The medical systems are becoming challenged by the issues associated with the secure verification of patient identities, privacy-protecting access control, and dynamic consent management of Electronic Health Records (EHRs). Conventional centralized designs infringe on patient-centered autonomy, have poor identity management, and put sensitive information at the risk of inaccessible information. The paper introduces a proposal to develop a decentralized and privacy preserving mechanism of access control using blockchain-enabled Self-Sovereign Identity (SSI), as proposing a combination of verifiable credentials (VCs), smart contracts, and zero-knowledge proofs (ZKPs). The proposed Consent-Driven Decentralized Identity Verification Algorithm (CD-DIVA) enables the patient to create decentralised identities, issue tokens of consent, and dynamically assign or remove access rights without having to involve third-party authorities. Smart contracts enable the conjurant enforcement of various consent terms and offer an inauditable audit trail and ZKPs grant healthcare providers with the ability to demonstrate their authority without revealing sensitive information. Experimental assessments show the framework to be efficient in minimizing the access latency, eliminating unauthorized disclosures and providing effective interoperability of heterogeneous healthcare systems. This contribution will be a complement to the current blockchain-based healthcare solutions since it helps adopt a patient-centric, tamper-proof, and privacy-preserving healthcare ecosystem by bridging the access control and trust gap in such systems.
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.
Zero-knowledge proof security rests on cryptographic reductions: breaking a ZK scheme requires breaking an underlying hard problem. We introduce an independent, complementary security analysis based on the Structural Action Principle. We extend the discrete action functional S[psi] = sum_t lambda(s_t) from Boolean CDCL trajectories to algebraic constraint systems over finite fields F_p, defining an Algebraic Structural Action with density functions that recover Groebner basis complexity, Polynomial Calculus proof size, algebraic degree growth, and elimination ordering as mechanical analogues. We prove a non-circular lower bound for preimage search in substitution-permutation network (SPN) hash functions: for a k-round SPN with S-box degree alpha and state width t, the preimage search system is a square polynomial system of k*t degree-alpha equations in k*t variables. Under the semi-regularity assumption (standard in algebraic cryptanalysis, empirically verifiable, and independent of any cryptographic security conjecture), the solving degree d_reg is determined by the Hilbert series H(z) = (1 - z^alpha)^{k*t}/(1-z)^{k*t}. We establish two action bounds: a peak bound S[psi] >= d_reg (any trajectory must encounter degree d_reg), and a stronger cumulative bound S[psi] >= sum_{d=alpha}^{d_reg-1} h_d using the Hilbert function coefficients as density, which captures the total algebraic work rather than just the peak degree. For Poseidon (alpha=5, t=3, k=8): d_reg = 97 and the cumulative bound gives S[psi] >= 2^{56}. The result applies to ANY SPN hash function (Rescue, Griffin, Anemoi, MiMC) and provides a second line of defense for Behavior-Bound Signature (BBS) security, grounded in algebraic proof complexity rather than crypto- graphic hardness assumptions. Keywords: structural action principle, algebraic proof complexity, polynomial calculus, semi-regularity, Hilbert function, SPN hash functions, zero-knowledge security, behavior-bound signatures
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.
Privacy disclosure from model parameters and malicious attacks are critical issues in federated learning (FL). Existing research has yet to effectively address the simultaneous need for efficient communication design, privacy protection, and attack detection, which impedes the widespread adoption of FL in mobile edge networks over 6G wireless communication. In this paper, we propose a trustworthy FL framework that can ensure privacy, robustness, accountability, fairness, and explainability in mobile edge networks. Specifically, we integrate authenticated zero-knowledge proofs (ZKPs) and Pedersen commitments into the FL process. Despite the lack of direct access between servers and mobile devices, the servers can still identify trustworthy clients for specific tasks. Clients can verify the authenticity of the received global model based on the provided proofs and commitments. Furthermore, we leverage Ethereum to act as the verifier and authenticator of models. This verification and authentication process enables the servers to detect abnormal local models and perform trust-based aggregations. Numerical results demonstrate that the proposed trustworthy FL framework significantly improves the global model's in terms of accuracy, convergence rate, and security.
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$) is analyzed and shown to be compatible with clinical research workflows operating on daily or weekly training cycles. We emphasize that the current defense guarantees rejection of large-norm malicious updates; robustness against subtle low-norm or directional poisoning remains future work.
