Zeiger is a pencil puzzle consisting of a rectangular grid, with each cell having an arrow pointing in horizontal or vertical direction. Some cells also contain a positive integer. The objective of this puzzle is to fill a positive integer into every unnumbered cell such that the integer in each cell is equal to the number of different integers in all cells along the direction an arrow in that cell points to. In this paper, we prove that deciding solvability of a given Zeiger puzzle is NP-complete via a reduction from the not-all-equal positive 3SAT (NAE3SAT+) problem. We also construct a card-based physical zero-knowledge proof protocol for Zeiger, which enables a prover to physically show a verifier the existence of the puzzle's solution without revealing it.
Abstract: In traditional digital identity management solutions, the authentication process mostly adopts a centralized authentication model, where user identity data is stored in a single authentication authority, which leads to a myriad of problems such as data leakage, data tampering, and single point of failure. How to ensure the security of user identity data and how to carry out effective privacy protection is the key to ensuring the digitalization process in various industries. Blockchain has the characteristics of decentralization and tampering, which can provide a reliable data storage solution. Zero-knowledge proof technology can verify the correctness of certain facts without revealing specific information. In digital identity applications, zero-knowledge proof allows users to prove their identity without revealing any personal information. Therefore, this paper proposes an identity authentication model based on blockchain and zero-knowledge proof to ensure the security of the user's identity data during the identity authentication process. Meanwhile, in order to make the authentication process more reliable, smart contract technology is introduced to complete the whole authentication process.
Current authentication schemes based on zero-knowledge proof (ZKP) still face issues such as high computation costs, low efficiency, and security assurance difficulty. Therefore, we propose a secure and efficient authentication scheme (SEAS) for large-scale IoT devices based on ZKP. In the initialization phase, the trusted authority creates prerequisites for device traceability and system security. Then, we propose a new registration method to ensure device anonymity. In the identity tracing and revocation phase, we revoke the real identity of abnormal devices by decrypting and updating group public keys, avoiding their access and reducing revocation costs. In the authentication phase, we check the arithmetic relationship between blind certificates, proofs, and other random data. We propose a new anonymous batch authentication method to effectively reduce computation costs, enhance authentication efficiency, and guarantee device authentication security. Security analysis and experimental results show that an SEAS can ensure security and effectively reduce verification time and energy costs. Its security and performance exceed existing schemes.
Open access
User Authentication and Security Systems
Advanced Steganography and Watermarking Techniques
Olive Franzese, Ali Shahin Shamsabadi, Luck, Carter, Haddadi, Hamed
The black-box service model enables ML service providers to serve clients while keeping their intellectual property and client data confidential. Confidentiality is critical for delivering ML services legally and responsibly, but makes it difficult for outside parties to verify important model properties such as fairness. Existing methods that assess model fairness confidentially lack either (i) reliability because they certify fairness with respect to a static set of data, and therefore fail to guarantee fairness in the presence of distribution shift or service provider malfeasance; and/or (ii) scalability due to the computational overhead of confidentiality-preserving cryptographic primitives. We address these problems by introducing online fairness certificates, which verify that a model is fair with respect to data received by the service provider online during deployment. We then present OATH, a deployably efficient and scalable zero-knowledge proof protocol for confidential online group fairness certification. OATH exploits statistical properties of group fairness via a cut-and-choose style protocol, enabling scalability improvements over baselines.
Dominik Meißner, Frank Kargl, Benjamin Erb, Felix Engelmann
Taking part in surveys, experiments, and studies is often compensated by rewards to increase the number of participants and encourage attendance. While privacy requirements are usually considered for participation, privacy aspects of the reward procedure are mostly ignored. To this end, we introduce PrePaMS, an efficient participation management system that supports prerequisite checks and participation rewards in a privacy-preserving way. Our system organizes participations with potential (dis-)qualifying dependencies and enables secure reward payoffs. By leveraging a set of proven cryptographic primitives and mechanisms such as anonymous credentials and zero-knowledge proofs, participations are protected so that service providers and organizers cannot derive the identity of participants even within the reward process. In this paper, we have designed and implemented a prototype of PrePaMS to show its effectiveness and evaluated its performance under realistic workloads. PrePaMS covers the information whether subjects have participated in surveys, experiments, or studies. When combined with other secure solutions for the actual data collection within these events, PrePaMS can represent a cornerstone for more privacy-preserving empirical research.
