Mario Rusev, Rafael Schmidt, Edward Lambe, Christian Schmieder · 5 authors
International organizations including the Bank for International Settlements (BIS) have adopted SDMx (The standard for Statistical Data and Metadata) as the standard for exchanging official statistics. Trust in published data is essential for evidence-based policymaking. This paper shows how binding each SDMx dataset to its source using blockchain technology can enhance confidence in official statistics. We present a proof of concept implemented on the XRP Ledger (XRPL) and contribute, as an integrated whole, (i) an SDMx-native canonicalization and per-< Series > hashing pipeline, (ii) a domain-separated Merkle aggregation scheme for batched anchoring, (iii) a self-contained, identity-bound verification artefact in which the SDMx message itself carries both the ordered Merkle leaves and a W3C Verifiable Credential signed by a publisher identity key cryptographically bound to the publisher’s XRPL address via an on-chain attestation registry, so any consumer can re-derive the anchored root and verify the publisher’s identity from the file alone plus a single ledger lookup, (iv) an open-source XRPL-based reference implementation, and (v) a cost model that captures the batch-size / latency / fee trade-off and is solved for an economically optimal batch size. The system enables near-real-time data verification, provides cryptographic integrity guarantees, and establishes a foundation for future extensions, including zero-knowledge proofs and automated verification by AI agents. Measurements on the prototype show median publication latency of 3–5 s and verification latency of 1–2 s under the controlled test conditions described in Section 7. The approach is data-format-agnostic and can be extended to other structured statistical or regulatory formats.
Cybersecurity is one of the most pressing concerns with regard to autonomous systems' ever-increasing adoption across multiple sectors, including transportation, health care, and smart city developments. The aim of this chapter is to focus on the various methods of securing autonomous systems through artificial intelligence (AI)-powered intrusion detection systems (IDS) and privacy-preserving mechanisms. For example, this chapter will explore the use of machine learning for anomaly detection as well as secure federated learning and blockchain technology to enhance the integrity of data in autonomous systems. Furthermore, it will provide an overview of the use of adversarially attacking AI models and provide recommendations for reducing cyber risk. Utilizing AI-based security frameworks, autonomous systems can identify threats and respond to them almost instantly, while ensuring user privacy. Finally, this chapter addresses the regulatory hurdles surrounding autonomous technology, as well as potential areas for future research related to security in autonomous systems.
E-Healthcare Systems (EHS) are transforming medical service delivery by enabling real-time data sharing, remote diagnostics, and integrated care via IoT and cloud infrastructures.However, the increasing volume of sensitive medical data being transmitted over distributed systems creates serious privacy and security concerns.This article reviews several papers on the EHS Data Privacy Framework, addressing critical issues such as illegal data access, identity exposure, and data integrity breaches.This review examines the existing data privacy frameworks used in EHS, focusing on four domains: traditional EHS, cloud-based EHS, IoT-based EHS and blockchain-based EHS with an emphasis on author, year, objective, and limitation.This framework provides a scalable and interoperable approach to protecting privacy for future healthcare systems.
