A polynomial commitment scheme (PCS) enables a prover to commit to a polynomial and later prove the correctness of its evaluation without revealing the polynomial. Although discrete logarithm-based PCSs offer succinct proofs, they are not quantum-safe. Lattice-based PCSs provide post-quantum security and additive homomorphism, making them suitable for applications such as zero-knowledge proofs and secure multiparty computation. In this article, we review two recent lattice-based PCSs, Greyhound and HyperWolf, both relying on the Module-SIS assumption but differing in target polynomial classes and proof techniques. In particular, Greyhound achieves a smaller proof size O(log log N) through folding and LaBRADOR proofs, while HyperWolf supports univariate and multilinear polynomials with lower verifier cost O(log N) using hypercube evaluation.
IoT devices constitute an important component of Industry 4.0 paradigm, but are greatly hindered by their inherent resource constraints. Resource sharing is therefore an essential operating requirement for these devices but lack of privacy and heavy reliance on centralized architectures pose a serious risk for stable functioning. Use of decentralized and high availability platforms like distributed ledgers can provide divergent and distributed networking conditions but leaves any inter-device interactions completely exposed to third party view. To resolve this, we utilize an innovative combination of smart contracts alongside a zero-knowledge proof generator, known as Tornado Cash, to align IoT devices on a distributed resource exchange platform with privacy guarantees. In concert with public-key cryptography, our solution provides a framework for resource constrained IoT machines to interact through the blockchain for resource exchange purposes with strong privacy guarantees for both devices. Absolute anonymity is ensured by the protocolâs inherent architecture, meaning that participant devices do not reveal any sensitive information and consequently it becomes nontrivial to breach the privacy of either participant. Performance evaluations performed by testing ZeKSA against a competing & comparatively vulnerable protocol yield promising outcomes in terms of blockchain metrics like gas usage & incurred transaction costs.
Cross chain interoperability in blockchain systems exposes a fundamental tension between user privacy and regulatory accountability. Existing solutions enforce an all or nothing choice between full anonymity and mandatory identity disclosure, which limits adoption in regulated financial settings. We present VeilAudit, a cross chain auditing framework that introduces Auditor Only Linkability, which allows auditors to link transaction behaviors that originate from the same anonymous entity without learning its identity. VeilAudit achieves this with a user generated Linkable Audit Tag that embeds a zero knowledge proof to attest to its validity without exposing the user master wallet address, and with a special ciphertext that only designated auditors can test for linkage. To balance privacy and compliance, VeilAudit also supports threshold gated identity revelation under due process. VeilAudit further provides a mechanism for building reputation in pseudonymous environments, which enables applications such as cross chain credit scoring based on verifiable behavioral history. We formalize the security guarantees and develop a prototype that spans multiple EVM chains. Our evaluation shows that the framework is practical for today multichain environments.
The generation and exchange of diverse e-health records, such as Personal Health Records (PHRs) and Electronic Medical Records (EMRs), have become increasingly critical in supporting comprehensive clinical decision-making across healthcare institutions. While significant progress has been made in securely and efficiently sharing these records, current solutions often struggle to handle multiple types of e-health data simultaneously. Moreover, a patient-centric approach, which balances ease of use for patients with the need to ensure their privacy, remains a critical challenge that requires further exploration. In this paper, we propose a decentralized, IoT-enabled e-health data-sharing model leveraging blockchain and cloud technologies, designed to support both PHRs and EMRs. Our model incorporates advanced security features, including zero-knowledge proof, elliptic-curve cryptography, and decentralized access control, to ensure a practical, secure, and privacy-preserving system. We simulate a real healthcare environment to demonstrate its practical feasibility, and performance evaluations show our systemâs superior efficiency and enhanced security compared to existing solutions.
The verifiability of machine learning models and the privacy of training data have become critical concerns due to their widespread deployment in sensitive applications. Ensuring that a model performs as claimed, without revealing private data or algorithms, is a significant challenge. Zero-Knowledge Proof systems (ZKPs) have emerged as a promising cryptographic solution, enabling the verification of statements without disclosing underlying information. Their integration with blockchain technology further enhances trust and decentralization, offering robust solutions for secure and transparent AI systems. This paper explores the use of ZKPs in machine learning, focusing on privacy-preservation techniques, model verifiability, and confidential AI. It compares the differences and challenges of employing ZKPs in machine learning versus blockchains, highlighting their unique requirements and overlapping benefits. We review the basic concepts of ZKPs, advances such as zkSNARKs and zk-STARKs, and their applications in blockchainbased AI frameworks to ensure data integrity, immutability, and scalability. Furthermore, the paper delves into the practical implications of using ZKPs in AI, providing case studies and analyzing their scalability, performance, and limitations. We conclude by identifying key challenges and presenting future research directions to extend the applicability of ZKPs in AI, particularly in federated learning, model fairness, and decentralized AI pipelines.
