In a democratic regime, voting is crucial to making collective decisions. Unfortunately, although this activity has great significance and value, little effort has been made to improve the way we vote. Paper ballots are still the most used method, although this method is relatively simple, brings many inconveniences, and represents a contradiction to the modern world and its advances. This paper mostly focuses on a review study of blockchain-based voting systems. It aims at identifying the strategies and the guidelines as well as provides a comprehensive end-to-end electronic voting system based on blockchain, with the help of cryptographic techniques such as zero-knowledge proofs to improve privacy. The novelty of this paper is that we tackle the limitations of electronic voting systems found in the literature, including cost, identity management, and scalability problems. Our purpose is to provide key elements for organizations on how to design their proper electronic voting system based on blockchain technology.
Open access
Internet Traffic Analysis and Secure E-voting
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
ABSTRACT Voting has always been a crucial topic of public attention for democratic reasons. The ease of use and low cost are the result of e‐voting being frequently used for such important decision outcomes. However, the tremendous authority and intervening data in current e‐voting systems make it risky and difficult to achieve correct equity and clarity in e‐voting. So, by combining e‐voting with blockchain technology, these issues can be resolved while providing reorganization and intervention‐resistant characteristics. A voter's improper manipulation, frequent voting, or non‐party voting, may also undermine fairness. A verifier is therefore required to check the e‐voting mechanism in order to ensure its effectiveness and control the process equality and fairness. In this paper, a Blockchain‐based e‐Voting Mechanism (BVM) is developed for providing the end to end security and fairness for transparent voting. This mechanism also provides a zero‐knowledge proof (ZP) based verifier to inspect the voting procedure against voter's mis‐operations and uses a novel Improved Master‐key Administration (IMA) based public key cryptography to attack prevention. The utilization of blockchain technology ensures transparency, anonymity, confidentiality, authentication, tamper resistance, and a high level of data integrity, making it a promising choice for modernizing and enhancing the electoral process. Also, the performance of BVM has been compared with similar voting mechanisms and analyzed based on time complexity, security analysis, performance factors like delay and throughput, and anti‐attack examination.
Open access
2 source records
Internet Traffic Analysis and Secure E-voting
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Stefanos Chaliasos, Jens Ernstberger, David Theodore, David A. Wong · 6 authors
Zero-knowledge proofs (ZKPs) have evolved from being a theoretical concept providing privacy and verifiability to having practical, real-world implementations, with SNARKs (Succinct Non-Interactive Argument of Knowledge) emerging as one of the most significant innovations. Prior work has mainly focused on designing more efficient SNARK systems and providing security proofs for them. Many think of SNARKs as "just math," implying that what is proven to be correct and secure is correct in practice. In contrast, this paper focuses on assessing end-to-end security properties of real-life SNARK implementations. We start by building foundations with a system model and by establishing threat models and defining adversarial roles for systems that use SNARKs. Our study encompasses an extensive analysis of 141 actual vulnerabilities in SNARK implementations, providing a detailed taxonomy to aid developers and security researchers in understanding the security threats in systems employing SNARKs. Finally, we evaluate existing defense mechanisms and offer recommendations for enhancing the security of SNARK-based systems, paving the way for more robust and reliable implementations in the future.
Secure aggregation enables federated learning (FL) to perform collaborative training of clients from local gradient updates without exposing raw data. However, existing secure aggregation schemes inevitably perform an expensive fresh setup per round because each client needs to establish fresh input-independent secrets over different rounds. The latest research, Flamingo (S&P 2023), designed a share-transfer-based reusable secret key to support the server continuously performing multiple rounds of aggregation. Nevertheless, the share transfer mechanism it proposed can only be achieved with P probability, which has limited reliability. To tackle the aforementioned problems, we propose a more reliable and anonymously authenticated scheme called Chu-ko-nu for multi-round secure aggregation. Specifically, in terms of share transfer, Chu-ko-nu breaks the probability P barrier by supplementing a redistribution process of secret key components (the sum of all components is the secret key), thus ensuring the reusability of the secret key. Based on this reusable secret key, Chu-ko-nu can efficiently perform consecutive aggregation in the following rounds. Furthermore, considering the client identity authentication and privacy protection issue most approaches ignore, Chu-ko-nu introduces a zero-knowledge proof-based authentication mechanism. It can support clients anonymously participating in FL training and enables the server to authenticate clients effectively in the presence of various attacks. Rigorous security proofs and extensive experiments demonstrated that Chu-ko-nu can provide reliable and anonymously authenticated aggregation for FL with low aggregation costs, at least a 21.02% reduction compared to the state-of-the-art schemes.