Haotian Yang, Pujie Jing, Zihan Wu, Lu Liu · 5 authors
The image integrity of real-time monitoring is crucial for monitoring crop growth, helping farmers and researchers improve production efficiency and crop yields. Unfortunately, existing schemes just focus on ground equipment and drone imaging, neglecting satellite networks in remote or extreme environments. Given that satellite internet features wide area coverage, we propose SEAIS, a secure and efficient agricultural image storage scheme combining blockchain and satellite networks. SEAIS presents the mathematical model of image processing and transmission based on satellite networks. Moreover, to ensure the integrity and authenticity of image data during pre-processing such as denoising and enhancement, SEAIS includes a secure agricultural image storage and verification method based on blockchain, homomorphic encryption, and zero-knowledge proof. Specifically, images are stored via IPFS, with hash values and metadata recorded on the blockchain, ensuring immutability and transparency. The simulation results show that SEAIS exhibits more stable and efficient processing times in extreme environments. Also, it maintains low on-chain storage overhead, enhancing scalability.
In this paper, we propose a smart contract-based multi-candidate self-tallying voting scheme in order to guarantee the privacy of ballots in the case of multiple candidates. This scheme uses the ElGamal cryptosystem to ensure the security of the ballots, and combines it with a Distributed Encryption algorithm to make the voting scheme have self-tallying features, and guarantees the correctness of the intermediate data through zero-knowledge proofs. The experimental results show that the scheme improves the voting efficiency without compromising the security.
Identity-based threshold signature (IDTHS) allows a threshold number of signers to generate signatures to improve the deterministic wallet in the blockchain . However, the IDTHS scheme cannot determine the identity of malicious signers in case of misinformation . To solve this challenge, we propose an identity-based threshold (multi) signature with private accountability (for short AIDTHS) for privacy-preserving blockchain . From the public perspective, AIDTHS is completely private and no user knows who participated in generating the signature. At the same time, when there is a problem with the transaction, a trace entity can trace and be accountable to the signers. We formally define the syntax and security model of AIDTHS. To address the issue of identifying malicious signers, we improve upon traditional identity-based threshold signatures by incorporating zero-knowledge proofs as part of the signature and leveraging a tracer holding tracing keys to identify all signers. Additionally, to protect the privacy of signers, the signature is no longer achievable by anyone, which requires a combiner holding the keys to produce a valid signature. We give a concrete construction of AIDTHS and prove its security. Finally, we implement the AIDTHS scheme and compare it with existing schemes. The key distribution algorithm of AIDTHS takes 13.04 ms and the signature algorithm takes 34.60 μ s . The verification algorithm takes 1 s , which is one-third of the time the TAPS scheme uses.
R. Shashidhara, Renju Chirakarotu Nair, Pavan Kumar Panakalapati
ABSTRACT Blockchain technology has revolutionized data sharing and storage, yet maintaining privacy and security while ensuring transparency and immutability remains a significant challenge. This research addresses this challenge by investigating the promising solution of Zero‐Knowledge Proofs (ZKPs), which allow for validation without revealing additional information. Unlike previous studies that focus on specific ZKP types or limited performance metrics, this article provides a comprehensive analysis of multiple ZKP technologies, along with their distinct features and blockchain applications. We evaluate ZKP tools such as snarkjs, ZoKrates, and Circom, focusing on key factors including proof size, trusted setup, prover time, verifier time, and scalability. A detailed case study on the Ethereum blockchain demonstrates the practical implementation and performance of ZKPs, effectively bridging the gap between theoretical analysis and real‐world applications. Furthermore, we explore how ZKPs can significantly enhance scalability in blockchain applications. By proposing innovative ZKP use cases and addressing current challenges, this article not only provides a comprehensive understanding of ZKP technologies but also outlines potential future research directions, making a significant impact on the field of blockchain privacy and security.
A Zero-Knowledge Proof basically is a protocol between two parties, the Prover and the Verifier, that allows the Prover to convince the Verifier about the truthness of a non trivial statement without revealing any additional information. Zero Knowledge Proofs have found a lot of practical applications covering most of the protocols concerning about data privacy and protocol verification. Examples of that are anonymous cash or electronic voting. The possibility to have real quantum computers with a reasonable size in a near future is forcing the cryptographic community to devise new methods to provide security that resist quantum attacks. Most of the zero-knowledge protocols used nowadays are based on computational problems like the discrete logarithm problem that can no longer be considered hard, since there are known efficient ways to solve them with quantum algorithms. Cryptographic research about the quantum security of zero knowledge proofs started nearly 20 years ago in a very theoretical approach, but not many papers on that topic appeared since then. The goal of this thesis is writing a survey including the main concepts about quantum secure zero-knowledge protocols, the state-of-the-art both from the theoretical and practical approaches, and an exploration of their potential application areas. The survey will be a good starting document for further students willing to do research in this topic.