Dense retrieval services increasingly underpin semantic search, recommendation, and retrieval-augmented generation, yet clients typically receive only a top-$k$ list with no auditable evidence of how it was produced. We present V3DB, a verifiable, versioned vector-search service that enables audit-on-demand correctness checks for approximate nearest-neighbour (ANN) retrieval executed by a potentially untrusted service provider. V3DB commits to each corpus snapshot and standardises an IVF-PQ search pipeline into a fixed-shape, five-step query semantics. Given a public snapshot commitment and a query embedding, the service returns the top-$k$ payloads and, when challenged, produces a succinct zero-knowledge proof that the output is exactly the result of executing the published semantics on the committed snapshot -- without revealing the embedding corpus or private index contents. To make proving practical, V3DB avoids costly in-circuit sorting and random access by combining multiset equality/inclusion checks with lightweight boundary conditions. Our prototype implementation based on Plonky2 achieves up to $22\times$ faster proving and up to $40\%$ lower peak memory consumption than the circuit-only baseline, with millisecond-level verification time. Github Repo at https://github.com/TabibitoQZP/zk-IVF-PQ.
The research areas of digital forensics are subject to Blockchain (BC) technology implementation and enhanced over the current alternative introduced in this publication and capable of addressing certain grave issues, which are concomitant with data collection and preservation of digital evidence. Another puzzle that is resolved by the blockchain is the inherent transparency, immutability that central storing data is extremely prone, and the blockchain ensure that this does not happen, as it provides the location with a distributed setting that would make security and responsibility in the conventional world of digital forensics a nightmare. Cryptographic Distributed Ledger Technologies of Blockchain are based on the consensus technology, which allows aggregating, housing and authenticating digital evidences in a set of peer pairs in a secure way. As a result, the study that has been suggested in this paper, Blockchain Based Digital Forensic Architecture (BC-DFA) is founded on the distributed characteristics of blockchain, and is concerned with the decentralization and security of digital evidences. A combination of multiple essential methods including AOKGE: authentication with the best key generation encryption, SBVM: Secure Block Verification Mechanisms and implementing Multi-key Homomorphic Encryption or MHE is used to end up with the truly secure data encryption. The information and the secret key are spread in a manner that reduces the chances of single point failure that is associated with the centralized cloud design that reveals the information in the literal form and promotes hacking and unauthorized access. The SBVM authentication is done to authenticate all the data blocks in order to ease the tracing of any tampering or alteration of forensic evidence.
In the emerging trends technologies, such as blockchain, security is one of the most critical aspects to be strongly addressed. In general, confidentiality, integrity, and availability (CIA) are the concepts that are considered to provide security. To handle the public blockchain, this is always an important challenge that needs special attention. For example, in distributed systems, when copying information over the whole network, confidentiality will be the main issue. In real time, the CIA triad faces considerably more problems in terms of integrity; even the integrity of information appears to be unaffected. In blockchain, read availability is considered much higher than write availability. This chapter delves into the combination of two technologies: Generative Adversarial Networks (GANs) and blockchain. With the increasing prevalence of blockchain applications, ensuring security is a main concern. This chapter explores methods of incorporating GANs into blockchain security by introducing an approach to protect distributed ledger systems. Utilizing GANs for detecting anomalies in modern threats and adopting security measures will strengthen blockchain networks against emerging cyberthreats. This chapter focuses on obstacles, emphasizing the impact of GANs on blockchain security and offering insights for researchers looking to enhance the resilience of decentralized systems in today&s;s cybersecurity environment.