Although online auctions have gained popularity as a marketplace format, they encounter challenges stemming from bidders withdrawing high bids without penalty, and wasting organizer resources due to redundant auction rounds. This behavior, known as overbidding, undermines efficiency and remains a longstanding issue in traditional auction designs. It’s worth noting that one of the common reasons for bid withdrawals is exceeding their financial capacity. Participants bid beyond their budgetary constraints, not necessarily with the goal of obtaining the asset but for alternative motives. In an effort to address these overbidding inefficiencies, this paper presents Zk-Auction, a decentralized blockchain-based cross-chain auction system. Zk-Auction leverages the privacy-preserving capabilities of zero-knowledge proofs to cryptographically verify bidder funds for each bid, deterring inflation beyond actual holdings. A novel sidechain architecture simulates interoperability between independent "Banking" and "Auction" blockchains, enabling bid authentication without revealing identities or balances. By tackling the core challenges of existing centralized auction platforms, Zk-Auction adopts a distributed off-chain matching and on-chain settlement approach to maintain security and interoperability across heterogeneous distributed ledgers. The integration of Zero-Knowledge Proofs in bid validation ensures a privacy-preserving manner. Additionally, a proof-of-concept prototype demonstrates feasibility at scale. Evaluations of the system show enhanced participant accountability, thereby incentivizing sincere bidding behavior in contrast to traditional designs that are susceptible to wasteful overbidding.
Jonathan Ku, Junyao Zhang, Haoxuan Shan, Saichand Samudrala · 9 authors
Elliptic curve cryptography (ECC) is widely used in security applications such as public key cryptography (PKC) and zero-knowledge proofs (ZKP). ECC is composed of modular arithmetic, where modular multiplication takes most of the processing time. Computational complexity and memory constraints of ECC limit the performance. Therefore, hardware acceleration on ECC is an active field of research. Processing-in-memory (PIM) is a promising approach to tackle this problem. In this work, we design ModSRAM, the first 8T SRAM PIM architecture to compute large-number modular multiplication efficiently. In addition, we propose R4CSA-LUT, a new algorithm that reduces the cycles for an interleaved algorithm and eliminates carry propagation for addition based on look-up tables (LUT). ModSRAM is co-designed with R4CSA-LUT to support modular multiplication and data reuse in memory with 52% cycle reduction compared to prior works with only 32% area overhead.
With the vigorous development of the social network, massive data have generated by large amount of users. To utilize these data, social computing is widely used. However, due to the conflict between proprietary data and the use of data, it is a challenge to fully obtain data value in an efficient and legal way. Moreover, how to manage, govern and schedule data, a technical and feasible approach is still a major problem. To this end, as an emerging distributed ledger technology, blockchain is considered a promising technology for data security and privacy and is introduced for cross-domain data governance. In this paper, we propose a blockchain-assisted cross-domain data sharing scheme for social computing data governance. Specifically, permissioned blockchain is introduced to construct trust among different parties. A zero-knowledge proof scheme is designed to verify data ownership confidentially and anonymously. The security of the zero-knowledge proof scheme is also analyzed. The extensive simulations and experiments have proven the effectiveness and efficiency of the mechanism.
Chhavi Yadav, Amrita Roy Chowdhury, Dan Boneh, Kamalika Chaudhuri
Machine learning models are increasingly used in societal applications, yet legal and privacy concerns demand that they very often be kept confidential. Consequently, there is a growing distrust about the fairness properties of these models in the minds of consumers, who are often at the receiving end of model predictions. To this end, we propose \name -- a system that uses Zero-Knowledge Proofs (a cryptographic primitive) to publicly verify the fairness of a model, while maintaining confidentiality. We also propose a fairness certification algorithm for fully-connected neural networks which is befitting to ZKPs and is used in this system. We implement \name in Gnark and demonstrate empirically that our system is practically feasible. Code is available at https://github.com/infinite-pursuits/FairProof.