Electronic voting (e-voting) has become an essential topic in the modernization of democratic systems, with promises of accessibility, faster counting, and reduced logistical challenges compared to traditional paper ballots. Yet, widespread adoption has been hindered by persistent trust and security concerns. Vulnerabilities such as malware, server compromise, insider threats, and limited verifiability have generated skepticism regarding the integrity of e-voting platforms. Blockchain technology has emerged as a disruptive innovation capable of reshaping this discourse. Its intrinsic propertiesâimmutability, decentralization, transparency, and consensus-driven validationâdirectly address many of the fundamental challenges associated with securing digital elections. This manuscript provides a comprehensive exploration of blockchain-based electronic voting, with particular emphasis on the trust and security challenges that shape its practical deployment. Drawing on global case studies, theoretical models, and simulation insights, the research examines how blockchain can ensure tamper resistance, facilitate end-to-end verifiability, and empower voters through transparent audit trails. Key challenges such as scalability bottlenecks, voter anonymity risks, usability barriers, and regulatory gaps are analyzed in depth. The results indicate that hybrid blockchain architectures, which integrate advanced cryptographic techniques such as zero-knowledge proofs, homomorphic encryption, and sharding, hold promise for balancing the competing demands of scalability, privacy, and trust. Furthermore, blockchain must be supported by strong institutional frameworks, inclusive accessibility measures, and continuous technical audits to achieve legitimacy in electoral processes. By systematically mapping both the opportunities and limitations, this research contributes to the ongoing discourse on how technology can strengthen democratic resilience in the digital era. Ultimately, blockchain-enabled voting should be regarded not as a replacement but as an augmentation of existing systems, combining the strengths of distributed technologies with constitutional safeguards to advance secure, transparent, and inclusive electoral participation.
The vulnerabilities in this vigorous digital landscape are taking a more sophisticated shape as the nature and danger of cyber threats and the nature of cyber threats are taking new forms as a zero-day attack, polymorphic malware, insider threat and advanced social engineering methodologies and where traditional reactive security measures are no longer relevant. This issue of organizations detecting the threat, and that they need to mitigate the threat in real time is rather of a challenge in the light of the fact the behaviour of the adversaries is very much similar to that of legitimate user behaviour and as such will create ambiguity issues that will make an organization believe that it has hit a false positive or missed a threat. A proactive approach to cyber threat countering, the Adaptive AI-Driven Autonomous Threat Hunting (ADCH) Framework, is the focus of this paper as it will be able to monitor, analyse, and mitigate the development of the new threats far before it becomes reality. Behavioural profiling and reinforcement learning are employed at ADCH to differentiate between innocent and harmful actors on the fly even in the ambiguous or complex situation, and ethical precautions are taken so that the deepfake interaction modules are tightly regulated and privacy safeguarding. The framework brings together automated threat intelligence extraction where Indicators of compromise (IoCs) are extracted, classified and pooled across different sources and offer rapid and actionable information. Additionally, ADCH is compatible with Siem and SOAR, automates the incident response and mitigation process to minimise latency and dependency on people. Secure logging provides auditability resistant to tampering and transparent recording of events without de-anonymization of sensitive operational data, with blockchain used to enforce the use of permissioned ledger systems, smart contracts, and zero-knowledge proofs. The results of simulation testify that ADCH has been doing consistently well in terms of detection performance and accuracy, precision, recall and F1 scores, the results stand at $80-90$ and far better than the traditional threat hunting systems. Autonomous AI-controlled detection, ethical simulation, automated intelligence extraction, coordinated response, and blockchain-based logging can be combined to help in establishing a strong and intelligent paradigm of defense against the emergent and advanced cyberspace threats and ensure the safety of the digital infrastructures, transparency, accountability and ethical standards.