Abdul Kalam Aasath J, Ajay Kumar K, A. Sathiyaraj, Mary Gladence L
This paper introduces a Zero-Trust, provably secure electronic voting protocol that overcomes the long-standing trilemma of security, privacy, and end-toend verifiability by combining advanced cryptographic mechanisms with multimodal biometric authentication. Electronic voting promises improved accessibility and efficiency, yet traditional systems depend heavily on centralized trusted authorities, leaving them vulnerable to insider threats, coercion, and large-scale manipulation. To eliminate such vulnerabilities, the proposed framework employs deep-learning-based facial and fingerprint recognition with liveness detection to prevent identity spoofing at the edge. Voter privacy and coercion resistance are ensured through a Nullifiable Commitment Scheme integrated with Paillier Homomorphic Encryption, enabling vote tallying directly over encrypted ballots without exposing vote contents. Additionally, the system introduces a novel Homomorphic Encrypted Anomaly Detection (HE-AD) module capable of identifying malicious traffic patterns on ciphertext features with an accuracy of 98.2%, thereby enabling real-time threat monitoring without compromising confidentiality. All encrypted ballots, Zero-Knowledge Proofs (ZKPs), and audit logs are recorded on a permissioned blockchain to guarantee Verifiability of Cast as Intended and Recorded as Cast. Experimental results confirm that the system achieves an Equal Error Rate (EER) of 0.85% in biometric verification and maintains an average transaction latency below 2.5 seconds, demonstrating its feasibility for national-scale elections in high-assurance environments.
• A novel Framework for Secure and Efficient Healthcare Data Management • A Blockchain-Based Identity Management and Access Control • Data Integrity Verification with Merkle Trees • Scalable and Compliant Data Storage • Secure Data Sharing via Proxy Re-Encryption The healthcare sector increasingly relies on digital infrastructures to manage large volumes of sensitive medical data. Ensuring integrity, controlled access, interoperability, and auditability remains a fundamental challenge. We propose BlockHealth, a hybrid blockchain-based framework that integrates smart contracts, distributed databases, and proxy re-encryption to support secure and verifiable healthcare data management. The system leverages Ethereum and NFT-based identities for access control, Merkle-tree commitment for tamper-evident integrity verification, and a distributed Cassandra storage layer for scalable and regulation-compliant off-chain data management. Proxy re-encryption enables secure delegation of access without exposing private keys, while a coordinating API service ensures interoperability with existing hospital infrastructures. Our evaluation demonstrates the feasibility and efficiency of core operations — including hashing, on-chain commits, and re-encryption — indicating that the proposed framework can provide a practical balance among verifiability, performance, and deployability in realistic healthcare environments.
• The STipChain+ system employs a combination of cryptographic accumulators and verifiable computing techniques to create a stateless blockchain for smart contracts. This system offers a solution for the use of blockchain in big data scenarios. • A new hierarchical group commitment scheme for smart contracts based on the RSA cryptographic accumulator has been developed with the objective of reducing the storage overhead of contract evidence in the system by 99 • Design a composite RSA accumulator scheme for decentralised SGX Remote Attestation under stateless conditions. This scheme should transfer the trust in Intel to the trust in the group of nodes on the chain, and accumulate the certificate information through cryptographic accumulator technology. This should reduce the burden of storage on the system and reduce the dependence on a single organisation. • The experimental results demonstrate that, while maintaining decentralisation and security, STipChain+ can significantly reduce the storage pressure on consensus nodes, thereby enhancing system throughput. Blockchain is regarded as one of the most transformative and innovative technological developments of recent times, due to its decentralised nature. Nevertheless, the majority of public chains are currently grappling with the challenge of exponential data growth within the block chain system, largely due to the presence of high levels of data redundancy, which is resulting in a significant consumption of node storage resources. However, the scheme is subject to several limitations, including the storage overhead associated with contract evidence under the conditions of extensive smart contracts and the necessity of placing trust in the central node for the purpose of verifying smart contracts. The objective of this paper is to propose a new stateless blockchain architecture, designated as STipChain+. The STipChain+ system enables the real-time computation of contract evidence and the completion of the execution and verification of smart contracts without compromising the decentralization of the blockchain. The layered grouping design of smart contracts enables the reduction of the data involved in generating contract evidence, thus resolving the shortcomings of the existing stateless blockchain scheme, which necessitates the pre-calculation of evidence and its subsequent update in a sequential manner. Furthermore, the storage of substantial amounts of evidence is also alleviated. In addition, the issue of the trusted third party when verifying smart contracts is further addressed by proposing a composite RSA accumulator certificate commitment scheme with a new smart contract execution logic and transaction verification method. The results of experimental studies indicate that STipChain+ reduces the storage requirements for state data by 99% in comparison to existing blockchain storage optimisation schemes. This is achieved without the introduction of third-party trusted entities, and the reduction in storage requirements can be verified in a fixed amount of time for any smart contract.