Akila Rekima, Lieke van den Elsen, Charlotte Isnard, Danielle J. Smyth · 19 authors
Colostrum is the physiological food for the first 72 h of a newborn.1 Its window of intake and high content in microbiota-shaping and growth factors1 suggest that colostrum is critical in guiding gut immune development. To address this hypothesis, we developed a mouse model of colostrum deprivation (Figure 1A). Like humans, mice have different lactation stages.2 We compared pups nursed immediately after birth by dams that no longer produced colostrum (Day 9 of lactation, a well-defined lactation stage in mice that is distinct from colostrum2) with control pups. This allowed us to assess the causal role of colostrum in the perinatal expansion of two cell types important in gut immune regulation, namely ILCs and CD4+ T cells. While we found a major increase in small intestine ILC2 frequency and numbers between Days 7 and 14 in control mice, ILC2 expansion was severely compromised when mice were deprived of colostrum (Figure 1B). Colostrum deprivation did not impact gut ILC, ILC1, ILC3, CD4+ T cells, Th1, Th2, Th17 and Treg cells representation in 2-week-old mice (Figure S1), suggesting a selective effect of colostrum on ILC2 ontogeny. The low numbers of Th1, Th2 and Th17 cells are consistent with the predominantly naïve T cell compartment at this time point of life.3 A more detailed analysis of CD4 T-cell phenotype including T-cell activation and their response to inflammatory signals remains to be performed to fully elucidate the role of colostrum in T-cell ontogeny. To verify that the decreased representation of ILC2 in 14-day-old mice was due to the imprinting of a different trajectory due to the absence of colostrum at birth, we performed two additional experiments. First, we investigated whether the impact of the intervention on ILC2 at Day 14 was due to changes in diet at birth versus at later time points. Therefore, pups were cross-fostered at Day 10, instead of Day 0, to dams that gave birth 9 days earlier than their biological mothers. As shown in Figure S2A, their percentage and number of ILC2 at Day 14 were similar to ctrl mice demonstrating that ILC2 ontogeny is not affected by exposure to ‘old’ milk at the time when ILC2 massively expands. We then investigated whether the reduced ILC2 expansion in mice nursed from birth by dams at Day 9 of lactation was due to colostrum deprivation versus exposure to mature milk at birth. We cross-fostered pups at birth to dams that had delivered only 3 days earlier. Similar to mice nursed by mothers at Day 9 of lactation, we found a major decrease in the representation of ILC2 compared to control mice (Figure S2B), supporting the hypothesis that colostrum at birth is required for ILC2 ontogeny. Given the importance of the microbiota in gut immune ontogeny, we next evaluated whether colostrum shaped the gut microbiota.3 Both the alpha and beta diversity of the gut microbiota significantly differed between control and colostrum-deprived 2-week-old mice (Figure S3A,B). To address whether this difference played a causal role in decreased ILC2 expansion in colostrum-deprived mice, experiments were repeated in germ-free mice. As observed in specific pathogen-free mice, we found that colostrum deprivation resulted in a 30% decrease in small intestine ILC2 compared to control germ-free mice (Figure S3C), indicating that the role of colostrum in ILC2 ontogeny is microbiota independent. The alarmins, IL-33, IL-25 and TSLP, play a major role in ILC2 proliferation and/or activation.4 During the first days of lung alveolarization, transient high levels of IL-33 were found to promote ILC2 accumulation.5 We also observed a transient increase in IL-33 secretion in the gut of 4-day-old control mice, which was two-fold lower in colostrum-deprived mice (Figure 1C). In addition to IL-33, IL-25, which is known to be important for gut ILC2 expansion/activation was significantly reduced in colostrum-deprived mice at Day 4 (Figure 1C). TSLP has a synergistic effect on the proliferation and Type 2 cytokine production of ILC2 and may be particularly important in early life where IL-33 alone is not sufficient to activate cytokine secretion.4 We also found a trend towards reduced TSLP secretion in colostrum-deprived mice (Figure 1C). Altogether, these data strongly suggest an important role for colostrum in alarmins-driven perinatal ILC2 expansion. Whereas we found that colostrum intake affected neither the circulating pool of ILC2 nor the expression of gut-homing molecules CCR9 and α4β7 on small intestine ILC2 nor their proliferation (Figure S4A–C), we found a threefold increase in apoptotic ILC2 in colostrum-deprived 2-week-old mice compared to controls (Figure 1D). The reduction in alarmins secretion in colostrum-deprived mice may contribute to their increased apoptotic death of ILC2.4 Finally, we evaluate the functional consequences of a decreased representation of small intestine ILC2 in colostrum-deprived mice by measuring their gut content in IL-13 and their ability to clear helminth infection, which is known to involve ILC2.5 IL-13 levels in gut tissues form colostrum-deprived mice were significantly reduced compared to control mice (Figure 1E). When 3-week-old colostrum-deprived mice were infected with Heligmosomoides polygyrus, twice as many worms in the intestine and threefold more eggs in the faeces were found 21 days later, compared to control mice (Figure 1F), showing the decreased ability of colostrum-deprived mice to efficiently control helminth infection later in