Michael Herbert Ziegler, Mariusz Nowostawski, Basel Katt
In this literature review, we critically examine the evolving landscape of privacy in blockchain systems, with a particular focus on the differentiation of privacy attacks and protective measures across three distinct layers: the on-chain layer; the off-chain layer; and on the infrastructure, i.e., peer-to-peer network layer. In this review, we categorize prevalent privacy attacks, such as transaction tracing, data leakage, and network surveillance, highlighting their implications at each layer. In addition, we evaluate a range of protective techniques, including cryptographic methods, zero-knowledge proofs, and other privacy-preserving protocols. We explore the compatibility of these privacy techniques with existing blockchain systems. By synthesizing current research and practical implementations, our aims are to provide a comprehensive understanding of privacy challenges and solutions in blockchain environments, identify gaps, and guide future developments in privacy-enhancing technologies within the blockchain ecosystem.
Statistical witness indistinguishability is a relaxation of statistical zero-knowledge which guarantees that the transcript of an interactive proof reveals no information about which valid witness the prover used to generate it. In this paper we define and initiate the study of QSWI, the class of problems with quantum statistically witness indistinguishable proofs. Using inherently quantum techniques from Kobayashi (TCC 2008), we prove that any problem with an honest-verifier quantum statistically witness indistinguishable proof has a 3-message public-coin malicious-verifier quantum statistically witness indistinguishable proof. There is no known analogue of this result for classical statistical witness indistinguishability. As a corollary, our result implies SWI is contained in QSWI. Additionally, we extend the work of Bitansky et al. (STOC 2023) to show that quantum batch proofs imply quantum statistically witness indistinguishable proofs with inverse-polynomial witness indistinguishability error.
Designing secure electronic voting systems that truly protect voter privacy, ensure vote accuracy, and allow independent verification continues to pose serious difficulties. Many current cryptographic approaches require excessive computational resources and use encryption keys that are too large for practical implementation. This paper proposes modifications to the Chaum, Pedersen and Cramer, Franklin, Schoenmakers, and Yung voting protocols by integrating elliptic curve cryptography (ECC), which offers stronger security per bit and more compact key representations. The use of ECC allows for reduced parameter sizes while maintaining resistance against known attacks, including those targeting the discrete logarithm problem. We present detailed adaptations of these protocols on elliptic curves and demonstrate how they preserve core security properties such as vote secrecy, universal verifiability, and resistance to double voting under a more efficient cryptographic framework. Our findings contribute to the development of scalable, high-assurance e-voting mechanisms suitable for modern digital infrastructures. The presented modifications significantly enhance the scalability and efficiency of e-voting systems without compromising cryptographic strength.
Prizadevanje za vzpostavitev evropskega okvira za digitalno identiteto je leta 2024 doprineslo do pomembnega koraka naprej, saj je 20. maja 2024 začela veljati novela EU uredbe št. 910/2014 za e-identifikacijo in storitve zaupanja, ki jo poznamo tudi kot Uredba eIDAS 2.0. Ta vzpostavlja pravno podlago za uvedbo evropske denarnice za digitalno identiteto po vsej EU. Z denarnico bodo uporabniki lahko tudi varno pridobili, shranili in delili svoje pomembne dokumente, npr. o izobrazbi in licencah, pooblastila za zastopanje pravnih oseb, finančne podatke in podatke o družbah, ter elektronsko podpisovali oz. v primeru denarnic za podjetja elektronsko žigosali dokumente. Da bi dosegli interoperabilnost med denarnicami, izdanimi s strani držav članic, so v izvedbenih aktih k Uredbi eIDAS 2.0 določeni standardi za evropsko denarnico, ki jih morajo upoštevati vse implementacije denarnic po državah, pravila za certificiranje denarnic in sporočanje Evropski komisiji. Skupne zahteve za denarnico se pripravljajo v okviru Arhitekturnega in referenčnega okvirja (ARF), poleg tega pa Evropska komisija pripravlja tudi referenčno implementacijo denarnice. V prispevku so podrobneje predstavljene nekatere visokonivojske zahteve ARF, ki se nanašajo na področje zasebnosti, še posebej uporaba metod ničelno spoznalnih dokazov (angl. Zero knowledge Proof) za zagotavljanje zasebnosti v ekosistemu denarnic.