Private set intersection (PSI) has important application value, however, current quantum PSI protocols are either unsuitable for multi-party scenarios or inefficient. Recently, Imran (arXiv: 2303.17196v3 , 2023) proposed two quantum secure multi-party greatest common divisor (GCD) protocols that can be used for PSI, but with the downside of information leakage and resource consumption. In this paper, we propose a novel quantum secure multi-party GCD protocol that has higher security and lower complexity. To hide privacy, each party randomly selects a coefficient within a range determined by his input integer, and with the assistance of a semi-honest third party TP, all parties secretly calculate the linear combination of their inputs under these coefficients. Once enough linear combinations are collected, TP calculates the GCD of these combinations, which is equal to the GCD of all input integers. To verify the honesty of participants, a quantum zero-knowledge proof sub-protocol is designed. Analysis shows that our GCD protocol is correct and has security against malicious attacks. Moreover, its complexity is polynomial level and lower than Imran’s. Furthermore, we demonstrate the scalability of our GCD protocol in private set operations, such as private set intersection, private set intersection cardinality, private multi-set intersection, etc.
The increased use of artificial intelligence generated content (AIGC) among vast user populations has heightened the risk of private data leaks. Effective auditing and regulation remain challenging, further compounding the risks associated with the leaks involving model parameters and user data. Blockchain technology, renowned for its decentralized consensus mechanism and tamper-resistant properties, is emerging as an ideal tool for documenting, auditing, and analyzing the behaviors of all stakeholders in machine learning as a service (MLaaS). This study centers on biometric recognition systems, addressing pressing privacy and security concerns through innovative endeavors. We conducted experiments to analyze six distinct deep neural networks, leveraging a dataset quality metric grounded in the query output space to quantify the value of the transfer datasets. This analysis revealed the impact of imbalanced datasets on training accuracy, thereby bolstering the system's capacity to detect model data thefts. Furthermore, we designed and implemented a novel Bio-Rollup scheme, seamlessly integrating technologies such as certificate authority, blockchain layer two scaling, and zero-knowledge proofs. This innovative scheme facilitates lightweight auditing through Merkle proofs, enhancing efficiency while minimizing blockchain storage requirements. Compared to the baseline approach, Bio-Rollup restores the integrity of the biometric system and simplifies deployment procedures. It effectively prevents unauthorized use through certificate authorization and zero-knowledge proofs, thus safeguarding user privacy and offering a passive defense against model stealing attacks.
On 30 April, Regulation (EU) 2024/1183 amending Regulation (EU) 910/2014 as regards establishing the European Digital Identity Framework, known as eIDAS 2.0, was published in the Official Journal of the European Union. This amendment introduces zero-knowledge proof (ZKP) privacy-enhancing technologies to support two key innovations: the European Digital Identity Wallet (EUDIW) and the electronic attestations of attributes ((Q)EAA). In parallel, the European Digital Identity Wallet Architecture and Reference Framework (EUDIW ARF) provide a common set of standards and best practices for the technical implementation of eIDAS 2.0. This paper analyses how the EUDIW can rely on ZKPs since neither the eIDAS 2.0 Regulation nor the EUDIW ARF does so. We propose a software product regime for the cryptographic derivation of data in a ZKP format through three mutually compatible regulatory options: zero-knowledge issuance by default through the EUDIW, through a software product extension of the EUDIW, or a private standalone wallet. These schemes provide the legal basis for the secure use of ZKPs, ensure effective compliance with the GDPR, and contribute to the debate on the value that electronic ledgers introduced by eIDAS 2.0 bring to digital identity.
UAVs are increasingly being used in various domains, from personal and commercial applications to military operations. Ensuring the security and trustworthiness of UAV communications is crucial, and blockchain technology has been explored as a solution. However, privacy remains a challenge, especially in public blockchains. In this work, we propose a novel approach utilizing zero-knowledge proof techniques, specifically zk-SNARKs, which are non-interactive cryptographic proofs. This approach allows UAVs to prove their authenticity or location without disclosing sensitive information. We generated zk-SNARK proofs using the Zokrates tool on a Raspberry Pi, simulating a drone environment, and analyzed power consumption and CPU utilization. The results are promising, especially in the case of larger drones with higher battery capacities. Ethereum was chosen as the public blockchain platform, with smart contracts developed in Solidity and tested on the Sepolia testnet using Remix IDE. This novel proposed approach paves the way for a new path of research in the UAV area.
