Ownership Verification Tools

• Ownership Verification Tools are mechanisms and protocols that prove, verify, or transfer ownership rights using cryptographic safeguards and watermarking techniques.
• They employ methods such as encrypted message exchanges, zero-knowledge proofs, and model fingerprinting to ensure secure and tamper-resistant ownership validation.
• These tools are applied across various domains including device management, collaborative ML, intellectual property, and decentralized blockchain platforms.

Ownership Verification Tools are technical mechanisms and protocols that enable entities—whether individuals, organizations, or collaborative agents—to prove, verify, or transfer ownership rights to assets, data, computational models, or devices. These tools leverage cryptographic protocols, watermarking, fingerprinting, formal verification, and privacy-preserving credential systems to establish trust in ownership claims, support fair transfers or transactions, and provide legal or technical basis for asserting intellectual property or data rights.

1. Cryptographic Protocols for Tangible Ownership Transfer

Cryptographic ownership verification protocols are foundational for transferring device or asset ownership in ubiquitous computing environments. In protocols such as the Ownership Authentication Transfer Protocol (OATP), ownership is transferred by orchestrating a sequence of encrypted message exchanges among the current owner, the prospective owner, and a trusted Central Key Server (CKS). Secure encryption (using the CKS’s public key), the use of nonces, and dual-user acknowledgments are employed to guarantee message confidentiality, authentication, and freshness.

Key protocol steps include:

• Old owner initiates a transfer request with credentials and an Ownership Transfer Request (OTR), securely encrypted to the CKS.
• The CKS issues a Ticket acknowledging the pending transfer.
• The new owner presents credentials, Ticket, and own nonce to the CKS.
• Upon successful authentication and financial checks, the CKS issues an Ownership Transfer Confirmation (OTC), encrypted to the old owner’s nonce.
• Final acknowledgment from the old owner allows the CKS to complete the process by issuing new credentials to the new owner.

This sequence ensures that if any step is incomplete, neither party can use the device, preserving both liveness and safety even in adversarial scenarios involving device theft or impersonation. Formal verification using tools like AVISPA and model checking approaches (e.g., NuSMV with CSP and CTL specifications) confirm properties such as secrecy and authentication invariants, ruling out impersonation and replay attacks (H et al., 2012, Bhat et al., 2012).

2. Ownership Verification in Federated and Collaborative ML

In collaborative settings like Federated Learning (FL), ownership verification tools embed cryptographically protected watermarks or credentials within trained deep learning models, thereby enabling decentralized, privacy-preserving verification. Advanced schemes such as FedIPR, FedTracker, FedZKP, and FedSOV all address the twin needs of model IP protection and robust, multi-client verification:

• FedIPR: Allows each client to privately embed a unique watermark (feature-based or backdoor-based). When total embedded bit-length does not exceed target parameter capacity, clients can always achieve perfect detection via secret extraction, with detection rate $\eta_F = 1$ if $K \cdot N \leq M$; for $K \cdot N > M$, $$\eta_F \geq \frac{K \cdot N + M}{2 \cdot K \cdot N}$$ (Li et al., 2021).
• FedZKP: Employs zero-knowledge proofs based on exact Learning Parity with Noise (xLPN) to allow clients to prove their credentials without disclosure. Multi-round $\Sigma$-protocols provide statistically negligible probability of successful forgery or credential leakage (Yang et al., 2023).
• FedSOV: Uses digital signature schemes for scalable, ambiguity-resistant credential verification, compressing many client public keys via collision-resistant Hash functions to a fixed-length watermark, and requiring unforgeable signatures for ownership proof (Yang et al., 2023).
• FedTracker: Combines global watermark (for group ownership) and individualized local fingerprints (for traceability) within a continual learning framework, using a Fingerprint Similarity Score (FSS) for discriminative identification and robust backtracking (Shao et al., 2022).

These methods collectively advance privacy, robustness to removal attacks, resilience against ambiguous or malicious credential claims, and legal accountability for collaborative IP.

3. Watermarking and Fingerprinting Techniques for ML Models

Ownership verification in deep learning models frequently relies on watermarking or fingerprinting, implemented via parameter manipulation, behavior induction, or architectural signatures:

• Parameter Watermarking: Binary strings are embedded directly into normalization parameters via additional regularization terms, as in feature watermarking for Tokenized Model platforms (Li et al., 2023) or FedIPR.
• Backdoor Watermarking: Selected triggers are injected so that models react by making predetermined predictions on specific inputs (e.g., BadNets, cluster-based triggers for speaker verification). This enables statistical black-box verification by querying for anomalous model behavior (Li et al., 2022, Li et al., 2 Mar 2025).
• Architecture Fingerprinting: Watermarks are embedded into the model’s design using Neural Architecture Search (NAS), with uniqueness ensured via hard-coded “stamps” in network cells. Verification can be performed via cache side-channel analysis, robust to parameter-level changes (Lou et al., 2021).
• Embedding Fingerprints: For graph neural networks (GNNs), tools like GrOVe use the inherent node embeddings as fingerprints, leveraging a learned similarity classifier to distinguish models extracted via model stealing from independently trained models with the same architecture (Waheed et al., 2023).
• Privacy Leakage Fingerprints: Techniques such as VeriDIP extract a model’s “memory” of sensitive training data via membership inference attacks. Fingerprints are sharpened by focusing on “less private” samples—those most affected by removal during training—to strengthen statistical hypothesis testing (Hu et al., 2023).