Recently, there has been a significant discourse in the AI community regarding "Hierarchical Reasoning LLMs," which attempt to categorize and optimize probabilistic generation tasks to reduce computational overhead. While such hierarchical inference structures optimize generation speed and coherence, they fundamentally fail to resolve the core structural crises of modern Generative AI: inevitable hallucination and extreme structural energy consumption (GPU lock-in). This paper introduces the "Hierarchical Stateless Key Generation" (HSKG) and the Mersenne Stateless Architecture, challenging the premise of neural network 'reasoning.' Instead of storing data within 820GB of neural weights and using probabilistic matrix multiplication, HSKG mathematically maps 'Absolute Truth' data into a 4096-dimensional Mersenne Prime Lattice. During query resolution, the system simply retrieves a 4KB Phase Coordinate and instantaneously materializes the data in RAM, only to vaporize it when the session terminates. By abandoning the "search and compute" paradigm for "coordinate retrieval," HSKG enforces a mathematical 0% hallucination rate, 0-byte persistent storage, and sub-0.01% GPU utilization, establishing a definitive paradigm for enterprise Zero-Trust knowledge systems. This paper explicitly defines the term "Hierarchical Stateless" to contrast with the probabilistic "Hierarchical Reasoning" of contemporary LLMs, establishing a rigorous mathematical protocol for deterministic, zero-hallucination data materialization without persistent models or physical data transfer. * Version 2.0 Update: Added section 7.A (Empirical Validation via DevTools: The 0-Byte Payload Proof). [Version 4.0 Update (Mar 2, 2026)] Formally established the "Four-Pillar Verification Metrics" table to empirically prove the 0-Byte Payload and Minimum Kolmogorov Descriptive Length. Inserted Section VIII: Disrupting Existing Paradigms (Architectural Supremacy Matrix), demonstrating the superiority over FIDO2/WebAuthn and Zero-Knowledge Proofs (ZKP). Included Supplementary Material: Independent 3rd-Party Forensic Audit Report by Claude 4.6 verifying 100% Stateless Zero-Payload execution.
Οι Υπογραφές Προσαρμογέα (Adaptor Signatures, AS) επεκτείνουν τις κλασικές ψηφιακές υπογραφές επιτρέποντας την υπό όρους υπογραφή: μια προ-υπογραφή που σχετίζεται με μια δύσκολη υπολογιστική σχέση μπορεί να μετατραπεί σε πλήρη υπογραφή μόνο όταν αποκαλυφθεί το αντίστοιχο μυστικό (witness). Αυτή η ιδιότητα επιτρέπει την υλοποίηση πρωτοκόλλων ιδιωτικών συναλλαγών χωρίς τροποποιήσεις στην υποδομή του blockchain και αποτελεί βασικό εργαλείο για εφαρμογές όπως οι ατομικές ανταλλαγές και τα ψηφιακά συμβόλαια σε blockchains περιορισμένων δυνατοτήτων, όπως το Bitcoin. Η πρώτη συνεισφορά της εργασίας είναι ένας επανακαθορισμός της ασφάλειας των AS μέσω της εισαγωγής της ιδιότητας απόκρυψης μάρτυρα (witness hiding), που διασφαλίζει ότι η ψηφιακή υπογραφή δεν αποκαλύπτει πληροφορίες για τον μάρτυρα. Παρουσιάζουμε μια γενική κατασκευή AS για οποιαδήποτε NP σχέση, βασισμένη μόνο σε μονόδρομες συναρτήσεις. Η δεύτερη συνεισφορά προτείνει μια εναλλακτική κατασκευή AS βασισμένη στο μοντέλο