The integration of Internet of Things (IoT) technologies into public healthcare enables continuous monitoring and sustainable health management. However, conventional frameworks often depend on transmitting and storing raw personal data on centralized servers, posing challenges related to privacy, security, ethical compliance, and long-term sustainability. This study proposes a privacy-preserving framework that avoids the exposure of true health-related data. Sensor nodes encrypt collected measurements and collaborate with a secure computation core to evaluate health indicators under homomorphic encryption, maintaining confidentiality. For example, the system can determine whether a patientâs heart rate within a monitoring window falls inside clinically recommended thresholds, while the framework remains general enough to support a wide range of encrypted computations. A compliance verification client generates zero-knowledge range proofs, allowing external parties to verify whether health indicators meet predefined conditions without accessing actual values. Simulation results confirm the correctness of encrypted computation, controllability of threshold-based compliance judgments, and resistance to inference attacks. The proposed framework provides a practical solution for secure, auditable, and sustainable real-time health assessment in IoT-enabled public healthcare systems.
ABSTRACT Blockchain technology has emerged as a pivotal solution for securing sensitive data across various domains, including artificial intelligence (AI), supply chain management, cloud computing, and healthcare. Its core attributes, confidentiality, decentralization, security, and privacy, offer significant advantages to the healthcare sector. The integration of Internet of Things ( IoT ) devices within healthcare systems enhances interoperability, enabling seamless communication between healthcare software and IT infrastructure. However, traditional healthcare systems face persistent security challenges, including phishing, masquerading, and identity theft. To address these issues, we propose a secure blockchainâbased decentralized application for generating, maintaining, and validating medical certificates. This application facilitates secure interactions among healthcare entities, including hospitals, patients, and IoT devices, while ensuring confidentiality, authentication, and access control through smart contracts. The proposed blockchain architecture enhances data integrity and secure transmission using the zeroâknowledge proof ( ZKP ) mechanism. Additionally, we incorporate the interplanetary file system ( IPFS ) for offâchain data storage to optimize storage costs and enhance security through Ethereum smart contracts. Performance evaluations demonstrate the effectiveness of our approach in mitigating existing security vulnerabilities, thereby offering a robust and scalable solution for secure healthcare data management.
Namrata Mishra, P. K. Chidambaram, Hassan Mohamed Mahdi, Arumalla Spandana ¡ 7 authors
SSI is a quickly appearing paradigm to secure and user-sovereign digital identity management. Nevertheless, existing implementations of SSI still have privacyprotection, interoperability, anti-fraud, and anti-cryptographic resiliency weaknesses. To tackle these issues, this paper presents a proposal of an AI-enhanced, blockchain-based protocol incorporating the use of Zero Knowledge Proofs (ZKP), Multiple Layer Decentralization (MLD) as well as quantumresistant cryptography. The framework uses AI to do dynamic Identity verification and real-time fraud detection, risk-based authentication and provides great advantage to traditional SSI models. The system proposed will utilize ZKPs to provide its users with privacy-preserving authentication so that one can confirm attributes but not reveal sensitive personal data. Multi-layer decentralized identity validation structure is developed to enhance the level of trust, reduce dependence on centralized authorities and enhances/supported interoperability across homogeneous systems. Ancillary, postquantum cryptographic schemes will also be incorporated to protect identities by mitigating the possible quantum computing attacks. Experimental evidence shows that our framework significantly enhances the accuracy of verification, the authentication latency and increases security in comparison to centralised and federated identity management solutions. The scheme is very flexible in financing sector, cross boundaries identity, e-governance portals and Web3 online portals. In the end, this study leads to an increment of a scaleable and privacy-sensitive digital identity system because it bridges existing security, usability and compliance gaps and opens pathways to robust and resilient SSI implementations into the future.
With the proliferation of decentralized applications (DApps), the conflict between the transparency of blockchain technology and user data privacy has become increasingly prominent. While Decentralized Identity (DID) and Verifiable Credentials (VCs) provide a standardized framework for user data sovereignty, achieving trusted identity verification and data sharing without compromising privacy remains a significant challenge. This paper proposes a novel, comprehensive framework that integrates DIDs and VCs with efficient Zero-Knowledge Proof (ZKP) schemes to address this core issue. The key contributions of this framework are threefold: first, it constructs a set of strong privacy-preserving protocols based on zk-STARKs, allowing users to prove that their credentials satisfy specific conditions (e.g., "age is over 18") without revealing any underlying sensitive data. Second, it designs a scalable, privacy-preserving credential revocation mechanism based on cryptographic accumulators, effectively solving credential management challenges in large-scale scenarios. Finally, it integrates a practical social key recovery scheme, significantly enhancing system usability and security. Through a prototype implementation and performance evaluation, this paper quantitatively analyzes the framework's performance in terms of proof generation time, verification overhead, and on-chain costs. Compared to existing state-of-the-art systems based on zk-SNARKs, our framework, at the cost of a larger proof size, significantly improves prover efficiency for complex computations and provides stronger security guarantees, including no trusted setup and post-quantum security. Finally, a case study in the decentralized finance (DeFi) credit scoring scenario demonstrates the framework's immense potential for unlocking capital efficiency and fostering a trusted data economy.