life. Our data suggest that the reduced representation of ILC2 underlies this increased susceptibility to helminth infection. Future studies will establish whether other characteristics of colostrum-deprived mice may explain this observation. As a first step in translating our findings to humans, we analyzed the association between delayed initiation of breastfeeding (based on WHO guidelines recommending initiation within 1 h6), a practice that deprives the newborn of the full dose of colostrum, and the susceptibility to helminth infection in young children. Three-hundred mothers and their children (aged 1–3 years) were recruited in Uganda, and data on early feeding practices were collected retrospectively (Table 1). Among mothers who initiated breastfeeding after 1 h, 78% initiated breastfeeding on the first day of life, 17% on Day 2 and 5% after 1 week. Delayed initiation of breastfeeding was strongly associated with an increased risk for helminth infection [OR (95% CI): 5.3 (1.9–15.06, p = .009)] (Figure 1G). Data remained significant after adjustment for maternal and child age, which differed between the two groups [aOR (95% CI): 6.6 (2–22)]. A limitation of this proof-of-concept study is the recall bias on early feeding practice. To fully establish the importance of colostrum in the prevention of helminth infections, prospective studies specifically addressing the relationship between the amount of colostrum feeding and helminth infections will be required. Mouse and human data show there is a massive infiltration of ILC2 in the infant small intestine7, 8; however, factors involved in this process remained unknown. This work uncovers a critical role for colostrum in gut ILC2 ontogeny and reveals its importance for anti-helminth defence. Given the importance of ILC2 in the regulation of allergic responses,5 future research will need to address the impact of colostrum deprivation at birth on allergy risk. Despite WHO guidelines, more than half of newborns globally are non-optimally colostrum-fed,6 which deprives the newborn of colostrum bioactives at a time of both high vulnerability and critical developmental change. There is strong evidence that optimal colostrum feeding has a major impact on the prevention of neonatal mortality, especially in low-and middle-income countries.9 Our data provide evidence that colostrum may also be fundamental in imprinting healthy immune development. Expanding the knowledge on colostrum bioactives responsible for ILC2 expansion should lead to a major impact on child health. Project design and supervision: VV. Conceptualization: VV (whole project), AR (whole project), ML (GF exp), DL (GF exp), RL (ILC2 ontogeny), RM (helminth mice), TE (helminth human) and RB (microbiota). Mice experiments, data analysis and interpretation: AR, LvdE, CI, SM, ND, CT, ML, NS, TY and VV. Human data collection and analysis: MB, CR and TE. Microbiota data analysis: FS, RB and VV. Writing—original draft: AR and VV. Writing—review and editing: All. The authors would like to thank Simone Ross and Caitlin Murray at the Harry Perkins Institute for Medical Research, the Gnotobiotic facilities and the technical assistance of staff in the South Australian Health and Medical Research Institute (SAHMRI) Preclinical Imaging and Research Laboratories (PIRL) and Translational Research Institute (TRI). Flow cytometry analysis was performed with the help of Catherine Rinaldi from the Cytometry Centre of Microscopy Characterisation, and Analysis (CMCA, UWA) and at the ACRF Cellular Imaging and Cytometry Core Facility in SAHMRI. The ACRF Facility is generously supported by the Detmold Hoopman Group, Australian Cancer Research Foundation and Australian Government through the Zero Childhood Cancer Program. We also thank Benjamin Lelouvier from Vaiomer for the data analysis. VV, LE, AR, SM, MB and ND were supported by the Larsson-Rosenquist Foundation. AR and LE were supported by a Raine collaborative grant award. DJL was supported by an EMBL Australia Group Leader award. RMM thanks the Wellcome Trust for support through an Investigator Award (Ref 219530), and core-funded Wellcome Centre for Integrative Parasitology (Ref: 104111). Florence Servant declares she is an employee of the “Vaiomer SAS” company. All the other authors declare they have no conflict of interest related to this publication. The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions. Data S1. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
It is well-known that digital signatures can be constructed from one-way functions in a black-box way. While one-way functions are essentially the minimal assumption in classical cryptography, this is not the case in the quantum setting. A variety of qualitatively weaker and inherently quantum assumptions (e.g. EFI pairs, one-way state generators, and pseudorandom states) are known to be sufficient for non-trivial quantum cryptography. While it is known that commitments, zero-knowledge proofs, and even multiparty computation can be constructed from these assumptions, it has remained an open question whether the same is true for quantum digital signatures schemes (QDS). In this work, we show that there $\textit{does not}$ exist a black-box construction of a QDS scheme with classical signatures from pseudorandom states with linear, or greater, output length. Our result complements that of Morimae and Yamakawa (2022), who described a $\textit{one-time}$ secure QDS scheme with classical signatures, but left open the question of constructing a standard $\textit{multi-time}$ secure one.