The rapid growth of cybercrime, ransomware attacks, digital fraud, and large-scale cyber threats has significantly increased the need for secure and collaborative cyber forensic investigations. Traditional machine learning approaches often require organizations to share or centralize sensitive forensic datasets, creating challenges related to privacy, confidentiality, data ownership, and security. To address these limitations, this project proposes a PrivacyPreserving Distributed Training Architecture for Cyber Forensics using Blockchain and Homomorphic Encryption. The proposed framework integrates Federated Learning, Distributed Learning, CKKS-based Homomorphic Encryption, Blockchain Technology, and a Secure Model Exchange Space to enable multiple agencies to collaboratively train machine learning models without exposing their raw forensic data. Federated Learning allows organizations to train models locally and securely aggregate encrypted model updates, while Distributed Learning enables encrypted dataset partitions to be processed collaboratively by helper nodes without revealing the original data. CKKS Homomorphic Encryption protects sensitive information during computation, and blockchain technology provides decentralized trust through secure node authentication, transparent validation, immutable audit trails, and trusted model exchange among participating agencies. The framework is implemented using Python, Flask, Scikit-learn, TenSEAL, Ganache, Solidity, and Web3.py, providing a web-based platform for collaborative project management, encrypted training, blockchain monitoring, secure model sharing, performance evaluation, and cyber forensic prediction. Experimental results demonstrate that the proposed architecture successfully supports secure collaborative learning, encrypted computation, blockchain-based validation, and trusted model sharing while maintaining effective prediction performance. By integrating distributed learning, federated learning, homomorphic encryption, and blockchain into a unified framework, the proposed system provides a scalable, secure, and privacy-preserving solution for next-generation cyber forensic intelligence, enabling organizations to collaboratively strengthen cybersecurity without compromising the privacy, confidentiality, or ownership of sensitive forensic data
Federated Learning (FL) enables collaborative model training across decentralized participants without sharing raw data. However, existing FL systems remain vulnerable to Byzantine attacks and suffer from a lack of accountability, verifiability, and economic incentives for honest participation. We present BFL-Guard, a novel blockchain-orchestrated federated learning framework integrating: (i) zk-SNARK-based zero-knowledge gradient proofs, (ii) an on-chain Byzantine-tolerant aggregation smart contract, and (iii) a tokenized incentive protocol (FedToken). BFL-Guard stores model checkpoints as IPFS hashes anchored on Ethereum, ensuring tamper-evident auditability. Experiments on CIFAR-10 and Shakespeare benchmarks demonstrate 95.2% and 87.6% accuracy in IID and Non-IID settings, surpassing all baselines while converging 12.4% faster even under 30% Byzantine injection.
The security and privacy of blockchain data have become critical research challeSimilarly, the full-function accounting node verifies the validitynges. While numerous approaches have been proposed to address these concerns, many existing schemes suffer from high computational complexity or excessive verification latency. To bridge this gap, this paper presents a secure and privacy-preserving blockchain data transaction verification system. By integrating the Paillier cryptosystem with a zero-knowledge range proof protocol, the proposed system ensures the confidentiality of transaction amounts and participant identities, simultaneously achieving strong anonymity and conditional traceability for users. Moreover, fully functional accounting nodes support efficient ciphertext-domain balance updates, eliminating the need for decryption during accounting operations. Experimental evaluation confirms the practicality and high performance of the proposed system.
Andrea Rizzini, Marco Esposito, Tommaso Gagliardoni, Francesco Bruschi
Federated Learning (FL) is an advancement in Machine Learning motivated by the need to preserve the privacy of the data used to train models. While it effectively addresses this issue, the multi-participant paradigm on which it is based introduces several challenges. Among these are the risks that participating entities may behave dishonestly and fail to perform their tasks correctly. This misbehavior, in turn, also threatens privacy, because an undetected deviation in training or aggregation can silently undermine the confidentiality guarantees that FL was designed to provide. This motivates mechanisms that provide checkable evidence that released checkpoints are consistent with a declared learning specification and an auditable execution trace. In this SoK, we model federated learning as an append-only transcript of submissions, admissions, aggregation, and finalization events, and formalize verifiability as a collection of integrity claims issued by clients and the aggregator, and checked by different verifier classes. We derive a taxonomy of recurring client-side and aggregator-side claims and use it to analyze representative verifiable FL (VFL) systems spanning Zero-Knowledge Proofs (ZKP) and Trusted Execution Environment (TEE) technologies. Our analysis suggests that, while verifiable aggregation is comparatively mature, data verifiability appears feasible but still sparsely adopted in practice, and verifiable training remain costly and rarely scale to modern models.