Bruno M. F. Ricardo, Lucas C. Cardoso, Leonardo T. Kimura, Marcos A. Simplício · 5 authors
In 2023, Barreto and Zanon proposed a three-round Schnorr-like blind signature scheme, leveraging zero-knowledge proofs to produce one-time signatures as an intermediate step of the protocol. The resulting scheme, called BZ, is proven secure in the discrete-logarithm setting under the one-more discrete logarithm assumption with (allegedly) resistance to the Random inhomogeneities in a Overdetermined Solvable system of linear equations modulo a prime number p attack, commonly referred to as ROS attack. The authors argue that the scheme is resistant against a ROS-based attack by building an adversary whose success depends on extracting the discrete logarithm of the intermediate signing key. In this paper, however, we describe a distinct ROS attack on the BZ scheme, in which a probabilistic polynomial-time attacker can bypass the zero-knowledge proof step to break the one-more unforgeability of the scheme. We also built a BZ variant that, by using one secure hash function instead of two, can prevent this particular attack. Unfortunately, though, we show yet another ROS attack that leverages the BZ scheme’s structure to break the one-more unforgeability principle again, thus revealing that this variant is also vulnerable. These results indicate that, like other Schnorr-based strategies, it is hard to build a secure blind signature scheme using BZ’s underlying structure.
Jingcheng Zhang, Yekai Zhou, Yingxuan Ren, Man Ho Au · 9 authors
Advancements in sequencing technologies grant individuals unprecedented access to their genomic data. However, existing data management systems or protocols are inadequate in privacy protection, limiting individuals' control over their genomic information, hindering data sharing, and posing challenges for biomedical research. Therefore, demand exists for an owner-governed system fulfilling owner authority, life cycle data encryption, and verifiability simultaneously. Here, we realized Governome, an owner-governed data management system empowering individuals with real-time control over their genomic data. Governome leverages a blockchain to manage transactions and permissions, granting data owners dynamic permission management with full transparency on data usage. It uses homomorphic encryption and zero-knowledge proofs to enable genomic data storage and computation in an encrypted and verifiable form throughout its life cycle. Governome can support versatile genomic applications. We implemented and tested individual variant query, cohort study, genome-wide association study (GWAS) analysis, and forensics on 2,504 1000 Genomes Project (1kGP) genomes, demonstrating its robustness and scalability. Governome is open-source at https://github.com/HKU-BAL/Governome.
Abstract: Ensuring the integrity, privacy and accessibility of electoral system remains a critical global challenge. This paper proposes a secure blockchain based e-voting framework enhanced with anti-spoofing facial recognition for voter authentication and zero-knowledge proofs to preserve voter anonymity while enabling verifiable results. The proposed system integrates seamlessly with existing election infrastructure, allowing transparent vote recording on a tamper-resistant distributed ledger while preventing identity fraud through advanced biometric anti-spoofing techniques. Zero Knowledge Proofs enable vote verification without revealing individual choices, ensuring both privacy and trust. By combining blockchain’s immutability, biometric security and cryptographic privacy guarantees, this approach addresses vote tampering, impersonation, and transparency concerns, offering a scalable , auditable, and privacy-preserving solution for modern elections. Keywords: Blockchain, E-Voting, Anti-Spoofing, Facial Recognition, Zero Knowledge Proofs, Election Security, Privacy preserving systems.