Zero-knowledge proofs (ZKPs) are increasingly adopted in practical cryptographic systems, yet zkSNARK generation remains computationally expensive, limiting scalability. Recent distributed zkSNARK frameworks, such as zkSaaS and Siniel, mitigate this cost by partitioning witnesses across multiple workers. However, they often depend on heavy MPC interactions and full verifier-side proof checking, which hinders their usability in asynchronous or large-scale settings. We present CoVer, a novel distributed zkSNARK system over binary fields, optimized for hardware-level parallelism. CoVer introduces a verifier-guided VOLE-based challenge mechanism that enforces global constraint consistency across subproofs while removing multi-round MPC and tag consistency checks. This design reduces communication and prevents challenge manipulation. Experiments show CoVer achieves up to$150 \times$verification efficiency improvement under variable bandwidth conditions.
Feng Wang, Shuo Yang, Min Zhang, Yang Liu ¡ 6 authors
In decentralized ecosystems, Decentralized Identifiers (DID) and Verifiable Credentials (VC) enable self-sovereign identity, cross-domain interoperability, and privacy-preserving data exchange. However, current VC models face critical limitations, including static attribute binding, inefficient updates, high on-chain verification costs, and privacy leakage. To address these issues, this paper proposes a Dynamic Attribute-oriented Verifiable Credential (DAVC) model, designed to support flexible attribute lifecycle management. The model adopts a three-layer architecture that combines minimal on-chain anchoring, off-chain attribute decoupling, and hierarchical recursive verification to enable efficient and scalable identity verification. First, the onchain layer introduces Sparse Merkle Tree (SMT) root hashes to reduce the need for recording off-chain attribute statuses. Second, the off-chain layer achieves semantic isolation between attributes and identities through an Anonymous Attribute Identifier (AID) mechanism, while improving update efficiency via path caching and incremental strategies. Finally, a Hierarchical Recursive Zero-Knowledge Proof (HR-ZKP) mechanism, based on the Halo2 framework, achieves logarithmic complexity in multiattribute proof generation, supporting attribute-level minimal disclosure and structural anonymization. Experimental results demonstrate that DAVC maintains constant on-chain storage, significantly reduces gas consumption, keeps proof sizes within reasonable limits (e.g., 2.9KB for 10 attributes), and achieves proof generation delays within hundreds of milliseconds. Overall system performance exhibits logarithmic growth as the number of attributes increases. The DAVC model achieves a balance between strong privacy protection, high composability, and dynamic identity expression through minimal on-chain data usage and closed verification paths. This provides a valuable reference for composable identity authentication in Web3 scenarios.
Tradicionalne metode preverjanja prisotnosti, kot so roÄno beleĹženje ali QR kode, so podvrĹžene manipulaciji in ne zagotavljajo zadostne varnosti ter zasebnosti uporabnikov. Magistrsko delo naslavlja te izzive z razvojem decentraliziranega sistema za preverjanje fiziÄne prisotnosti, ki temelji na tehnologiji verige blokov in niÄelno spoznavnih dokazih (zk-SNARK). Sistem integrira ZoKrates ogrodje za generiranje zasebnih dokazov, geolokacijsko verifikacijo z GPS koordinatami, Ethereum pametne pogodbe ter hibridni pristop k shranjevanju podatkov. Implementirani so bili veÄplatformski uporabniĹĄki vmesniki (spletna in mobilna aplikacija) z razliÄnimi naÄini potrjevanja prisotnosti. Razvita reĹĄitev predstavlja funkcionalen in robusten sistem, ki omogoÄa varno ter transparentno preverjanje prisotnosti brez razkrivanja osebnih podatkov uporabnikov.