Context The exponential evolution and widespread integration of Artificial Intelligence (AI) and Machine Learning (ML) systems have fundamentally transformed industries, establishing AI as a central component in decision-making processes, task automation, and the optimization of complex operational pipelines. From healthcare diagnostics to financial forecasting and increasingly across critical cybersecurity infrastructure such as intrusion detection systems and malware classifiers, AI models are being deployed in environments where the correctness and authenticity of their outputs carry direct operational and safety consequences. Nevertheless, as the deployment of AI systems becomes widespread, the conditions under which these models are trained have evolved in a direction where the security landscape of them radically changes. The traaditional assumption of a centralized, fully controlled training environment, where a single trusted entity acquires data, trains the model, and deploys it, no longer reflects the reality of modern machine learning practice. The frequent use of remote sensing, federated learning and/or outsourced machine learning has introduced architectures where the entity that acquires the data, the entity that trains the model and the entity that ultimately relies on the model's output are three distinct and mutually distrusting parties. In a remote sensing scenario, sensors owned by a data provider transmit raw measurements to a training node that may be geographically or administratively distant. In a federated learning scenario, multiple decentralized devices train local models on their private data and submit the results to a central aggregator. In an outsourced learning scenario, a resource-constrained model sponsor delegates the training computation entirely to a third-party cloud provider. In all three cases, the common factor is the same: the model sponsor, the entity that is ultimately responsible for and dependent on the trained model, that does not control the data acquisition process, does not observe the training execution and has no native mechanism to verify that the model they receive is the result of the computation they requested, performed on the data they provided. This separation of control is the main focus addressed by this dissertation. It is not merely a theoretical concern: the literature has documented a wide range of attacks that exploit precisely this gap. When a malicious trainer substitutes data, alters labels, ignores some dataset's subsets or modifies model parameters, the resulting model may appear functionally correct on standard evaluation metrics while being systematically compromised for specific classes of input, an attack vector particularly dangerous in cybersecurity applications where a model that has been quietly trained to misclassify a specific type of malicious traffic provides no observable anomaly until the attack it was designed to hide occurs. Problem and Motivation The main motivation of this dissertation can be addressed as follows. Given a sensor, that produces a set of data points in a given time frame, or a dataset owned by a data provider and a model computed by a model trainer from that data, the model sponsor wants to ensure that the trained model is the result of executing a known training process over the complete and authenticated dataset $D_t$. That is, all data points in $D_t$ and only those data points were used as the training set. No modifications were made to those points or their labels and the obtained model is indeed the result obtained from the execution of the agreed training algorithm. This guarantee cannot be provided by standard Machine Learning procedures, like accuracy, precision or F1-score. A malicious trainer can submit a model that passes all the standard evaluation metrics on benign inputs while maintaining a targeted misclassification on a specific attack pattern. The only way to close this gap is to make the training process itself verifiable by requiring the trainer to produce and submit a cryptographic proof that is mathematically impossible to forge without having correctly executed the agreed computation on the authenticated data. This verification challenge comes together with a second problem, the \emph{model integrity gap} that exists between a trained model and its deployed representation. Even if the training process was all validated, the model must subsequently be transpiled and deployed into a certain non-ML format. In the context of this dissertation, this gap is particularly sensitive, the Python model trained by the data scientist must be translated into a ZoKrates arithmetic circuit for zero-knowledge proof generation, a process that involves converting continuous floating-point decision boundaries into discrete integer arithmetic. If this translation introduces a small inversion in a comparison operator or a shifted threshold values, the deployed circuit will produce systematically different predictions from the intended model and standard testing may not surface the discrepancy. The literature has proposed cryptographic solutions to the verifiable training but has largely left the second problem unaddressed. The foundational work by Keshavarzkalhori et al. demonstrated that it is possible to construct a pipeline combining hash chains, digital signatures and zero-knowledge proofs to verify that a simulated Naive Bayes classifier was trained on authenticated sensor data. Their implementation, built on the ZoKrates toolset, provided a proof-of-concept that the building blocks exist for end-to-end training verification. However, scaling this approach from a simple probabilistic classifier to a more complex, non-linear ensemble model, in this specific case, a Random Forest, introduces severe architectural bottlenecks that their work explicitly identified as open problems: the computational overhead of bitwise hashing inside arithmetic circuits, the floating-point to integer translation problem and the absence of any mechanism to verify that the transpilation of the model into the circuit was performed faithfully. This dissertation directly addresses these open problems. It proposes, implements and evaluates an end-to-end verifiable machine learning architecture for Random Forest classifiers that provides mathematical guarantees over three distinct integrity boundaries: the origin of the training data, the correctness of the training computation and the fidelity of the model's translation into a verifiable circuit. The framework is evaluated on both a simulated sensor dataset used by Keshavarzkalhori et al. and the CICIDS2017 network intrusion detection benchmark, the real-world cybersecurity dataset used by the most directly comparable prior work, demonstrating that the proposed integrity guarantees are achievable at practical computational cost for cybersecurity-relevant workloads. Research Questions The main objective of this thesis was to build a framework capable of protecting the overall AI Models from data and model poisoning attacks. In alignment with the goal, four research questions were set: Research Question 01: What state-of-the-art mechanisms exist to verify the integrity of AI models across the training pipeline? Research Question 02: What threats exist against AI models integrity? Research Question 03: What computational overhead do integrity verification mechanisms introduce across the AI modeling pipeline and how does this overhead scale with model complexity?