This study explores the balance between data privacy and regulatory compliance in blockchain-based financial systems, focusing on privacy-enhancing technologies (PETs) such as Zero-Knowledge Proofs (ZKPs) and multiparty computations (MPCs). Through a comprehensive methodology combining literature review, comparative analysis, and empirical testing on the Ethereum test network, the research reveals significant trade-offs. Implementing ZKPs increased transaction times from 5 seconds to 12 seconds and gas fees from 0.02 ETH to 0.05 ETH, while computational load rose by 60%, highlighting the impact on scalability and efficiency. Chi-Square tests and regression analysis uncovered notable algorithmic biases, with low-value accounts experiencing 15% fewer transaction approvals and small mining pools receiving 20% fewer rewards than larger counterparts. Additionally, MPCs, while offering robust privacy, increased communication overhead by 35%, posing scalability challenges. The study recommends adopting a tiered privacy approach, implementing basic privacy measures for low-sensitivity transactions, and advanced technologies like ZKPs for high-sensitivity transactions while optimizing ZKPs to reduce their computational burden and enhance transaction speeds, and integrating artificial intelligence to detect and mitigate algorithmic biases in blockchain systems. Future research should also explore hybrid privacy solutions that combine the strengths of different PETs, such as ZKPs and MPCs, to achieve both robust privacy and high efficiency. Furthermore, investigating quantum-resistant cryptographic methods is crucial to safeguarding blockchain systems against emerging threats. These insights provide valuable guidance for financial institutions, blockchain developers, and policymakers, promoting the development of blockchain-based financial systems that optimize data privacy while maintaining system performance and regulatory compliance.
Xudong Zhu, Haoqi He, Zhengbang Yang, Yi Deng · 6 authors
Zero-knowledge proof (ZKP) is a cryptographic primitive that enables a prover to convince a verifier that a statement is true, without revealing any other information beyond the correctness of the statement itself. Due to its powerful capabilities, its most practical type, called zero-knowledge Succinct Non-interactive ARgument of Knowledge (zkSNARK), has been widely deployed in various privacypreserving applications such as cryptocurrencies and verifiable computation. Although state-of-the-art zkSNARKs are highly efficient for the verifier, the computational overhead for the prover is still orders of magnitude too high to warrant use in many applications. This overhead arises from several time-consuming operations, including large-scale matrix-vector multiplication (MUL), number-theoretic transform (NTT), and especially the multi-scalar multiplication (MSM) which constitutes the largest proportion. Therefore, further efficiency improvements are needed.In this paper, we focus on comprehensive optimization of running time and storage space required by the MSM algorithm on GPUs. Specifically, we propose a novel, modular and adaptive parameter configuration technique—elastic MSM to enable us to adjust the scale of MSM according to our own wishes by performing a corresponding amount of preprocessing. This technique enables us to fully unleash the potential of various efficient parallel MSM algorithms. We have implemented and tested elastic MSM over three prevailing parallel Pippenger algorithms on GPUs. Across various preprocessing space limitations (across various MSM scales), our constructions achieve up to about 1.90×, 1.08× and 1.36× (2.58×, 1.39× and 1.91×) speedup versus three state-of-the-art parallel Pippenger algorithms on GPUs, respectively.From another perspective, elastic MSM could also be regarded as a preprocessing technique over the well-known Pippenger algorithm, which is modular and could be used to accelerate almost all the most advanced parallel Pippenger algorithms on GPUs. Meanwhile, elastic MSM provides an adaptive trade-off between the running time and the extra storage space needed by parallel Pippenger algorithms on GPUs. This is the first preprocessing technique to retain the improved MSM computation brought by preprocessing under varying storage space limitations. Specifically, across various preprocessing space limitations (across various MSM scales), our constructions achieve up to about 192× and 223× (159× and 174×) speedup versus two state-ofthe- art preprocessing parallel Pippenger algorithms on GPUs, respectively.