In recent years, zero-knowledge proofs have made great strides in efficiency, on two fronts: minimizing the finite field size without sacrificing security and allowing more constructs in their intermediate representations, such as look-ups. These have lead to novel, efficiently aritmetizable, hash functions, which are the backbone of modern proof systems. We take advantage of these improvements and present a resource constrained hardware design for the Monolith hash function, targeted torwards embedded, edge IoT devices. The hardware implementation demonstrates a significant performance advantage over the software-only solution on the Zynq SoC, achieving a 22Ă speed-up while consuming only a few hundred milliwatts.
Cryptographic Implementations and Security
Cryptography and Residue Arithmetic
Physical Unclonable Functions (PUFs) and Hardware Security
The rapid adoption of Internet of Things (IoT) in Healthcare has significantly enhanced real-time patient monitoring and decision making. However, security and privacy still remain the major concern due to sensitive medical data of patients especially on low-power IoT devices. Traditional authentication schemes like Zero Knowledge Proof (ZKP) and Elliptic Curve Cryptography (ECC) often struggle with efficiency in resource-constraint environments due to their computational overhead. In order to address these challenges, we propose a Neural-Based Hybrid and Adaptive Framework that combines Schnorr ZKP with Kyber-based key encapsulation, using a neural network to dynamically select Kyber variants (512, 768, 1024) based on device parameters (type, authentication time, transmission time) to balance security and efficiency for low-power IoT devices. Extensive experiments validated robust security against replay and spoofing attacks, achieving authentication success for legitimate clients and zero attack successes. Furthermore, our proposed framework outperforms traditional Kyber1024 and ZKP/ECC based authentication schemes in terms of authentication time and computational overhead making it robust and scalable solution for sensitive and resource-limited environments like HealthCare IoT systems. ⢠Design a novel hybrid authentication framework that integrates ZKP and Kyber PQC (Post Quantum Cryptography) to ensure secure and efficient authentication for IoT healthcare devices. ⢠Utilize Neural Network to intelligently select the most appropriate Kyber variant based on threat level and computational efficiency. ⢠Provide comprehensive experimental analysis comparing the proposed hybrid and adaptive framework with traditional ZKP, ECC and static Kyber implementations.
In the contemporary online interaction, digital iden- tity is central, but most systems follow a centralized provider like Google or Facebook. Although convenient, these models pose ma- jor risks to data breach, surveillance, and single point of failure. The proposed paper proposes a privacy-sensitive decentralized identity verification system that uses Zero-Knowledge Proofs (ZKPs) to allow users to make it through claims (e.g., age, citizen- ship, or enrollment) without any personal data being provided, thereby solving the challenges outlined in the paper. Our architecture integrates verifiable credentials, zero-knowledge-SNARKs and an issuer trust registry that is maintained on blockchain smart contracts to guarantee trustless verification and eliminate dependence on centralized authorities. Its system gives its users full control over credentials, allows reuse across applications, and does not expose data.A proof-of-concept implementation has shown the approach to be feasible with Circom and SnarkJS to generate proof, Solidity smart contracts to verify them, and a user interface implemented in React. Results of the evaluations showed that determination of the verification latency is low, the overhead to generate proofs is moderate, and no privacy leakage occurs. It can be used in potential applications such as education, e-governance, healthcare, finance, and online voting.
Zero-knowledge proof (ZKP) applications require translating high-level programs into arithmetic circuitsâa process that demands both correctness and efficiency. While recent DSLs improve usability, they often yield suboptimal circuits, and hand-optimized implementations remain difficult to construct and verify. We present Tabby, a synthesis-aided compiler that automates the generation of high-performance ZK circuits from highlevel code. Tabby introduces a domain-specific intermediate representation designed for symbolic reasoning and applies sketch-based program synthesis to derive optimized low-level implementations. By decomposing programs into reusable components and verifying semantic equivalence via SMT-based reasoning, Tabby ensures correctness while achieving substantial performance improvements. We evaluate Tabby on a suite of real-world ZKP applications and demonstrate significant reductions in proof generation time and circuit size against mainstream ZK compilers.