The Al-Rakhawy Document for Digital Sovereignty (EPSA) presents a complete engineering blueprint for encrypted machine learning. It integrates Federated Learning, Zero-Knowledge Proofs, and Smart Contracts across five layers. Key innovations include Pedersen Commitments for lightweight edge processing and the Al-Rakhawy Equation, which calculates fair rewards based on marginal impact. This system ensures absolute data privacy, breaks central monopolies, and provides users with immediate, mathematically guaranteed economic returns.
Pawan Kumar Sanjaya, Christina Giannoula, Valdy Oktavian, Mehdi Saeedi · 7 authors
Zero-knowledge machine learning (zkML) enables a server to perform verifiable inference while keeping model parameters private from the client. However, existing zkML systems incur prohibitive proof-generation costs. We observe that proof generation exhibits limited parallelism; that is, prover time does not decrease significantly as the number of threads increases. This limitation is because existing systems rely on monolithic proof computation, constructing a single proof for the entire machine learning model. We introduce zkComposer, a modular proof-construction framework that unlocks an additional dimension of parallelism, in addition to the parallelism in existing proof kernels. zkComposer decomposes the zkML proof of correct inference into independent sub-proofs, each covering a subset of the computation for inference e.g., each independent sub-proof can cover a subset of contiguous layers in the ML model. Adjacent sub-proofs are cryptographically linked through shared commitments to the activations from the boundary layer. zkComposer provides the same guarantees as the monolithic proof without requiring additional linking proofs or changes to the underlying cryptographic primitives. We implement zkComposer and evaluate it on three CNNs and GPT-2. We show that, on CNN workloads, zkComposer reduces prover time and response time by up to 3.25x relative to zkCNN [1]. On GPT-2, zkComposer reduces these times by up to 4.83x relative to zkGPT [2], when partitioning along the model layers. When partitioning across both model layers and input sequences in GPT-2, we show that zkComposer reduces prover time and response time by up to 6.84x relative to zkGPT [2].
Data visibility is more vital and decisive than ever before in the current data-driven world of technology. There is a significant upsurge in businesses leveraging digital technology, which has led to a greater amount of data being available than ever before. Additionally, managing the visibility in compliance with the organization's rules and regulations is crucial. The implementation of efficient data visibility will not merely improve decision-making but also streamline business processes with enhanced security. Numerous technologies offer solutions to manage data visibility, and distributed ledger technology (DLT) is one of them. DLT facilitates the execution of different methodologies to strengthen the governance of data visibility in enterprise-grade applications. On the other hand, these DLTs raise concerns regarding data visibility in this decentralized network, as not every enterprise-grade application requires data transparency across all the nodes. In this paper, a detailed systematic review is conducted with a clear focus on two essential data visibility parameters, Access control and anonymity, for the period 2020-2025, following a standardized Preferred Reporting Items for Systematic Review and Meta-Analyses -based breakdown of the selection process. Three clear dimensions of in-depth analysis are presented in the study: first, investigating how DLT can maintain transparency and decentralization in enterprise-grade applications; second, ensuring secure data access management for effective data governance; and third, the approach for anonymization to ensure privacy and security. The key finding highlights the credence of hyperledger fabric, a permissioned DLT, compared to other DLTs and exponentially growing concerns related to data visibility, as well as the conceptual and empirical research contributions made thus far. The limitations presented in this paper formulate a strong basis for research and enhancement of the existing models to offer controlled yet transparent data visibility.