Υπολογισμού Πολλαπλών Μερών στο Κεφάλι (MPCitH). Η προσέγγισή μας καταργεί την ανάγκη για αναγωγές τύπου Karp, ενώ διατηρεί το σημαντικό χαρακτηριστικό του MPCitH: η κατασκευή χρησιμοποιεί το κρυπτογραφικό πρωτόκολλο ως «μαύρο κουτί» (χωρίς εσωτερική γνώση), ενώ η υπολογιστική σχέση παραμένει ρητά επεξεργάσιμη. Το προτεινόμενο πλαίσιο απλοποιεί το σχεδιασμό καθολικών υπογραφών προσαρμογέα και ενισχύει την πρακτική τους εφαρμογή σε αποκεντρωμένα δίκτυα τύπου blockchain και συστήματα προστασίας ιδιωτικότητας. Η τρίτη συνεισφορά εξερευνά τον χώρο του πρακτικού ασφαλούς υπολογισμού και εισάγει το MPC Versatile Virtual Machine (mpcVVM), ένα ανεξάρτητο από backend πλαίσιο και εργαλείο μεταγλωττιστή για την αυτοματοποιημένη μίξη πρωτοκόλλων στον ασφαλή υπολογισμό μεταξύ πολλών συμμετεχόντων (MPC). Σε αντίθεση με τις υπάρχουσες προσεγγίσεις που είναι χειροκίνητες, ευριστικές και δεσμευμένες σε ένα μόνο backend, το mpcVVM μεταγλωττίζει προγράμματα υψηλού επιπέδου MPC σε μια ενδιάμεση αναπαράσταση, επιτρέποντας αυτοματοποιημένη μίξη με επίγνωση διανυσματοποίησης σε πολλαπλά backends. Υλοποιούμε το πλαίσιο μας με τα MP-SPDZ και MOTION και το αξιολογούμε σε τυποποιημένα benchmarks. Πέρα από τα εμπειρικά κέρδη απόδοσης, παρέχουμε μια εγγύηση αποδεδειγμένης βελτιστότητας για τη μεθοδολογία μίξης μας και αποδεικνύουμε την πρακτική επιλυσιμότητα υπό φυσικές δομικές υποθέσεις στα προγράμματα εισόδου, επιλύοντας έτσι ένα πρόβλημα που είχε προηγουμένως θεωρηθεί ως NP-hard. Η συνεισφορά αυτή προάγει τόσο το συστημικό όσο και το θεωρητικό σκέλος του MPC, επεκτείνοντας την αποδεδειγμένα βέλτιστη μίξη πρωτοκόλλων στην περίπτωση πολλαπλών πρωτοκόλλων. Η τέταρτη συνεισφορά της διατριβής επεκτείνεται στο πεδίο των Non-Fungible Tokens (NFTs) και εισάγει το BarterSwap, ένα πλήρως αποκεντρωμένο πρωτόκολλο ανταλλαγής πολλαπλών NFTs χωρίς χρηματικά ανταλλάγματα, με στόχο την καθιέρωση μιας ανταλλακτικής οικονομίας (barter economy) στο περιβάλλον των blockchains. Το Barter-Swap βασίζεται στον αλγόριθμο Top Trading Cycles (TTC), επιτρέποντας τη δίκαιη, ταυτόχρονη και αμοιβαία επωφελή ανταλλαγή μεταξύ πολλών συμμετεχόντων χωρίς την ανάγκη ύπαρξης μεσολαβητή ή κεντρικού μηχανισμού εκκαθάρισης. Η υλοποίηση του πρωτοκόλλου στο Ethereum καταδεικνύει την πρακτική του βιωσιμότητα και αποδεικνύει ότι είναι εφικτή η δημιουργία ενός αποκεντρωμένου μοντέλου ανταλλαγής ψηφιακών περιουσιακών στοιχείων, το οποίο συνδυάζει ντετερμινιστική ορθότητα, ασφάλεια και άμεση εκτέλεση του αποτελέσματος εντός έξυπνων συμβολαίων, ενώ πραγματοποιεί ανταλλαγές χωρίς καμία χρήση χρηματικής αξίας, στηριζόμενο αποκλειστικά στις προτιμήσεις των χρηστών. Η πέμπτη συνεισφορά της διατριβής αφορά τον σχεδιασμό και την υλοποίηση ενός πλήρως αποκεντρωμένου συστήματος ανταλλαγής πληροφοριών κυβερνοαπειλών (Cyber Threat Intelligence – CTI), το οποίο συμμορφώνεται με τα πρότυπα STIX/TAXII και λειτουργεί πάνω σε ιδιωτικό δίκτυο Hyperledger Fabric. Η προτεινόμενη αρχιτεκτονική εισάγει μηχανισμούς οργανωσιακής αποκέντρωσης και ελεγχόμενης πρόσβασης, επιλύοντας πρακτικά ζητήματα ακεραιότητας, διαθεσιμότητας και μη αποποίησης ευθύνης που χαρακτηρίζουν τις κεντρικοποιημένες TAXII υποδομές.