Open access
Internet Traffic Analysis and Secure E-voting
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
In the rapidly evolving landscape of blockchain technology, the twin challenges of scalability and security remain significant obstacles to widespread adoption. Traditional blockchain architectures struggle to balance the increasing demands for transaction throughput and the imperative of maintaining robust security measures. This work addresses these limitations by proposing an innovative model that integrates advanced privacy mechanisms, rigorous security analysis, and scalability enhancements to forge a more resilient and efficient blockchain framework. The cornerstone of our model is the introduction of ZeroKnowledge Proofs (ZKPs) to enhance user privacy significantly. By enabling transaction verification without revealing sensitive information, ZKPs mitigate information leakage and boost transaction confidentiality. Our findings suggest an estimated 15% improvement in privacy levels, marking a substantial advancement over existing methods that often compromise user privacy for transparency. Addressing the security aspect, we employ Temporal Logic of Actions Plus (TLA+) for formal verification of the blockchain protocol. This method allows us to model the blockchain's behavior systematically, ensuring its correctness, safety, and liveness even under adverse conditions such as Byzantine faults. Our analysis reveals a 98% success rate in detecting and thwarting Byzantine behaviors, thereby substantiating the robustness of our proposed model against a range of security threats. To tackle the issue of scalability, we introduce adaptive sharding with dynamic load balancing. This approach not only partitions the network into manageable shards but also optimizes transaction processing by adapting to changes in transaction volume and network congestion. Our results prove a 20% increase in transaction throughput and a 25% decrease in network latency, showcasing the effectiveness of adaptive sharding in enhancing blockchain scalability and performance.
Aiming at the problems of insufficient integrity assurance, high risk of privacy leakage, and low cross-departmental circulation efficiency in the field of government data security, this study proposes an optimized blockchain solution integrating an improved Practical Byzantine Fault Tolerance (PBFT) consensus algorithm and dynamic Attribute-Based Encryption (ABE). By constructing a government data security storage model, the data layer's hash verification mechanism (SHA-256 + Merkle Patricia Tree) and network layer's P2P protocol (with packet loss retransmission) are optimized based on typical government blockchain platforms (e.g., Nanjing Electronic License Sharing Platform). Experiments with 200 nodes simulated government networks, comparing TPS, data verification delay, and privacy protection before and after optimization. The results show that after optimization: TPS increased by 37.2% (from 89.6 to 123.0), cross-departmental data verification time was shortened to 0.42 seconds, and zero-knowledge proof was used to realize anonymized query of sensitive fields (such as ID card numbers and real estate information). The research indicates that this technical solution can effectively resolve the contradictions among integrity, privacy, and circulation of government data, providing technical references for the engineering implementation of government blockchain systems.
This paper studies the practical aspects of adding zero-knowledge proofs of vote correctness to Internet voting, specifically to the IVXV system used in Estonia. We discuss various available alternatives and present a concrete instantiation based on Bulletproofs together with implementation details and benchmarking results. As IVXV currently uses the ElGamal cryptosystem with a 3072-bit prime modulus for vote encryption, but Bulletproofs work most efficiently on elliptic curves, a group switching solution is also implemented and benchmarked. Despite all the extra work required, our solution is very performant and well capable of sustaining the load of votes, even during peak vote submission periods.
Maria Nuțu, Giorgi Akhalaia, Răzvan Bocu, Maksim Iavich
Commitment schemes represent foundational cryptographic primitives enabling secure verification protocols across diverse applications, from blockchain systems to zero-knowledge proofs. This paper presents a systematic survey of vector, polynomial, and functional commitment schemes, analyzing their evolution from classical constructions to post-quantum secure alternatives. We examine the strengths and limitations of RSA-based, Diffie–Hellman, and lattice-based approaches, highlighting the critical shift toward quantum-resistant designs necessitated by emerging computational threats. The survey reveals that while lattice-based schemes (particularly those using the Short Integer Solution problem) offer promising security guarantees, they face practical challenges in proof size and verification efficiency. Functional commitments emerge as a powerful generalization, though their adoption is constrained by computational overhead and setup requirements. Key findings identify persistent gaps in adaptive security, composability, and real-world deployment, while proposed solutions emphasize optimization techniques and hybrid approaches. By synthesizing over 90 research works, this paper provides both a comprehensive reference for cryptographic researchers and a roadmap for future developments in commitment schemes, particularly in addressing the urgent demands of post-quantum cryptography and decentralized systems.