Jin Ah Seo, Kun Hwa Lee, Vijayan Sugumaran, Jo Yeon Park · 5 authors
We build and evaluate a concrete Zero-Knowledge Machine Learning (ZKML)-based pipeline for epidemic diagnosis and show that it can enforce computational integrity without exposing raw medical data in a Web3 setting. In response to security challenges posed by centralized data handling in medical AI applications, particularly during public health crises such as COVID-19, ZKML offers a privacy-preserving alternative by combining machine learning and Zero-Knowledge Proofs (ZKP). We experimentally applied ZKML to a CNN (Convolutional Neural Networks)-based COVID-19 diagnostic model, achieving 87% accuracy and 0.35 loss. All proof generation and verification processes were executed entirely off-chain, with the verified outputs represented as committed public_vals recorded on-chain via smart contracts. To ensure authenticity, the system enforces dual ECDSA signature verification from both the model provider and the data provider. This mechanism prevents unauthorized submissions and confirms the validity of the result before it is stored on-chain. The system was tested under both normal and adversarial conditions, demonstrating robust and reliable operation. By enabling decentralized trust and self-sovereign control over data, this architecture aligns well with Web3 principles. The results indicate that ZKML can support the development of privacy-preserving and verifiable AI systems.
Open access
Adversarial Robustness in Machine Learning
Privacy-Preserving Technologies in Data
Artificial Intelligence in Healthcare and Education
Behzad Abdolmaleki, Amir R. Asadi, Vahid R. Asadi, Stefan Köpsell · 7 authors
Stochastic Gradient Descent (SGD) is the foundation of modern machine learning (ML). In privacy-sensitive settings, gradients can reveal details about individual data points. Differential Privacy (DP) protects sensitive data during ML training by clipping gradients and adding calibrated Gaussian noise. However, existing frameworks assume semi-honest participants, which fails in adversarial or federated environments where malicious actors can bypass or alter the noise addition process, breaking privacy guarantees. We present VeriDP, a framework for verifiable differentially private training that cryptographically enforces and proves the correct execution of differentially private stochastic gradient descent (DP-SGD) in zero knowledge. VeriDP integrates Zero-Knowledge Proofs (ZKPs) with polynomial commitments, sumcheck and GKR-based proofs, and incrementally verifiable computation (IVC) to generate compact proofs of correct gradient computation, clipping, averaging, and Gaussian noise generation—without revealing private data or randomness. Unlike previous systems that only verify the final privacy budget, VeriDP enables per-iteration verifiability of each model update, providing strong privacy assurances even in adversarial settings. This establishes a novel and complete Zero-Knowledge Proof of Differentially Private Stochastic Gradient Descent (ZK-DPSGD), uniting differential privacy and verifiable computation for secure and auditable ML. Our evaluation shows that prover time increases linearly with the number of input samples, while both verifier time (2–5 ms) and proof size (3–4 KB) remain compact and effectively constant.
With the rapid proliferation and interconnection of massive IoT devices, efficient and secure identity authentication has become a crucial prerequisite for ensuring communication security. Establishing trust among mutually untrusted devices remains a key research focus. Leveraging its tamper-resistance and traceability, blockchain technology has emerged as a foundational infrastructure for building trustworthy identity management systems. However, existing blockchain-based identity authentication schemes face critical challenges in large-scale IoT environments, including low authentication efficiency, complex certificate management, and risks of user privacy leakage. Achieving a balance among authentication efficiency, certificateless key management, and privacy protection remains a pressing challenge. In this paper, we propose a certificateless identity authentication scheme based on blockchain sharding. The scheme employs blockchain sharding to parallelize identity authentication across multiple shards, significantly enhancing overall efficiency. Within each shard, a certificateless public key cryptography (CL-PKC) scheme is adopted to eliminate certificate issuance and enable key generation via user interaction, thereby reducing key management overhead and improving security. For cross-shard authentication, a registration-based encryption (RBE) mechanism is utilized, allowing users to authenticate via their identity after registration. Any verifier can confirm the legitimacy of the authentication message solely based on the registration information and the user ID, ensuring transparency and public verifiability. Furthermore, a zero-knowledge proof-based verifiable credential (VC) selective disclosure mechanism is introduced, enabling users to reveal only the minimal necessary information required for authentication while protecting sensitive identity attributes. Experimental results demonstrate that the proposed scheme maintains high throughput under high-concurrency scenarios while effectively preserving user privacy.