Fatemeh Erfan, Mohammad Yahyatabar, Martine Bellaïche, Talal Halabi
• Created a refined, expanded, and precisely labeled dataset with explanations, risk assessments, and fixes for each vulnerability • Fine-tuned an open-source LLM: LLaMA-3.1-8B using parameter-efficient techniques (LoRA) for smart contract vulnerability detection • Fine-tuned GPT-4o-mini on the same corpus for comparative analysis • Developed a real-time Visual Studio Code (VSCode) plugin integrating GPT-4o-mini for smart contract auditing • Released the datasets, tool, and fine-tuned model to advance research in smart contract security Since the advent of Ethereum, ensuring the security of smart contracts has become imperative. Integer overflow and underflow, reentrancy, and timestamp dependency remain the three most prevalent vulnerabilities in deployed contracts. Existing static-analysis tools often yield insufficient accuracy, and datasets derived from them inherit the same shortcomings. Moreover, the smart contract ecosystem lacks a dependable, real-time auditing aid for developers and a fine-tuned model trained on a truly comprehensive corpus. In this paper, we present three main contributions. (1) Dataset curation: the state-of-the-art vulnerability datasets are aggregated and harmonized, producing a clean, fully labeled dataset that integrates detailed explanations, potential security risks, vulnerable line ranges, code snippets, and corresponding fixes. The dataset is publicly available via our GitHub repository. (2) Model fine-tuning: the LLaMA-3.1-8B model as well as GPT-4o-mini are fine-tuned on this corpus and evaluated with both standard classification metrics and text-quality measures. The fine-tuned LLaMA-3.1 model achieves a precision of 93.55%, an average semantic similarity of 77.48%, and a code similarity of 87.25%. (3) IDE integration: We implement a real-time Visual Studio Code extension, backed by the GPT-4o API, that highlights, explains, and automatically patches vulnerabilities as the developer writes. Together, these contributions deliver a rigorously validated model and a practical developer toolchain that markedly advance the state of smart contract security research and practice.
Global disruptions, such as the COVID-19 pandemic, have exposed the fragility of supply chains and the critical need for coordination. However, effective collaboration is often hindered by the reluctance of firms to disclose sensitive proprietary data, such as inventory levels or logistical bottlenecks, due to competitive concerns. To resolve this dilemma, this study introduces a privacy-preserving framework integrating Zero-Knowledge Proofs (ZKPs) with blockchain technology. This approach allows stakeholders to validate compliance and operational status without revealing the underlying raw data, thereby fostering trust and resilience in decentralized networks.
Every standard signature scheme enforces one property: only the key holdercan sign. What the key holder signs is unconstrained. Policy enforcement-- spending limits, rate limits, access control -- lives in smartcontracts, middleware, or governance: layers that can be upgraded,bypassed, or exploited. We call this the software-layer assumption:compliance holds only if the enforcing code is correct and unmodified. We eliminate this assumption. We introduce behavior-bound signatures(BBS), in which a policy constraint delta(x) < epsilon is committed atkey generation and enforced inside the signature's zero-knowledge proof.If the action violates the policy, the ZK constraint system isunsatisfiable -- no witness, no proof, no signature. This is not asoftware check. It is a mathematical impossibility. No software canoverride. Unlike policy-based signatures (where an authority imposes policy onsigners), BBS is self-committed: the signer binds their own futurebehavior at key generation, and even the signer cannot later violate orrevoke this commitment. We formalize this as policy-soundness (PS-CMA), a security modelstrictly stronger than EUF-CMA, and prove it under standard assumptions(Pedersen binding, Poseidon CR, ZK knowledge soundness). From thissingle primitive, five independent consequences follow -- not as separatedesigns, but as necessary implications of one cryptographic root: (A) Compliance safety under f <= n-1 Byzantine faults, decoupled from honest-quorum assumptions.(B) O(1) verification and audit via a single ZK check and Pedersen homomorphic aggregation.(C) Elimination of the virtual-machine execution layer for policy-constrained transactions.(D) A gasless ledger: branch C removes metering, while ZK-encoded rate limits make spam mathematically nonexistent.(E) The first cryptographic guarantee that a compromised autonomous AI agent cannot exceed its authorized behavioral envelope. Moreover, the zero-knowledge property ensures that complianceverification reveals neither the signer's identity nor the transactionparameters -- achieving regulatory compliance without identitydisclosure, complementary to existing ZK-KYC frameworks that verifystatic identity attributes.