This paper introduces the zk-IoT framework, a novel approach to enhancing the security of Internet of Things (IoT) ecosystems through the use of Zero-Knowledge Proofs (ZKPs) on blockchain platforms. Our framework ensures the integrity of firmware execution and data processing in potentially compromised IoT devices. By leveraging the concept of ZKP, we establish a trust layer that facilitates secure, autonomous communication between IoT devices in environments where devices may not inherently trust each other. The framework includes zk-Devices, which utilize functional commitment to generate proofs for executed programs, and service contracts for encoding interaction logic among devices. It also utilizes a blockchain layer and a relayer as a ZKP storage and data communication protocol, respectively. Our experiments demonstrate that proof generation, reading, and verification take approximately 694, 5078, and 19 milliseconds in our system setup, respectively. These timings meet the practical requirements for IoT device communication, demonstrating the feasibility and efficiency of our solution. The zk-IoT framework represents a significant advancement in the realm of IoT security, paving the way for reliable and scalable IoT networks across various applications, such as smart city infrastructures, healthcare systems, and industrial automation.
Shrabani Sutradhar, S. P. Majumder, Rajesh Bose, Haraprasad Mondal · 5 authors
The Internet of Medical Things (IoMT) has transformed healthcare, collecting and transmitting vast medical data. This study proposes an innovative solution, integrating blockchain into IoMT within a fog-cloud computing framework for secure medical data transmission. The blockchain-based zero-trust system ensures reliable data auditing and Electronic Health Repository (EHR) protection. New blockchain entries atop prior blocks deter tampering, with Quad Merkle tree and zero-knowledge proof encryption ensuring data integrity and privacy. The multi-critic deep deterministic policy gradient algorithm optimizes task-offloading decisions in the fog-cloud layer. Security analyses validate its effectiveness, improving computing efficiency and ensuring data fidelity and privacy. These findings position the system as a promising solution for enhancing medical data security and integrity in edge-fog cloud environments, aligning with evolving healthcare technology demands.
Bianca-Mihaela Ganescu, Jonathan Passerat‐Palmbach
Generative AI, exemplified by models like transformers, has opened up new possibilities in various domains but also raised concerns about fairness, transparency and reliability, especially in fields like medicine and law. This paper emphasizes the urgency of ensuring fairness and quality in these domains through generative AI. It explores using cryptographic techniques, particularly Zero-Knowledge Proofs (ZKPs), to address concerns regarding performance fairness and accuracy while protecting model privacy. Applying ZKPs to Machine Learning models, known as ZKML (Zero-Knowledge Machine Learning), enables independent validation of AI-generated content without revealing sensitive model information, promoting transparency and trust. ZKML enhances AI fairness by providing cryptographic audit trails for model predictions and ensuring uniform performance across users. We introduce snarkGPT, a practical ZKML implementation for transformers, to empower users to verify output accuracy and quality while preserving model privacy. We present a series of empirical results studying snarkGPT's scalability and performance to assess the feasibility and challenges of adopting a ZKML-powered approach to capture quality and performance fairness problems in generative AI models.
This article discusses the issues with traditional scientific publishing and the solutions offered by DeSci. It covers problems caused by oligopoly in scientific publishing, author-reviewer-editor triangulation, predatory journals, and funding and resource allocation. These issues result in gated publishing, peer review bias, publish or perish culture, centralized funding, poor publication quality, low accessibility, low transparency, and low reproducibility. This study discusses DeSci's solutions to the aforementioned problems through blockchain technologies, including DAO, DBDAO, NFT, Zero-Knowledge Proofs, IP-NFT, and IPFS. Sample applications and projects are provided to illustrate these solutions. DeSci's solutions represent a transition from oligopoly in scientific production to the true Open Science era.