Fine-tuning large language models (LLMs) is crucial for adapting them to specific tasks, yet it remains computationally demanding and raises concerns about correctness and privacy, particularly in untrusted environments. Although parameter-efficient methods like Low-Rank Adaptation (LoRA) significantly reduce resource requirements, ensuring the security and verifiability of fine-tuning under zero-knowledge constraints remains an unresolved challenge. To address this, we introduce VeriLoRA, the first framework to integrate LoRA fine-tuning with zero-knowledge proofs (ZKPs), achieving provable security and correctness. VeriLoRA employs advanced cryptographic techniques -- such as lookup arguments, sumcheck protocols, and polynomial commitments -- to verify both arithmetic and non-arithmetic operations in Transformer-based architectures. The framework provides end-to-end verifiability for forward propagation, backward propagation, and parameter updates during LoRA fine-tuning, while safeguarding the privacy of model parameters and training data. Leveraging GPU-based implementations, VeriLoRA demonstrates practicality and efficiency through experimental validation on open-source LLMs like LLaMA, scaling up to 13 billion parameters. By combining parameter-efficient fine-tuning with ZKPs, VeriLoRA bridges a critical gap, enabling secure and trustworthy deployment of LLMs in sensitive or untrusted environments.
This paper presents a comprehensive literature review on the application of blockchain technology in ensuring data integrity and security within decentralized applications (dApps). Blockchain, with its inherent features such as immutability, decentralization, and transparency, offers a robust framework for safeguarding data across various sectors, including finance, healthcare, and supply chain management. Through an extensive qualitative analysis of existing studies, this research explores how cryptographic techniques, consensus mechanisms, and blockchain's distributed nature contribute to securing data in decentralized environments. The review examines key findings from the literature, including the integration of advanced cryptographic methods such as zero-knowledge proofs and homomorphic encryption, which enhance data privacy while maintaining integrity. Furthermore, the study discusses the challenges of scalability, energy consumption, and off-chain data security in blockchain-based systems, identifying areas for future research. The review also highlights the importance of hybrid blockchain models and scalable consensus algorithms in addressing the limitations of current blockchain frameworks. Overall, this paper contributes to the growing body of knowledge on blockchain-based data security and integrity in decentralized applications and offers recommendations for further research to enhance the scalability, efficiency, and environmental sustainability of blockchain systems.
Observational zero-inflated count data arise in a wide range of areas such as genomics. One of the common research questions is to identify causal relationships by learning the structure of a sparse directed acyclic graph (DAG). While structure learning of DAGs has been an active research area, existing methods do not adequately account for excessive zeros and therefore are not suitable for modeling zero-inflated count data. Moreover, it is often interesting to study differences in the causal networks for data collected from two experimental groups (control vs treatment). To explicitly account for zero-inflation and identify differential causal networks, we propose a novel Bayesian differential zero-inflated negative binomial DAG (DAG0) model. We prove that the causal relationships under the proposed DAG0 are fully identifiable from purely observational, cross-sectional data, using a general proof technique that is applicable beyond the proposed model. Bayesian inference based on parallel-tempered Markov chain Monte Carlo is developed to efficiently explore the multi-modal posterior landscape. We demonstrate the utility of the proposed DAG0 by comparing it with state-of-the-art alternative methods through extensive simulations. An application in a single-cell RNA-sequencing dataset generated under two experimental groups finds some interesting results that appear to be consistent with existing knowledge. A user-friendly R package that implements DAG0 is available at https://github.com/junsoukchoi/BayesDAG0.git.