Cloud computing has transformed data storage, accessibility, and enterprise operations; however, it has also increased exposure to sophisticated cyber threats. Traditional centralized Identity Management Systems (IDMs) often suffer from critical vulnerabilities such as a single point of failure, where the compromise of a central authority can expose sensitive user credentials. This research proposes ZKP-Shield, a security framework that integrates Non-Interactive Zero-Knowledge Proofs (NIZKPs) with a Software-Defined Perimeter (SDP) to create a secure and invisible cloud authentication environment. The proposed architecture eliminates the need to transmit passwords or sensitive identity data by allowing users to mathematically prove their identity without revealing secret information. Simultaneously, the SDP layer conceals cloud resources from unauthorized users by enforcing a “dark cloud” model, where services remain hidden until authentication is successfully verified. The framework employs cryptographic techniques such as the Discrete Logarithm Problem and the Fiat–Shamir heuristic to transform interactive proofs into efficient non-interactive authentication processes. Experimental simulations conducted in a distributed cloud environment demonstrate that the ZKP-SDP integration significantly reduces attack surfaces, prevents credential-based attacks, and maintains acceptable latency for enterprise applications. The results indicate that combining cryptographic identity verification with network invisibility provides a scalable and resilient security model for modern cloud infrastructures.
The use of Federated Learning (FL) in sensitive, multi-party settings has made it even more important to have trust, accountability and safe systems of governance have become increasingly critical in the context of Federated Learning (FL) being utilized in sensitive multi-party environments. FL defends locality of data, but still can be poisoned with models, tampered with malicious gradient modification and unstable client behaviour due to lack of trust verification. The current paper examines how integrating Bloackchain Technology into FL(BCFL) provides an unquestioned system of governance that facilitates clear client accountability, model provenance tracing and record tampering resistant record management. We integrate the architectural and cryptographic and consensus conditions that are necessary in the development of robust BCFL systems with a special focus on the lightweight and reputation based processes of consensus. As a case in point, we critically examine an example of solidity-based prototype, ReputationManager.sol, which executes a Proof-of-Reputation (PoR) mechanism in which the aggregation weight of each client in a model is determined by its past integrity. According to our review, we find there is a Centralization Paradox in that, despite the implementation being based on a decentralized ledger, the prototype is premised on singlet owner access control, delegating trustlessness to a centralized blockchain administrator, and creating a single point of failure. We achieve this by pointing out important future directions including decentralized PoR models, automatic reputation updates in the basis of cryptographically checkable conduct and the introduction of Zero-Knowledge Proofs to formulate privacy preserving, regulation conformable and truly trustworthy BCFL regulation.
Allu Venkata Sai, N. Purushotham, Sanapureddy Pallavi, Telugu Akula Nanda Sai · 6 authors
This paper presents a structured review of secure, cloud ready, multi tenant blockchain voting systems with an emphasis on privacy guarantees, deterministic finality, and end to end verifiability. We synthesize advances in consensus PoS BFT hybrids privacy preserving cryptography commtments, nullifiers, and zero knowledge proofs and operational isolation for multi tenant deployments. We analyz threat models spanning malicious voters, operators, and partialy Byzantine validator sets, and summarize design patterns that reduce manipulation risk while retaining auditabilty. The review highlights open challenges in coercion resistance, scalable proof systems, and governance. We conclude with a design checklist and research directions for practical, verifiable e voting at institutional scale.