Yash Bobde, Gokuleshwaran Narayanan, Manas Jati, S. P. Raja · 6 authors
In the rapidly evolving landscape of industrial ecosystems, Industrial IoT networks face increasing security challenges. Traditional security methods often struggle to protect these networks adequately, posing risks to data integrity, confidentiality, and access control. Our research introduces a methodology that leverages blockchain technology to enhance the security and trustworthiness of IoT networks. This approach starts with sensor nodes collecting and compressing data, followed by encryption using the ChaCha20-Poly1305 algorithm and transmission to local aggregators. A crucial element of our system is the private blockchain gateway, which processes and classifies data based on confidentiality levels, determining their storage in cloud servers or the Interplanetary File System for enhanced security. The system’s integrity and authenticity are further reinforced through the proof of authority consensus mechanism. This system employs Zero Knowledge Proof challenges for device authorization, optimizing data retrieval while maintaining a delicate balance between security and accessibility. Our methodology contributes to mitigating vulnerabilities in Industrial IoT networks and is part of a broader effort to advance the security and operational efficiency of these systems. It reflects an understanding of the diverse and evolving challenges in IoT security, emphasizing the need for continuous innovation and adaptation in this dynamic field.
Tácito Augusto Farias Júnior, Rafael Oliveira Vasconcelos, Admilson de Ribamar Lima Ribeiro
Privacy protection ensures that individuals have control over personal data, preventing abuse and preserving trust in the use of online services. In the “Digital Era”, where the collection, storage and processing of personal information have become ubiquitous, data privacy emerges as a relevant topic. In this sense, laws were created, such as the General Data Protection Law (LGPD) in Brazil and the General Data Protection Regulation (GDPR) in Europe, to control privacy and the processing of personal data. The article presents a comparative analysis of 2 (two) data privacy mechanisms, the Zero-Knowledge Proof (ZKP) and Ring Signatures, used in Blockchain, aiming at the legal and regulatory implications with the LGPD and GDPR. The comparative study between ZKP and Ring Signatures highlights the flexibility of ZKP in various contexts, including voting and secure authentication systems, while Ring Signatures offer significant advantages in terms of scalability and efficiency in systems where subscriber anonymity is considered fundamental. Furthermore, the legal and regulatory implications of the ZKP are discussed, mainly in relation to LGPD and GDPR. Finally, the article concludes that the comparative analysis offers insights into applications, challenges and legal and regulatory implications, particularly in relation to data privacy and compliance with regulations such as LGPD and GDPR.
Federated leaning (FL) has been frequently used in various field of studies and businesses. Traditional centralized FL systems suffer from serious issues. To address these concerns, decentralized federated learning (DFL) systems have been introduced in recent years in which with the help of blockchains, try to achieve more integrity and efficiency. On the other hand, privacy-preserving is an uncovered part of these systems. To address this, and also scaling the blockchain-based computations, we propose a zero knowledge proof (ZKP) based aggregator (zkDFL) that allows clients to share their large-scale model parameters with a trusted centralized server without revealing their individual data to other clients. We utilize blockchain technology to manage the aggregation algorithm via smart contracts. The server performs a ZKP algorithm to prove to the clients that the aggregation is done according to the accepted algorithm. The server can also prove that all inputs of clients have been used. We evaluate our measure through a public dataset about wearable internet of things. As demonstrated by numerical evaluations, zkDFL introduces verifiability of correctness of aggregation process and enhances the privacy protection and scalability of DFL systems, while the gas cost has declined significantly.
Abstract In the evolving landscape of global education, the significance of inclusivity and equity has never been more important. Emphasizing the United Nation Sustainable Development Goal 4, this paper explores the innovative application of blockchain-powered Zero-Knowledge Proofs (ZKPs) technology in education, with a particular focus on disability inclusion. This study introduces a novel disability management system powered by Zero-Knowledge Succinct Non-Interactive Argument of Knowledge (zk-SNARK). This advanced system enables educational institutions to verify the status of students with disabilities without compromising their personal information, thereby preserving their privacy and reinforcing their identity. This paper evaluates the potential operational efficiency of this prototype system against the existing costs incurred by higher education institutions in disability schemes. It also examines the system’s potential to enhance self-disclosure among students with disability, which is pivotal for their academic success. By advocating for privacy and inclusivity, this study highlights the transformative potential of ZKP in creating an educational environment where students with disabilities can comfortably disclose their needs. This approach not only protects their confidentiality but also empowers them academically, aligning with the global commitment to accessible and inclusive education.