Quantum pseudorandomness is an emerging research area. Ji, Liu, and Song defined pseudorandom states (PRSs) and pseudorandom unitaries (PRUs) as quantum analogs of pseudorandom generators and pseudorandom functions. A unitary oracle separation result between one-way functions and PRSs/PRUs, established by Kretschmer, suggests that certain quantum primitives may remain secure even if classical cryptography is compromised. This insight has spurred extensive work on quantum pseudorandomness and its applications in quantum cryptography. Many constructions of PRSs have been established under standard assumptions, yet building a secure PRU was a long-standing open problem. This dissertation aims to narrow the gap between PRSs and PRUs and presents results that go beyond PRSs. We introduce Pseudorandom State Scramblers (PRSSs), a new primitive that lies between PRSs and PRUs. A PRSS maps any pure state to a pseudorandom state, a property shared with PRUs but not with PRSs. We present a construction of PRSSs inspired by the well-known Kac’s walk, and in particular, we develop a parallel variant that significantly accelerates the mixing time, enabling an efficient construction. PRSSs support cryptographic tasks not known to be achievable from PRSs alone, including a quantum encryption scheme and a succinct quantum state commitment. Additionally, when suitable classical randomness is provided, our construction exhibits a special dispersing property not known to be satisfied by any existing construction of quantum pseudorandom primitives. Our subsequent work shows that, without asymptotically increasing the number of steps, our construction based on the parallel Kac’s walk yields PRUs with standard or even strong security. The proof builds on a recently developed technique for establishing adaptive security, known as the path-recording method. This result provides an alternative construction of PRUs and further showcases the power of this proof technique. In addition, this dissertation includes two side projects. The first revisits the Hidden Subgroup Problem over ℤn, providing a simplified analysis of a known quantum algorithm using elementary lattice tools. The second establishes a quantum analogue of a classical impossibility result for statistical non-interactive zero-knowledge arguments, showing limitations of black-box reductions under classical-query quantum adversaries.
With the widespread application of blockchain technology, the security of private transactions has become a bottleneck restricting further development. This project presents a blockchain privacy transaction optimization model utilizing zero-knowledge proof (ZKP). By extracting data features such as transaction volume, transaction frequency, and counterparty trustworthiness, the model dynamically assigns weights through an entropy-based framework for different transaction scenarios. It also adaptively modifies certificate generation and verification strategies using reinforcement learning to enhance efficiency and security. In terms of experiments, a blockchain simulation environment is constructed, and 100,000 transaction data points are used as samples to compare the DA-ZKP algorithm and the traditional zero-knowledge proof algorithm. The experimental results show that the DA-ZKP algorithm reduces the generation time by 35%, the verification time by 28%, and the memory overhead by 22% on average. At the same time, the algorithm has a privacy protection capability comparable to traditional algorithms and can resist replay and tampering attacks. The optimization model and algorithm proposed in this project can effectively improve the efficiency and security of blockchain privacy transactions and provide a new idea for developing blockchain privacy protection technology.
Kriti Patidar, Swapnil Jain, Mohammad Husain, Mohd Muqeem · 9 authors
As IoT-connected devices, sometimes referred to as the Internet of Things (IoT), continue to proliferate, existing centralized identity management systems struggle in the large scale due to issues with scalability, privacy and security. For these reasons, centralized identity management systems will not meet the requirements of large-scale IoT deployments. In this paper, we suggest a decentralized identity management system to authenticate and authorize IoT devices based on a hybrid blockchain and Zero-Knowledge Proof (ZKP) protocol. The proposed system utilizes decentralized identifiers (DIDs), verifiable credentials (VCs) and a hierarchical web-of-trust structure as part of the identity management process. The identity and credentials can be created and validated in a decentralized manner and locally, using smart contracts and lightweight consensus models such as Proof of Stake (PoS) and Practical Byzantine Fault Tolerance (PBFT). The performance evaluation demonstrated the performance in respect of authentication latency businesses managed to get the latency to 250 ms, throughput reaching to 200 messages per second and energy efficiency improved to 300mW/device. Based on the baseline comparisons including PoW, OAuth and Hash-MAC based systems included, the proposed method is scalably better, provides greater security against DDoS and MITM attacks and used less memory. The proposed method yields a robust, fully decentralized identification system for managing IoT identities without requiring a centralized authority, allowing scalable and secure interactions across distributed networks.