Open access
2 source records
Cryptography and Data Security
Privacy-Preserving Technologies in Data
Advanced Steganography and Watermarking Techniques
Health care data management comes with numerous barriers as a result of the use of different systems of record keeping, which are not compatible and increase the risks for data protection and privacy. Medical records are frequently distributed throughout various clinics and hospitals, and due to this it is hard to share information when patients are being treated. Centralized record systems bring unauthorized access to records and the problems related to the safety of data. In order to enhance the level of confidence of people and improve the level of transparency of health care data, advanced people choose decentralized technologies and uses cryptography for these purposes. Blockchain technology offers an unchangeable and decentralized ledger that guarantees safe monitoring of all information despite the presence of any centralized body. Coupled with sophisticated encryption methods, it provides the ability to limit access to private health information. In order to provide secure and respect privacy regarding medical data sharing, an Electronic Health Record (EHR) system powered by blockchain technologies is proposed. Patient record metadata is recorded on-chain while health data itself is stored on encrypted off-chain storage. In the realm of access management, smart contracts facilitate patients in designating by whom their records can be accessed and modified. The privacy of information is further strengthened by advanced cryptographic techniques like attribute-based encryption and zero-knowledge proofs. The system provides seamless interoperability among hospitals, laboratories, and telemedicine systems while ensuring high levels of security. The results of performance evaluation demonstrate that this method facilitates reliable transaction processing while providing better security, transparency and control than traditional centralized EHR systems.
Jun 30, 2026·Proceedings of the Workshop on Advanced Tools, Programming Languages, and PLatforms for Implementing and Evaluating algorithms for Distributed systems
This article addresses the security of Federated Learning (FL) in distributed systems against a range of attacks, including model poisoning and unverifiable client behavior, while ensuring the semantic correctness of gradient updates. It proposes ZK-FedLedger, a verifiable and adaptive FL framework that integrates multi-constraint zero-knowledge proofs with a reputation-weighted Byzantine fault-tolerant blockchain consensus. Each client generates a zk-SNARK proof certifying that its update satisfies both an adaptive norm bound and a geometric alignment constraint relative to a trusted reference gradient. Verified commitments are recorded on-chain, while model parameters are aggregated off-chain using a hybrid storage architecture that minimizes blockchain overhead. Experimental evaluation on MNIST demonstrates stable convergence, with test accuracies of 98.17% (IID) and 94.93% (Non-IID), and near-perfect detection of major poisoning attacks. The results show that ZK-FedLedger enables proactive, cryptographically verifiable FL without compromising scalability or model performance.
The transparency of the blockchain technology makes privacy issues acutely challenging in certain highly sensitive applications such as IP protection and contractual arguments. This chapter is an overview of privacy preserving methods and in particular Zero-Knowledge Proofs (ZKPs) and confidential evidence handling mechanisms. ZKPs are set to transform notarization by allowing parties to prove that information or statements are true without disclosing the information that they have associated with them. The study covers the concept of use of blockchain and how they can be used to combine with smart contracts for privacy-preserving IP access rights governance and dispute resolution. It also covers confidential computing, homomorphic encryption, and secure multi-party computation for processing privacy sensitive evidence on the blockchain. In regulated industries, these technologies hold the promise of increasing prevalence, but encounter challenges relating to regulations and trusted setup, as well as computational overhead issues.
Federated Learning (FL) enables privacy-preserving collaborative learning for Internet of Vehicles (IoV) scenarios, but extreme heterogeneity of vehicular-edge-cloud resources severely limits system efficiency. Dynamic scheduling strategies mitigate this issue but introduce new trust concerns: verifying fair scheduling decisions and faithful client execution of compression instructions without privacy leakage remains an open challenge. We propose Nautilus, a verifiable efficient federated learning framework. First, a multi-dimensional resource-aware scheduling algorithm dynamically allocates compression ratios and training tasks based on vehicle bandwidth, latency and computing power, improving training efficiency. Second, a Zero-Knowledge Proof (ZKP) mechanism ensures scheduling fairness and execution compliance while preserving privacy. Experiments show the framework reduces communication overhead and accelerates convergence with guaranteed system integrity.