Cryptography has always been the science of Secure Communication. The past decade has been epitomized by the emergence of Blockchain Technology, without leaving Cryptography unaffected. The manifesto of Blockchains is Decentralization, making it inevitable that the information is stored and verified in real-time by thousands of participants. This spotlighted two necessities: information should be as concise as possible and verification of processes should be fast. From a cryptographic perspective, this translates to a central desideratum: Succinctness. A cryptographic construction is called Succinct if its algorithm is generating outputs that are (exponentially) smaller than the inputs. This allows the cryptosystem to treat large data and produce concise outputs that, nevertheless, preserve the desired functionality of the system. In this thesis, we are concerned with a specific type of Succinct cryptographic primitives: Succinct Commitments. Cryptographic commitments are objects that allow one to commit to some data, providing a binding representative. Then at any later point they can open back the (committed) data providing an opening proof, but without being able to open differently the representative. In more detail, in our work we deal with commitments with more fine-grained openings, where one can generate an opening proof of the commitment to a function f (m) of the initial data m. Firstly, we deal with set commitments with private (non-)memberhsip openings. We construct succinct zero-knowledge proofs for the problem of set (non-)membership. Intuitively, a zero- knowledge proof is a cryptographic primitive that allows one to prove a statement, in a sound way, without leaking any other information except for the fact that the statement holds. In a zero-knowledge proof for set membership first one commits to a public set and then a party can prove membership to the set but without betraying which element of the set exactly is. Such (set) commitments with this type of fine-grained openings are the cornerstone of Anonymous Cryptocurrencies such as Zcash. In particular we provide efficient zero-knowledge proofs for the opening of RSA accumulators, one of the most popular set commitments. First, we show efficient protocols for membership and non-membership of a single element. Then we construct succinct zero-knowledge membership proofs for multiple elements, where the size of the proof is independent of the number of elements proven. The two techniques are qualitatively different. Secondly, we switch our attention to Vector Commitments, with local positional openings. We put forth the notion of Incremental Aggregation, in which one can arbitrarily aggregate opening proofs of any positions into a single (concise) proof and inversely disaggregate a proof of multiple points to many. We show applications of this notion (1) to speeding up the proof computation by using precomputation and moderate-sized precomputed values and (2) to Verifiable Decentralized Storage. Finally, we provide efficient construction of Incrementally Aggregatable Vector Commitments from Groups of Unknown Order. Thirdly, we turn to Functional Commitments, for linear functions, where one commits to a vector v and then can open f(v) = y, for a public f. We construct functional commitments that admit constant-sized public parameters and proofs. To this end, our core technique is a novel succinct protocol of cardinality for a set committed with an RSA accumulator, which is in turn based on a Range Proof. Finally, we show a generic way to turn any Vector Commitment into a Key-Value Map Commitment for arbitrary keys. A Key-Value Map resembles a Vector but the ordering of the values is not characterized by subsequent indices but by arbitrary keys. Key-Value Maps are the core data-structures in Cryptocurrencies like Ethereum. Our construction of Key-Value Map Commitments is generic and is based on a novel cryptographic application of Cuckoo-Hashing. RESUMEN La última década se ha caracterizado por la aparición de la tecnología Blockchain, afectando la criptografía. El manifiesto de Blockchains es la Descentralización, en la que la información es guardada y verificada en tiempo real por miles de participantes. Esto centra la atención en dos necesidades: la información debe ser lo más concisa posible y la verificación de los procesos debe ser rápida. Desde una perspectiva criptográfica, esto se traduce en un desiderátum central: la Compacidad. En esta tesis, nos ocupamos de un tipo específico de primitivas criptográficas compactas: Compromisos Compactos. Los compromisos criptográficos son objetos que permiten comprom- eterse con algunos datos, proporcionando un representante vinculante, de modo que en cualquier momento posterior se pueden volver a abrir, proporcionando una prueba de apertura. En nuestro trabajo tratamos compromisos con aperturas más detalladas, donde se puede generar una prueba de apertura del compromiso con una función f (m) de los datos iniciales m. En primer lugar, nos ocupamos de compromisos de conjuntos con aperturas privadas de (no) pertenencia. Construimos pruebas compactas de conocimiento cero para el problema de la (no) pertenencia a conjuntos. Una