Abstract Artificial intelligence (AI) systems are rapidly approaching capabilities that require an increasing level of human control. Existing AI alignment techniques remain opaque, model-specific, and vulnerable in human-level AI, or post-quantum scenarios. To address these issues, this paper proposes a novel AI alignment system architecture in which AI alignment rules are encoded as immutable smart contracts on a blockchain. The blockchain, in turn, is governed by a Proof of Personhood (PoP) consensus mechanism that only admits human agents to the rule validation processes. To protect the privacy of human agents in the identity verification process, the proposed AI alignment system facilitates techniques such as key derivation functions and asymmetric encryption of biometric data. In addition, this system also utilizes blockchain-based decentralized identity (DID) and zero-knowledge proofs (ZKPs). To ensure privacy in post-quantum scenarios, biometric data are linked to zk-STARKs. The proposed AI alignment system is formally described to capture human and AI agents, verification, authentication, and Sybil resistance. The AI shield, a reactive system that prevents unsafe actions by an AI agent that would violate predetermined conditions, enforces the blockchain-based AI alignment rules in real-time, independently of the underlying AI model. Thus, the contribution of this paper is a conceptual framework for the implementation of blockchain technology that utilizes a PoP-based consensus mechanism and zk-STARKs to foster privacy-friendly societal involvement and public auditability of AI developments, providing a democratically governed AI alignment layer applicable to current and future AI models, including those in a post-quantum era.
The evolution of 5G and emerging 6G networks has introduced unprecedented opportunities for connectivity, but also expanded the attack âsurface for Distributed Denial of Service (DDoS) amplification attacks. Service-Based Architecture (SBA), network slicing, and massive âIoT (mMTC) environments create new vectors for reflection and amplification, making conventional defenses inadequate. This paper proposes a novel layered defense framework that integrates edge filtering, AI-driven anomaly detection, slice isolation, cloud scrubbing, and quantum-safe cryptography to mitigate DDoS amplification attacks in 5G/6G environments.â The framework is theoretically modeled through equations for amplification, mitigation efficiency, resilience, and defense cost, and evaluated experimentally using simulated signaling floods, IoT-driven amplification, slice-targeted floods, and hybrid attacks. Performance was âmeasured using detection rate, false alarm rate, service availability, resilience score, and resource overhead. Two algorithmsââpseudonymous authentication with zero-knowledge proof (ZKP) and layered mitigation orchestrationâwere implemented to operationalize âthe defense strategy.â The results demonstrate that the proposed framework achieves a detection accuracy of 95â97%, reduces false positives to 2%, and maintains âa service availability of over 85% under prolonged amplification attacks. It scales efficiently in scenarios with up to 10,000 simulated IoT âdevices, retaining 70â80% throughput, and maintains URLLC latency below 10 ms, outperforming baseline defenses (firewalls, scrubbing, âand AI-only) and state-of-the-art defenses from the literature. These findings validate the framework as a scalable, efficient, and future-ready âsolution for mitigating amplification attacks in 5G/6G networks, with strong alignment with 3GPP, GSMA, and NIST post-quantum standardsâ.
Open access
Advanced Malware Detection Techniques
Network Security and Intrusion Detection
Physical Unclonable Functions (PUFs) and Hardware Security
In this report, we compare the performance of our UltraGroth-based zero-knowledge machine learning framework Bionetta to other tools of similar purpose such as EZKL, Lagrange's deep-prove, or zkml. The results show a significant boost in the proving time for custom-crafted neural networks: they can be proven even on mobile devices, enabling numerous client-side proving applications. While our scheme increases the cost of one-time preprocessing steps, such as circuit compilation and generating trusted setup, our approach is, to the best of our knowledge, the only one that is deployable on the native EVM smart contracts without overwhelming proof size and verification overheads.
This paper presents a privacy-preserving intrusion detection architecture tailored for smart home environments, addressing the dual challenge of maintaining data confidentiality while enabling accurate anomaly detection. The proposed system replaces conventional raw data analysis with a proof-driven mechanism leveraging Zero-Knowledge Proofs (ZKPs). Behavioral patterns from smart devices such as motion sensors, door contacts, and environmental monitors are abstracted into cryptographic representations, which are then processed by a zk-SNARK-compatible machine learning model. Inference results are accompanied by cryptographic proofs verifying the correctness of each decision without disclosing the input data. A private blockchain layer, implemented using Ethereum smart contracts, records event hashes, proof metadata, and decision outcomes to ensure tamper-evident logging and automated response handling. Experimental simulations on synthetic home automation datasets demonstrate that the architecture achieves over 92% anomaly detection accuracy while ensuring zero exposure of raw sensor streams. The system also exhibits low-latency proof generation (~400 ms) and end-to-end response time under 1.2 seconds, confirming its suitability for real-time smart home applications.