Yagmur Yigit, Mehmet Ali Erturk, Kerem Gursu, Berk Canberk
Digital twin (DT) technology is rapidly becoming essential for smart city ecosystems, enabling real-time synchronisation and autonomous decision-making across physical and digital domains. However, as DTs take active roles in control loops, securely binding them to their physical counterparts in dynamic and adversarial environments remains a significant challenge. Existing authentication solutions either rely on static trust models, require centralised authorities, or fail to provide live and verifiable physical-digital binding, making them unsuitable for latency-sensitive and distributed deployments. To address this gap, we introduce PRZK-Bind, a lightweight and decentralised authentication protocol that combines Schnorr-based zero-knowledge proofs with elliptic curve cryptography to establish secure, real-time correspondence between physical entities and DTs without relying on pre-shared secrets. Simulation results show that PRZK-Bind significantly improves performance, offering up to 4.5 times lower latency and 4 times reduced energy consumption compared to cryptography-heavy baselines, while maintaining false acceptance rates more than 10 times lower. These findings highlight its suitability for future smart city deployments requiring efficient, resilient, and trustworthy DT authentication.
In a time of growing environmental issues and climate change, the drive toward sustainability is more important than ever. Startups and small businesses are expected to be more instrumental in forming a sustainable future as world economies move toward greener paradigms. For many of these businesses, though, the financial load related to sustainable infrastructure, eco-innovation, and clean technology still be a major obstacle. For sustainable businesses trying to bring environmentally friendly ideas to market without sacrificing financial viability, green financing options including grants, subsidies, and green loans provide essential lifelines. Emphasizing the need of access to specific funding resources that support environmentally friendly practices, this abstract investigates the several green financing options open to startups. Examining both public and private sector projects emphasizes how green finance closes the innovation gap with implementation, especially for early-stage businesses trying to scale their green solutions. Grants and subsidies represent among the most well-known sources of green money. Usually governments, international organizations, and environmental NGOs supply these financial support to inspire creativity in fields including waste management, green manufacturing, sustainable agriculture, and renewable energy. Grants are a great choice for startups with limited cash flow since they unlike loans do not demand repayment. Many environmental grantinitiatives to support clean tech development have been started in areas including the European Union, North America, and portions of Asia. As part of the EU's larger goal to reach net-zero emissions by 2050, the European Green Deal, for instance, provides billions in support to sustainable businesses. To lower the initial costs of green investments, numerous local and national governments also provide direct subsidies and tax breaks. These could include financing for research and development of low-carbon technologies, subsidies for fleets of electric vehicles, or rebates for solar panel installations. In addition to fostering the growth of green startups, these policies hasten the market uptake of sustainable goods and services. Green loans have become a powerful instrument for sustainable finance in addition to grants. These are loans specifically designated for environmentally beneficial projects, and they frequently have favorable conditions like reduced interest rates, extended payback periods, or repayment plans that are based on performance. To assist with climate-resilient projects, organizations such as the World Bank, the Green Climate Fund, and several green investment banks provide specialized green loan programs. In order to specifically serve small and medium-sized businesses (SMEs) with environmental missions, some commercial banks have also entered this market by introducing green loan portfolios. Accessing green loans or grants for startups in need of these funds necessitates both a strong business plan and an unambiguous proof of environmental impact. The majority of funding organizations assess applications using standards like energy efficiency, circularity, social sustainability, and carbon footprint reduction. Thus, it is essential to have solid environmental metrics and data to support assertions. Furthermore, obtaining certifications such as B-Corp status or compliance with ESG (Environmental, Social, and Governance) standards can boost one's credibility and chances of getting funding. Additionally, startups now have more opportunities to interact with mission-driven investors who value sustainability in addition to financial returns thanks to the growth of impact investing. Green-minded venture capital firms and angel investors frequently offer seed money to eco-innovative companies, seeking high-growth prospects in line with long-term environmental objectives. Additionally, by reaching out to eco-aware communities, crowdfunding websites such as Kickstarter and Indiegogo are being used to fund green startups. Notwithstanding these encouraging advancements, obstacles still exist. Many startups are not equipped with the knowledge, skills, or resources necessary to successfully negotiate the intricate world of green finance. Grant and loan application procedures may be extremely competitive and cumbersome. Additionally, global scalability is hampered by the uneven distribution of green funding across various regions. Governments, financial institutions, and the private sector must work together more closely to close these gaps in addition to implementing policy changes and raising entrepreneur financial literacy. To address these challenges, startup incubators, accelerators, and advisory organizations are increasingly offering green finance consulting services, helping early-stage companies identify suitable funding options, prepare compelling applications, and build investor-ready sustainability strategies. Digital tools and platforms are also emerging to match green startups with appropriate funding sources, thereby streamlining the connection between innovative ideas and capital. In conclusion, green financing is not merely a niche category of economic support; it is an essential enabler of the global transition toward a more sustainable economy. By making green finance more accessible, equitable, and aligned with the realities of early-stage startups, stakeholders can unlock a wave of innovation that tackles some of the world’ s most pressing environmental issues. Whether through grants, subsidies, green loans, or impact investing, the opportunities for sustainable entrepreneurship have never been more abundant, but seizing them requires a well-informed, strategic, and purpose-driven approach.
