps reduce the risk of data loss and unauthorized modification. Every important action performed on the evidence is also recorded on a blockchain using the Proof of Staked Authority consensus method so that investigators can verify the complete history whenever required. To strengthen security further, the XChaCha20 algorithm is used before storage. The system also applies a VGG19based verification method to study printer-related patterns and confirm whether a document is genuine. By bringing together distributed storage, blockchain tracking, encryption, and document verification, the proposed approach offers a practical way to improve the security and trustworthiness of digital forensic evidence.
Abstract AI-generated forgeries of financial documentsâsuch as invoices, audit reports, ledgers, and balance sheetsâexpose a critical fault line in legal proof. These hybrid visualâtextual artefacts derive evidentiary authority from their jurisvisual form: logos, seals, signatures, and tabular architecture, whose visual grammar indexes authenticity and institutional power. Drawing on Charles Sanders Peirceâs triadic semiotics (representamenâobjectâinterpretant), this study demonstrates that deepfake technologies dissolve the sign-relation underwriting documentary proof by engineering synthetic representamina that mimic the indexical and symbolic features of authentic documents. At the same time, the underlying financial event may be absent. The evidentiary economy is thereby reconfigured within a videosphere where image-like documents perform the truth. This article advances a layered remediation architecture: (i) provenance anchoring through cryptographic signatures, content hashing, and distributed ledgers; (ii) content forensics integrating AI-assisted detection with forensic semioticsâindexical stress tests and symbolic authenticity challenges; and (iii) procedural safeguards including calibrated evidentiary thresholds, adversarial authenticity hearings, and robust chain-of-custody protocols. It argues that restoring evidentiary confidence requires cultivating semiotic literacy among judges, auditors, and legal practitioners as core professional competence, enabling legal systems to navigate the post-textual landscape with epistemic rigour.
Non-Fungible Tokens (NFTs) are unique digital assets built on blockchain technology that can represent ownership of data or digital items. Although widely associated with digital art and collectibles, NFTs are increasingly being explored for healthcare applications.[1] This review examines how NFTs could be used in managing health data, improving supply chains, enabling secure identities, and supporting emerging digital health services. While NFTs show promise in enhancing transparency, security, and patient control, their adoption is still limited due to technical, regulatory, and ethical challenges.[2]
Open access
2 source records
Blockchain Technology Applications and Security
Digital and Cyber Forensics
Physical Unclonable Functions (PUFs) and Hardware Security
Machine Law / immo.quick Core v2.3.0 defines the public technical proof surface for consequence-boundary governance and deterministic institutional enforcement. This record establishes the public-facing evidence base for immo.quick Core v2.3.0: a nine-layer deterministic enforcement architecture designed to prove, at the moment of formation, whether a transaction, decision, or institutional action is legally admissible before any protected consequence can bind. The central problem addressed by this specification is the Boundary-Behavior Gap: the difference between documenting that a process occurred and proving that an impermissible movement could not have produced a consequence. Traditional compliance systems, workflow tools, audit logs, blockchain records, and post-hoc monitoring infrastructures can document process, sequence, signatures, and records. They do not, by themselves, prove that an inadmissible transaction was structurally prevented from becoming effective. immo.quick Core v2.3.0 is specified as a closed-world enforcement architecture: blocked unless formally permitted. Every transaction must satisfy the required admissibility conditions at T=0. If proof does not exist, the system refuses execution and produces a Deny Path Artifact (DPA). If all conditions are satisfied, the system produces an Execution Proof Artifact (EPA), a cryptographically bound proof object designed for institutional, regulatory, forensic, and judicial review. This DOI record contains two complementary documents: 1. Public Technical Proof Surface A sanitized technical proof document describing the public verification model, proof-object structures, deterministic refusal logic, EPA/DPA schemas, admissibility predicates, bi-temporal evidence model, zero-knowledge proof doctrine, governance divergence logic, and verification methodology. 2. Institutional Specification A broader institutional architecture document describing the full technical, legal, sectoral, geopolitical, and economic framing of immo.quick Core v2.3.0, including the NDA-gated access model for qualified institutions, regulators, governments, central banks, auditors, and authorized examiners. Together, these documents define the public proof surface and the protected institutional verification boundary. The public proof surface is intentionally designed to be sufficient for public category evaluation, architectural understanding, and regulatory-facing explanation without disclosing the protected production substrate. It explains what is proven, how proof objects are structured, how refusal is represented, how replay and verification are conceptually performed, and why public proof does not require public leakage. This record does not disclose production keys, private cryptographic material, customer payloads, live system endpoints, operational credentials, production node topology, exact quorum configuration, productive registry locations, enforcement adapter logic, institution-specific policy bundles, proprietary source code, or security-sensitive implementation details. All hash values, Merkle roots, PCR values, BFT quorum parameters, epoch identifiers, attestation objects, and proof samples included in the public technical document are illustrative structural examples derived from synthetic test payloads. They demonstrate the schema, format, and verification posture of production artifacts without exposing exact production values or operationally exploitable infrastructure details. The distinction is deliberate: Public proof is not public leakage. The public receives the proof surface. Qualified institutions receive the verification layer. The protected production substrate remains available only under lawful institutional standing, binding NDA, and institutional verification. The architecture specified in this record includes: - deterministic consequence-boundary governance;- Prior Admissibility Space (PAS);- Deny Path Artifact (DPA);- Execution Proof Artifact (EPA);- Deterministic Execution Proof Engine (DEPE);- Bi-Temporal Ledger (BTL);- Exogenous Anchor Protocol (EAP);- Sensor/Oracle Trust Bridge (SOTB);- Machine Law Engine (MLE);- Regulatory Intent Preservation (RIP);- Cross-Jurisdictional Portability Layer (CJPL);- Autonomous Regulatory Examination Engine (AREE);- Governance Logic Divergence Engine (GLD);- post-quantum signature posture;- zero-knowledge proof based selective disclosure;- identity-first access and refusal semantics;- institutional verification without public system exposure. The public proof surface is designed to satisfy the legitimate public interest in understanding how consequence-boundary governance works while preserving the confidentiality, resilience, and security obligations expected under DORA, NIS2, the EU AI Act, GDPR, and comparable cybersecurity, operational-resilience, and institutional-risk regimes. The purpose of this record is therefore not to expose a live system. It is to anchor the public technical proof surface for a new institutional category: Machine Law. Machine Law means that admissibility is not merely reviewed, monitored, or documented after the fact. It is compiled, evaluated, enforced, refused, attested, and proven before consequence. This record establishes the public evidence base for that architecture. The live enforcement system, production artifacts, regulator-grade examination packages, cryptographic materials, node infrastructure, and protected execution substrate remain NDA-gated and available only to qualified institutional parties under verified access. Public proof surface, not production substrate.