Attiq Ur Rehman, Shuai Lü, Muhammad Usman, Zaheer Ahmad Gondal · 7 authors
Abstract The integration of Cloud computing with the Internet of Things (IoT) has greatly increased the scale and complexity of sharing data in Cloud-based Internet of Things (CIoT) environments. However, this growth brings challenges with regard to security of sensitive data, privacy, access control management, and accountability. In this paper, we propose Block-chain Enabled Secure and Anonymous Data Sharing (BS-ADS), which is a framework that tries to overcome these challenges by combining Aggregate Key Searchable Encryption (AKSE) and blockchain-based auditing, edge-assisted revocation and accountable anonymity. BS-ADS eliminates the need for centralized trust by using blockchain to store encrypted metadata such as ciphertext hash and revocation identifier. This provides trust and transparency which are decentralized without revealing plaintext data. The frame-work includes the support of linkable ring signatures and zero-knowledge proofs to maintain user privacy while allowing for traceability and therefore allowing for the detection of malicious behavior to take place without the need of the loss of anonymity. Furthermore, edge nodes are responsible for the encryption up-dates in case of revocation, and they remove the computational load from the resource-constrained IoT devices. The performance of BS-ADS was evaluated in terms of encryption overhead, ciphertext size, and the communication cost. Experimental results demonstrate the effectiveness of BS-ADS in terms of encryption overhead, which reduces the encryption overhead by 30–40%, while guaranteeing efficient keyword-based search and data retrieval. Additionally, it offers a 20–30% reduction in communication overhead in multi-user scenarios, maintaining constant communication cost per operation (O(1)). The system is also proven secure against selective chosen-ciphertext attacks (SIND-CCA), replay attacks, Sybil attacks, and man-in-the-middle attacks. This work introduces a novel architecture for secure, scalable, and accountable data sharing in CIoT, incorporating efficient revocation mechanisms and privacy-preserving search. BS-ADS provides significant improvements in efficiency, security, and privacy, offering a promising solution for large-scale CIoT applications.
Tuan Nguyen Kim, Ha Nguyen Hoang, Son Doan Trung, Lam Nguyen
Cloud computing has become a vital platform for large-scale data analytics, yet it poses significant privacy challenges when handling sensitive information, especially in healthcare and financial domains.Homomorphic Encryption (HE) enables computation on encrypted data, providing strong privacy guarantees, but traditional HE frameworks lack efficient query representation, do not protect query patterns, and cannot prove correctness of cloud-side computations.This paper proposes HE-Cloud, an integrated privacy-preserving framework that combines DSL-driven query compilation, HE, Zero-Knowledge Proofs (ZKP), and Oblivious RAM (ORAM).Our framework allows clients to express high-level analytical queries, securely executes them on encrypted data, protects query access patterns via ORAM, and returns verifiable results through ZKP.A proof-of-concept implementation using the Pima Diabetes dataset demonstrates feasibility: Average glucose computations can be performed entirely on encrypted data with sub-second latency for homomorphic operations and minimal accuracy loss (approximately 0.001).Scalable secure analytics, extendable to larger datasets and machine learning tasks.
Secure and bandwidth-conscious transmission of model updates is a central bottleneck in distributed machine learning. Existing secure aggregation and homomorphic encryption pipelines either reveal more than the task requires or incur prohibitive computation and communication costs. We introduce a verifiable functional encryption (VFE) framework that releases only the intended linear functions of client gradients while providing end-to-end integrity and privacy guarantees under standard lattice assumptions. Our instantiation, FlowAgg-FE, combines two novel components. First, KS-IPFE, a key-splittable inner-product FE scheme, supports per-round weighted aggregation, vector packing, and on-the-fly function changes without client re-encryption; function keys are distributed across two non-colluding helpers, eliminating a single point of trust and enabling lightweight, homomorphically verifiable tags on decrypted outputs. Second, PaS-Stream is a rate-adaptive encryption-and-compression pipeline that couples sketch-based gradient compression with batched FE ciphertext streaming, ensuring unbiased aggregation in the presence of stragglers and dropouts. We further bind client-side clipping to zero-knowledge range proofs and offer an optional differentially private release layer that composes with FE to yield (ε,δ)-privacy. A prototype based on LWE demonstrates practicality across cross-device and cross-silo training: client uplink is reduced by 1.9–3.4× and server CPU time by 1.6× versus state-of-practice encrypted secure aggregation, with accuracy within 0.3% of plaintext baselines and correctness preserved under up to 30% client dropout. These results show that verifiable FE can make secure, communication-efficient gradient transmission viable, as appropriate for theme of security and privacy in distributed machine learning of the Special Issue.