Aug 24, 2025·Proceedings of the 31st ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2, 2026
Thomas Gassmann, Stefanos Chaliasos, Thodoris Sotiropoulos, Zhendong Su
Zero-knowledge proofs (ZKPs) are the cornerstone of programmable cryptography. They enable (1) privacy-preserving and verifiable computation across blockchains, and (2) an expanding range of off-chain applications such as credential schemes. Zero-knowledge virtual machines (zkVMs) lower the barrier by turning ZKPs into a drop-in backend for standard compilation pipelines. This lets developers write proof-generating programs in conventional languages (e.g., Rust or C++) instead of hand-crafting arithmetic circuits. However, these VMs inherit compiler infrastructures tuned for traditional architectures rather than for proof systems. In particular, standard compiler optimizations assume features that are absent in zkVMs, including cache locality, branch prediction, or instruction-level parallelism. Therefore, their impact on proof generation is questionable. We present the first systematic study of the impact of compiler optimizations on zkVMs. We evaluate 64 LLVM passes, six standard optimization levels, and an unoptimized baseline across 58 benchmarks on two RISC-V-based zkVMs (RISC Zero and SP1). While standard LLVM optimization levels do improve zkVM performance (over 40\%), their impact is far smaller than on traditional CPUs, since their decisions rely on hardware features rather than proof constraints. Guided by a fine-grained pass-level analysis, we~\emph{slightly} refine a small set of LLVM passes to be zkVM-aware, improving zkVM execution time by up to 45\% (average +4.6\% on RISC Zero, +1\% on SP1) and achieving consistent proving-time gains. Our work highlights the potential of compiler-level optimizations for zkVM performance and opens new direction for zkVM-specific passes, backends, and superoptimizers.
Yang Li, Hanjie Wang, Yuanzheng Li, Jiazheng Li · 5 authors
Wind power data often suffers from missing values due to sensor faults and unstable transmission at edge sites. While federated learning enables privacy-preserving collaboration without sharing raw data, it remains vulnerable to anomalous updates and privacy leakage during parameter exchange. These challenges are amplified in open industrial environments, necessitating zero-trust mechanisms where no participant is inherently trusted. To address these challenges, this work proposes ZTFed-MAS2S, a zero-trust federated learning framework that integrates a multi-head attention-based sequence-to-sequence imputation model. ZTFed integrates verifiable differential privacy with non-interactive zero-knowledge proofs and a confidentiality and integrity verification mechanism to ensure verifiable privacy preservation and secure model parameters transmission. A dynamic trust-aware aggregation mechanism is employed, where trust is propagated over similarity graphs to enhance robustness, and communication overhead is reduced via sparsity- and quantization-based compression. MAS2S captures long-term dependencies in wind power data for accurate imputation. Extensive experiments on real-world wind farm datasets validate the superiority of ZTFed-MAS2S in both federated learning performance and missing data imputation, demonstrating its effectiveness as a secure and efficient solution for practical applications in the energy sector.