prueba de conocimiento cero es una primitiva criptográfica que permite probar una afirmación, de forma sólida, sin filtrar ninguna otra información excepto el hecho de que la afirmación es cierta. En una prueba de conocimiento cero para la pertenencia a un conjunto, primero uno se compromete con un conjunto público y luego una parte puede demostrar la pertenencia al conjunto, pero sin revelar qué elemento del conjunto es exactamente. Estos compromisos de conjuntos con este tipo de aperturas detalladas son la piedra angular de las criptomonedas anónimas como Zcash. En particular, proporcionamos pruebas eficientes de conocimiento cero para la apertura de acumuladores RSA, uno de los compromisos establecidos más populares. Primero, mostramos protocolos eficientes para la membresía y no membresía de un solo elemento. Luego construimos pruebas compactas de membresía de conocimiento cero para múltiples elementos, donde el tamaño de la prueba es independiente del número de elementos probados. Las dos técnicas son cualitativamente diferentes. En segundo lugar, centramos nuestra atención en los compromisos de vectores, con aperturas posicionales locales. Presentamos la noción de Agregación Incremental, en la que se pueden agregar arbitrariamente pruebas de apertura de cualquier posición en una prueba única (concisa) e inversamente desagregar una prueba de múltiples puntos en muchos. Mostramos aplicaciones de esta noción (1) para acelerar el cálculo de la prueba mediante el uso de precómputo y valores precalculados de tamaño moderado y (2) para el Almacenamiento Descentralizado Verificable. Finalmente, proporcionamos una construcción eficiente de compromisos vectoriales incrementalmente agregables a partir de grupos de orden desconocido. En tercer lugar, pasamos a los compromisos funcionales, para funciones lineales, donde uno se compromete con un vector v y luego puede abrir f(v) = y, para un f público. Construimos compromisos funcionales que admiten pruebas y parámetros públicos de tamaño constante. Con este fin, nuestra técnica principal es un protocolo novedoso y compacto de cardinalidad para un conjunto comprometido con un acumulador RSA, que a su vez se basa en una prueba de rango. Finalmente, mostramos una forma genérica de convertir cualquier compromiso de vector en un compromiso de mapa-de-valores-clave para claves arbitrarias. Un mapa-de-valores-clave se parece a un vector, pero el orden de los valores no se caracteriza por índices posteriores sino por claves arbitrarias. Los mapas-de-valores-clave son las estructuras de datos centrales en criptomonedas como Ethereum. Nuestra construcción de compromisos de mapas de valores clave es genérica y se basa en una novedosa aplicación criptográfica de Cuckoo-Hashing.
Ruben De Smet, Robrecht Blancquaert, Tom Godden, Kris Steenhaut · 5 authors
Elliptic curve cryptography is a widely deployed technology for securing digital communication. It is the basis of many cryptographic primitives such as key agreement protocols, digital signatures, and zero-knowledge proofs. Fast elliptic curve cryptography relies on heavily optimised modular arithmetic operations, which are often tailored to specific micro-architectures. In this article, we study and evaluate optimisations of the popular elliptic curve Curve25519 for ARM processors. We specifically target the ARM NEON single instruction, multiple data (SIMD) architecture, which is a popular architecture for modern smartphones. We introduce a novel representation for 128-bit NEON SIMD vectors, optimised for SIMD parallelisation, to accelerate elliptic curve operations significantly. Leveraging this representation, we implement an extended twisted Edwards curve Curve25519 back-end within the popular Rust library "curve25519-dalek". We extensively evaluate our implementation across multiple ARM devices using both cryptographic benchmarks and the benchmark suite available for the Signal protocol. Our findings demonstrate a substantial back-end speed-up of at least 20% for ARM NEON, along with a noteworthy speed improvement of at least 15% for benchmarked Signal functions.
In a world of increasing closed-source commercial machine learning models, model evaluations from developers must be taken at face value. These benchmark results-whether over task accuracy, bias evaluations, or safety checks-are traditionally impossible to verify by a model end-user without the costly or impossible process of re-performing the benchmark on black-box model outputs. This work presents a method of verifiable model evaluation using model inference through zkSNARKs. The resulting zero-knowledge computational proofs of model outputs over datasets can be packaged into verifiable evaluation attestations showing that models with fixed private weights achieve stated performance or fairness metrics over public inputs. We present a flexible proving system that enables verifiable attestations to be performed on any standard neural network model with varying compute requirements. For the first time, we demonstrate this across a sample of real-world models and highlight key challenges and design solutions. This presents a new transparency paradigm in the verifiable evaluation of private models.