We initiate the study of relativistic zero-knowledge quantum proof of knowledge systems with classical communication, formally defining a number of useful concepts and constructing appropriate knowledge extractors for all the existing protocols in the relativistic setting which satisfy a weaker variant of the special soundness property due to Unruh (EUROCRYPT 2012). We show that there exists quantum proofs of knowledge with knowledge error 1/2 + negl(η) for all relations in NP via a construction of such a system for the Hamiltonian cycle relation using a general relativistic commitment scheme exhibiting the fairly-binding property due to Fehr and Fillinger (EUROCRYPT 2016). We further show that one can construct quantum proof of knowledge extractors for proof systems which do not exhibit special soundness, and therefore require an extractor to rewind multiple times. We develop a new multi-prover quantum rewinding technique by combining ideas from monogamy of entanglement and gentle measurement lemmas that can break the quantum rewinding barrier. Finally, we prove a new bound on the impact of consecutive measurements and use it to significantly improve the soundness bound of some existing relativistic zero knowledge proof systems, such as the one due to Chailloux and Leverrier (EUROCRYPT 2017).
With the rapid development of Zero-Knowledge Proofs (ZKPs), particularly Succinct Non-Interactive Arguments of Knowledge (SNARKs), benchmarking various ZK tools has become a valuable task. ZK-friendly hash functions, as key algorithms in blockchain, have garnered significant attention. Therefore, comprehensive benchmarking and evaluations of these evolving algorithms in ZK circuits present both promising opportunities and challenges. Additionally, we focus on a popular ZKP application, privacy-preserving transaction protocols, aiming to leverage SNARKs' cost-efficiency through "batch processing" to address high on-chain costs and compliance issues. To this end, we benchmarked three SNARK proving systems and five ZK-friendly hash functions, including our self-developed circuit templates for Poseidon2, Neptune, and GMiMC, on the bn254 curve within the circom-snarkjs framework. We also introduced the role of "sequencer" in our SNARK-based privacy-preserving transaction scheme to enhance efficiency and enable flexible auditing. We conducted privacy and security analyses, as well as implementation and evaluation on Ethereum Virtual Machine (EVM)-compatible chains. The results indicate that Poseidon and Poseidon2 demonstrate superior memory usage and runtime during proof generation under Groth16. Moreover, compared to the baseline, Poseidon2 not only generates proofs faster but also reduces on-chain costs by 73% on EVM chains and nearly 26% on Hedera. Our work provides a benchmark for ZK-friendly hash functions and ZK tools, while also exploring cost efficiency and compliance in ZKP-based privacy-preserving transaction protocols.
Daniele Friolo, Geoffrey Goodell, D. R. Toliver, Hazem Danny Nakib
This article builds upon the protocol for digital transfers described by Goodell, Toliver, and Nakib, which combines privacy by design for consumers with strong compliance enforcement for recipients of payments and self-validating assets that carry their own verifiable provenance information. We extend the protocol to allow for the verification that reissued assets were created in accordance with rules prohibiting the creation of new assets by anyone but the issuer, without exposing information about the circumstances in which the assets were created that could be used to identify the payer. The modified protocol combines an audit log with zero-knowledge proofs, so that a consumer spending an asset can demonstrate that there exists a valid entry on the audit log that is associated with the asset, without specifying which entry it is. This property is important as a means to allow money to be reissued within the system without the involvement of system operators within the zone of control of the original issuer. Additionally, we identify a key property of privacy-respecting electronic payments, wherein the payer is not required to retain secrets arising from one transaction until the following transaction, and argue that this property is essential to framing security requirements for storage of digital assets and the risk of blackmail or coercion as a way to exfiltrate information about payment history. We claim that the design of our protocol strongly protects the anonymity of payers with respect to their payment transactions, while preventing the creation of assets by any party other than the original issuer without destroying assets of equal value.
Blockchain technologies have introduced a compelling paradigm for a new understanding of security through decentralized networks and consensus mechanisms. However, they need all data to be public, which may be unacceptable for use cases such as biometric data processing or sensitive monetary transactions. Therefore, confidentiality is identified as a need in blockchain. Additionally, blockchain can contribute to confidential applications by providing publicly verifiable mechanisms, therefore enhancing security. This work presents a framework for cost-effective and publicly verifiable confidential computations in blockchain, by relying on secure multi-party computation committees and zero-knowledge proofs. Our framework supports arbitrary computations on confidential data enforced by smart contracts. Additionally, staking, incentives, and cheat identification are provided as solutions to enhance trust. We also provide a technical solution to embed secure multi-party computations within smart contracts by using the Promise programming pattern. Finally, a cost analysis is provided to justify the feasibility of the framework compared to other solutions.