Zero-Knowledge Proofs (ZKPs) have emerged as a powerful tool for secure and privacy-preserving computation. ZKPs enable one party to convince another of a statement's validity without revealing anything else. This capability has profound implications in many domains, including machine learning, blockchain, image authentication, and electronic voting. Despite their potential, ZKPs have seen limited deployment because of their exceptionally high computational overhead, which manifests primarily during proof generation. To mitigate these overheads, a (growing) body of researchers has proposed hardware accelerators and GPU implementations of both kernels and complete protocols. Prior art spans a wide variety of ZKP schemes that vary significantly in computational overhead, proof size, verifier cost, protocol setup, and trust. The latest and widely used ZKP protocols are intentionally designed to balance these trade-offs. One particular challenge in modern ZKP systems is supporting complex, high-degree gates using the SumCheck protocol. We address this challenge with a novel programmable accelerator to efficiently handle arbitrary custom gates via SumCheck. Our accelerator achieves upwards of $1000\times$ geomean speedup over CPU-based SumChecks across a range of gate types. We include this unit in zkPHIRE, a programmable, full-system accelerator that accelerates the HyperPlonk protocol. zkPHIRE achieves $1486\times$ geomean speedup over CPU and $11.87\times$ geomean speedup over the state-of-the-art at iso-area. Together, these results demonstrate compelling performance while scaling to large problem sizes (upwards of $2^{30}$ constraints) and maintaining small proof sizes ($4-5$ KB).
Sai Teja Reddy Adapala, Yashwanth Reddy Alugubelly
The proliferation of autonomous AI agents marks a paradigm shift toward complex, emergent multi-agent systems. This transition introduces systemic security risks, including control-flow hijacking and cascading failures, that traditional cybersecurity paradigms are ill-equipped to address. This paper introduces the Aegis Protocol, a layered security framework designed to provide strong security guarantees for open agentic ecosystems. The protocol integrates three technological pillars: (1) non-spoofable agent identity via W3C Decentralized Identifiers (DIDs); (2) communication integrity via NIST-standardized post-quantum cryptography (PQC); and (3) verifiable, privacy-preserving policy compliance using the Halo2 zero-knowledge proof (ZKP) system. We formalize an adversary model extending Dolev-Yao for agentic threats and validate the protocol against the STRIDE framework. Our quantitative evaluation used a discrete-event simulation, calibrated against cryptographic benchmarks, to model 1,000 agents. The simulation showed a 0 percent success rate across 20,000 attack trials. For policy verification, analysis of the simulation logs reported a median proof-generation latency of 2.79 seconds, establishing a performance baseline for this class of security. While the evaluation is simulation-based and early-stage, it offers a reproducible baseline for future empirical studies and positions Aegis as a foundation for safe, scalable autonomous AI.
Hesam Azadjou, Suraj Chakravarthi Raja, Ali Marjaninejad, Francisco J. Valero‐Cuevas
Like mammals, robots must rapidly learn to control their bodies and interact with their environment despite incomplete knowledge of their body structure and surroundings. They must also adapt to continuous changes in both. This work presents a bio-inspired learning algorithm, General-to-Particular (G2P), applied to a tendon-driven quadruped robotic system developed and fabricated in-house. Our quadruped robot undergoes an initial five-minute phase of generalized motor babbling, followed by 15 refinement trials (each lasting 20 seconds) to achieve specific cyclical movements. This process mirrors the exploration-exploitation paradigm observed in mammals. With each refinement, the robot progressively improves upon its initial "good enough" solution. Our results serve as a proof-of-concept, demonstrating the hardware-in-the-loop system's ability to learn the control of a tendon-driven quadruped with redundancies in just a few minutes to achieve functional and adaptive cyclical non-convex movements. By advancing autonomous control in robotic locomotion, our approach paves the way for robots capable of dynamically adjusting to new environments, ensuring sustained adaptability and performance.