These mechanisms are typically evaluated for robustness against adaptive attacks, including fine-tuning, model pruning, and advanced detection and removal strategies.

4. Statistical and Certified Ownership Verification of Datasets

Recent advances in verification tools support statistically rigorous and certified dataset ownership verification under practical (often adversarial) conditions:

• Data Taggants: Out-of-distribution (OOD) samples paired with random labels are used as secret keys, and clean-label data poisoning optimizes for gradient alignment, allowing the keys to be detectable via high-confidence binomial hypothesis testing with top-k model predictions. The statistical certificate is robust, computationally efficient, and less likely to induce false positives than traditional backdoor watermarking (Bouaziz et al., 9 Oct 2024).
• Certified Dataset Watermarking (CertDW): Inspired by conformal prediction, two statistics—Principal Probability (PP) and Watermark Robustness (WR)—quantify the stability of predictions under perturbations. CertDW guarantees a provable lower bound gap between WR (on watermarked samples) and PP (on benign models), supporting statistically certified, noise-robust ownership verification decisions even under adversarial or accidental perturbations (Qiao et al., 16 Jun 2025).
• Clustering-Based Backdoor Watermarking (CBW): For speaker verification data, samples are clustered in feature space and encoded with cluster-specific triggers. Statistical verification (e.g., t-testing similarity scores or Wilcoxon decision tests) is used to distinguish between watermarked and unwatermarked models. The method achieves high watermark success rates and is resilient under fine-tuning and data augmentation (Li et al., 2 Mar 2025).

These approaches emphasize black-box verification, statistical rigor, and provable robustness, even in the presence of model or query perturbations.

5. Ownership Types and Formal Verification in Programs

Ownership types for program verification support strong correctness guarantees in low-level or pointer-manipulating code, especially in the presence of aliasing and pointer arithmetic:

• Borrowable Fractional Ownership Types: Integrate fractional permissions (read/write division among aliases) with lifetime-based borrowable permissions (à la RustHorn), formalized as types like $\tref{\alpha}{r}{B}$ annotating lifetimes, ownership portions, and active borrows. Type addition and splitting support reasoning about aliasing, with automated translation into pure functional programs for verification (Nakayama et al., 2023).
• Ownership and Refinement Types with Pointer Arithmetic: The type system jointly tracks logic predicates (refinements) and ownership functions over contiguous memory, allowing precise aliasing control and enabling “strong updates” on fully owned locations. Pointer arithmetic is handled as symbolic index shifting in both refinement and permission tracking. Automatic inference with SMT and CHC solvers proves absence of assertion failures, handling cases where alternative CHC-based verifiers are intractable (Tanaka et al., 2023).

These frameworks extend the applicability of ownership verification to verification of program correctness in systems software.

6. Blockchain and Decentralized Verification Platforms

Ownership verification platforms integrating smart contracts and blockchain infrastructure provide decentralized, tamper-resistant registries and automated financial mechanisms:

• Tokenized Model: Uniquely negotiates and registers watermarks (as tokens) on-chain, mapping each model’s proprietary signature to a user address. Verification involves extracting the model’s watermark and matching against the blockchain registry within an allowable Hamming distance. Protocols guarantee uniqueness (minimum inter-watermark Hamming distance), error tolerance, and support auditing via decentralized committees for verification (Li et al., 2023).
• Financialization and Reward Distribution: Models become tradeable digital assets with transparent provenance, enabling secure transactions. In federated learning, blockchain manages both global model copyright and contribution shares, allowing reward distribution proportional to verified participation.

Immutability, decentralization, and auditability address longstanding issues of central third-party trust and incentive alignment in copyrighted model exchanges and federated collaborations.

7. Data Ownership Verification in Regulatory and Consumer Contexts

In regulated data environments, privacy-preserving protocols support accountless and unlinkable proof of data ownership, enabling compliance with GDPR/CCPA and similar frameworks:

• VICEROY: Combines asymmetric cryptography with hierarchical key derivation (BIP32), session-linked public/private key pairs, and server-generated cookie wrappers. Consumers prove ownership from a master device-derived keybase, with cryptographic wrappers binding keys to server sessions. The protocol achieves unforgeability (no wrapper or VCR can be constructed without proper issuance), unlinkability across requests, and scalability to large user bases (Jordan et al., 2021).

Efficient implementation, minimal storage/latency overheads, and seamless integration with modern browser and session management systems address practical deployment barriers in web-scale data ownership.

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Ownership verification tools span a wide spectrum of security domains—cryptographic transfer protocols, machine learning watermarking, statistical dataset marking, formal programming correctness, and decentralized blockchain infrastructures. Across these contexts, rigorous mathematical invariants, formal model checking, cryptographic protocols, and statistical hypothesis testing ensure that ownership claims and transfers are not only technically robust but also verifiable in adversarial settings, underpinning intellectual property protection, regulatory compliance, and trustworthy digital asset management.