Protecting patient privacy in clinical trials and healthcare data management is becoming more difficult due to the growing amount of medical data. This study introduces a novel hybrid framework that combines fully homomorphic encryption (FHE) and zero-knowledge proofs (ZKPs), building on previous work that used blockchain and homomorphic encryption for secure cohort selection. By assigning result verification to effective ZKP constructs, the hybrid approach overcomes the main drawbacks of FHE, including high computational overhead and precision loss. Our tests show that this integrated approach maintains patient privacy while greatly increasing computational efficiency. We wrap up by talking about future research directions and possible healthcare applications.
Privacy-preserving systems have traditionally faced a fundamental tradeoff between data utility and confidentiality. Selective Disclosure Credentials (SDCs) enable users to prove specific attributes without revealing underlying personal information, while Fully Homomorphic Encryption (FHE) enables arbitrary computation on encrypted data without exposing plaintext. Although both technologies address critical privacy challenges, they solve different problems and are rarely integrated into a unified architecture. This paper introduces the concept of Composable Privacy, a layered framework that combines selective disclosure credentials, zero-knowledge proofs, and fully homomorphic encryption into a cohesive privacy architecture. The framework separates privacy concerns into three functional layers: an authentication layer using selective disclosure and zero-knowledge proofs, a computation layer using homomorphic encryption for confidential processing, and a verification layer that provides cryptographic assurances of computation correctness. The paper examines the cryptographic foundations of BBS+ signatures, Coconut threshold credentials, lattice-based homomorphic encryption schemes, and post-quantum security considerations. It further evaluates the practical feasibility of the architecture through applications in decentralized finance, healthcare federated learning, confidential governance systems, and blockchain-based identity infrastructure. Performance trends, scalability challenges, interoperability requirements, and future hardware acceleration pathways are also analyzed. The proposed Composable Privacy framework demonstrates how selective disclosure and encrypted computation can be combined to create privacy-preserving digital systems that maintain verifiability, confidentiality, and regulatory compliance simultaneously. The work provides a conceptual foundation for next-generation privacy architectures in blockchain, decentralized identity, and distributed computing environments.
Mohammed Ali Shaik, Salman Ali Syed, Imran Qureshi, Shivaratri Narasimha Rao
This chapter reviews the security and privacy issues related to the integration of blockchain and computer vision (CV) systems. CV is applied in the health sector, in car driving, in shopping centers, and in surveillance, but it highly depends on image and video data, which are difficult to authenticate, secure, or even kept private. The decentralized, immutable, and transparent format of blockchain provides a good choice to address these issues as it allows sharing of data safely, controlling access to data, and ensuring its auditability. This chapter starts with the introduction to the concept of CV applications and the confidentiality risks of these applications and then proceeds to explain how the concept of blockchain can be used to create a dependable, tamper-proof system to handle visual data, through the use of smart contracts and consensus algorithms. It talks of state-of-the-art methods, such as encryption, zero-knowledge, and federated learning (FL), to guarantee privacy preservation in blockchain-based CV systems. In fact, the real-life application illustrates how blockchain is used to lock up medical imaging, self-driving vehicles, and surveillance data. Lastly, this chapter discusses new regulatory and ethical issues such as ownership of the data, legislative privacy, and ethical smart use of surveillance technology.
Abstract Law firms and corporate legal departments hold large volumes of privileged text that could train superior legal AI models, but attorney-client privilege sharply constrains data sharing across organizational boundaries. Standard federated learning frameworks target statistical privacy rather than the stricter operational requirement that privileged communication content remain inaccessible to non-privileged parties. We present a federated learning architecture designed for multi-firm collaborative model training under explicit privilege constraints. The architecture integrates six components: a privilege classification engine that categorizes documents by privilege type before training; privilege-calibrated differential privacy where noise scales with sensitivity; homomorphic encryption of sanitized gradients with zero-knowledge sanitization proofs; trusted execution environment (TEE)-enclosed aggregation that combines encrypted updates without exposing individual contributions; a privilege boundary graph that models joint defense agreements with dynamic conflict detection and model rollback; and cryptographic audit trails designed for later judicial review. We evaluate the design through formal privacy analysis with composed R\'{e}nyi differential privacy budget bounds, a worked four-entity deployment scenario with conflict detection, and comparative security analysis against baseline federated configurations.