Vid Keršič, Martin Domajnko, Sašo Karakatič, Muhamed Turkanović
Z vse pogostejšo uporabo interneta in migracijo storitev iz fizičnega sveta v digitalni, postajajo vprašanja o varnosti, zasebnosti in digitalnem lastništvu osebnih podatkov vse pogostejša. Ena izmed ključnih tehnologij, ki omogoča razvoj rešitev na tem področju, so ničelno spoznavni dokazi (ang. zero-knowledge proofs, ZKP). ZKP so kriptografski protokoli, pri katerih dokazovalec dokaže pravilnost poljubne trditve preveritelju, ne da bi pri tem razkril dodatne informacije ali svoje podatke. V članku predstavimo ZKP protokole in njihove razlike, s posebnim poudarkom na dveh najpogostejših družinah protokolov: zk-SNARK in zk-STARK. Njihovo uporabno vrednost prikažemo na področju samo-upravljane in decentralizirane identitete ter na področju strojnega učenja. Pri decentralizirani identiteti ZKP omogočajo deljenje podatkov brez razkritja zasebnih informacij, medtem ko pri strojnem učenju omogočajo preverljivost izhodov modelov. To pomeni, da lahko uporabnik preveri, ali je bil za generiranje napovedi dejansko uporabljen pravilno izbran model.
Smart grid technologies have rapidly become one of the largest and most comprehensive sources of data for the modern utility. For the most part, data streams are seen as an essential tool that enable utilities to carry their day-to-day business operations, but they also create the need for efficient and secure data management strategies. In the context of the smart grid, ensuring data privacy is becoming an increasing concern due to a combination of factors that range from shifts in operational paradigms and rapid technology evolution to changes in legislation. Furthermore, researchers have highlighted the risks associated with improperly protected energy records. For example, energy consumption data from homes could be used to infer the behaviors and habits of home occupants through activity recognition or user profiling (Fan, 2017), which may lead to unfair service pricing, targeted advertising, or other personal security violations. Similarly, Electric Vehicles’ (EVs) charging metadata could be used to reveal private information about the owner such as their payment methods, preferred charging stations, and other locational and timing information that could be used to reconstruct the vehicle owner’s behaviors. The privacy of user data, even when used for statistical analysis or machine learning training processes, also needs to be carefully considered, as an individual’s private traits may still be vulnerable if their inclusion/exclusion greatly impacts the result or could be linked to a public dataset through cross-reference. The breach of user privacy also has severe impacts for organizations that store, transmit, or work on the data in the form of diminishing the public’s trust in them while potentially incurring legal consequences (e.g., fines and suspensions under the European Union General Data Protection Regulation, Health Insurance Portability and Accountability Act, etc.). Because of these risks, several privacy-preserving mechanisms are available to help organizations comply with privacy legislations and prevent the unauthorized and malicious use of user data. In light of these concerns, this report focuses on performing a computational review of privacy-preserving mechanisms that have received a significant amount of interest in literature. It specifically focuses on 1) homomorphic encryption, 2) zero-knowledge proofs, 3) differential privacy, and 4) federated learning. It is worth noting that although many of the methods presented in this document rely on cryptographic primitives, their intent is not to provide perfect secrecy, but rather to enable users to maintain privacy, and thus they shall not be compared or equated to other constructs that are aimed to address cybersecurity constructs.
Web 3.0 represents the next significant evolution of the internet that embodies the underlying decentralized network architectures, distributed ledgers, and advanced AI capabilities. Though the technologies are maturing rapidly, considerable barriers exist to high-scale adoption. The author discusses the barriers and the mitigations through specific technologies maturing to solve those issues in an earlier paper titled Moving Beyond POCs and Pilots, published in 2023 in Blockchain in Healthcare Today. These include privacy-preserving technologies, off-chain and on-chain design optimizations, and the multi-dimensional approach needed in planning and adopting these technologies. As an extension, this paper discusses one such enabler, zero knowledge machine learning (ZKML), which merges two streams of technology in unique ways to address problems in privacy and the cost of inference. Zero-knowledge proofs (ZKP) allow one party to prove the validity of a statement to another party without revealing any additional information about the statement itself. The ZKML combines the cryptographic principle of ZKP with machine learning (ML) techniques. It is still a maturing technology and needs baselines for applications in global healthcare. In this effort, the authors conceptualize the technical and operational feasibility of using ZKML and implement a reference healthcare implementation using the synthetic International Consortium for Health Outcomes Measurement (ICHOM) in the evaluation phase in a global healthcare setting for high-volume data collection, including patient-reported outcomes. Model complexity reduction is researched and reported for the ICHOM diabetes dataset to advance the usage of ML models in global standards of healthcare data collection in network decentralized architectures for increased data protection and efficiencies.