Relevant bibliographies by topics / Verifiable computing

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Verifiable computing'

Author: Grafiati
Published: 25 May 2024
Last updated: 19 July 2025

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Journal articles on the topic "Verifiable computing"

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Simunic, Silvio, Dalen Bernaca, and Kristijan Lenac. "Verifiable Computing Applications in Blockchain." IEEE Access 9 (2021): 156729–45. http://dx.doi.org/10.1109/access.2021.3129314.

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Yan, Zheng, Xixun Yu, and Wenxiu Ding. "Context-Aware Verifiable Cloud Computing." IEEE Access 5 (2017): 2211–27. http://dx.doi.org/10.1109/access.2017.2666839.

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Jeveriya, Anjum Dr Shameem Akhter. "CONTEXT AWARE VERIFIABLE CLOUD COMPUTING." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 7, no. 6 (2018): 33–56. https://doi.org/10.5281/zenodo.1336668.

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Cloud computing act as a significantpart for big data dispensation by providing statisticscalculating and treating facilities. Nevertheless, cloud facility breadwinners may spasm data confidentiality also offerimprecise data dispensation outcomes to operators, and hence cannot be completelyreliable. Contrariwise, inadequate by reckoning possessions and abilities, cloud users customarily cannot self-sufficientlyprocedure big data and accomplish authentication on the accuracy of data dispensation. This nurtures a distincttask on cloud computing authentication, exclusively when operator facts are kept at the cloud in an encodedmethod and administered for sustaining the requirementselevated in diverseframeworks. But the present prose still privations severe educations on this explorationtopic. In the present work, we offer a context-aware verifiable computing systembuilt on complete homomorphic encryption by arranging an assessingcode of behavior to authenticate the precision of the encoded data processing outcome. We projected four electiveassessing etiquettes to fulfilldiverse sanctuary necessities. The outcomes demonstrate the usefulness and productivity of our designs
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Song, Beibei, Dehua Zhou, Jiahe Wu, Xiaowei Yuan, Yiming Zhu, and Chuansheng Wang. "Protecting Function Privacy and Input Privacy in the Publicly Verifiable Outsourcing Computation of Polynomial Functions." Future Internet 15, no. 4 (2023): 152. http://dx.doi.org/10.3390/fi15040152.

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With the prevalence of cloud computing, the outsourcing of computation has gained significant attention. Clients with limited computing power often outsource complex computing tasks to the cloud to save on computing resources and costs. In outsourcing the computation of functions, a function owner delegates a cloud server to perform the function’s computation on the input received from the user. There are three primary security concerns associated with this process: protecting function privacy for the function owner, protecting input privacy for the user and guaranteeing that the cloud server performs the computation correctly. Existing works have only addressed privately verifiable outsourcing computation with privacy or publicly verifiable outsourcing computation without input privacy or function privacy. By using the technologies of homomorphic encryption, proxy re-encryption and verifiable computation, we propose the first publicly verifiable outsourcing computation scheme that achieves both input privacy and function privacy for matrix functions, which can be extended to arbitrary multivariate polynomial functions. We additionally provide a faster privately verifiable method. Moreover, the function owner retains control over the function.
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Sun, Jiameng, Binrui Zhu, Jing Qin, Jiankun Hu, and Qianhong Wu. "Confidentiality-Preserving Publicly Verifiable Computation." International Journal of Foundations of Computer Science 28, no. 06 (2017): 799–818. http://dx.doi.org/10.1142/s0129054117400196.

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Cloud computing enables users to outsource complicated computational tasks to a commercial computing server and relieves the users from establishing and maintaining expensive local computation systems. In this scenario, the minimum security requirement is that the result returned by the server must be correct. Publicly verifiable computation (PVC) has been proposed to address this issue by allowing the computational result to be publicly verifiable. Observing that computational tasks are usually private business in practice, we propose a confidentiality-preserving security tool referred to as confidentiality-preserving publicly verifiable computation (CP-PVC), to efficiently address the scenario where a client would like to outsource a computational task to a cloud server but does not possess the input value locally. The CP-PVC allows the client to delegate the outsourcing computational task to anyone authorized and keeps the computational result confidential to anyone except the client, while not sacrificing the property of public verifiability. We propose a CP-PVC construction based on any one-key secure attribute-based encryption (ABE). Our construction is general as known ABE schemes are all one-key secure. Analysis shows that our CP-PVC scheme achieves computational result privacy without any significant extra cost and is almost as efficient as the up-to-date PVC schemes. These features render our CP-PVC as a practical and widely applicable tool to secure cloud computing.
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Yao, Shuang, and Dawei Zhang. "An Anonymous Verifiable Random Function with Applications in Blockchain." Wireless Communications and Mobile Computing 2022 (April 19, 2022): 1–12. http://dx.doi.org/10.1155/2022/6467866.

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Verifiable random function is a powerful function that provides a noninteractively public verifiable proof for its output. Recently, verifiable random function has found essential applications in designing secure consensus protocols in blockchain. How to construct secure and practical verifiable random functions has also attracted more and more attention. In this paper, we propose a practical anonymous verifiable random function. Security proofs show that the proposed anonymous verifiable random function achieves correctness, anonymity, uniqueness, and pseudorandomness. In addition, we show a concrete application of our proposed anonymous verifiable random function in blockchain to improve the consensus mechanism for Hyperledger fabric. Finally, we implement the proposed anonymous verifiable random function and evaluate its performance. Test results show that the proposed anonymous verifiable random function supports faster computing operations and has a smaller proof size.
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Zara, Maham, Shuzhen Wang, and Hasan Ul Moin. "Blockchain-Based Verifiable Computation: A Review." Proceedings of the Pakistan Academy of Sciences: A. Physical and Computational Sciences 61, no. 2 (2024): 113–28. http://dx.doi.org/10.53560/ppasa(61-2)850.

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Verifiable computation has been studied as a way to verify the outcomes of an outsourced computation. It is usually seen from the view of a user who wishes to outsource computation to a centralized third party but wants to ensure that the party provides correct results. With the said scheme, the verifier requests the prover to perform the computational task and then verifies the outcome by checking the output and the proof obtained from the prover. However, there are several security challenges within a centralized third party to execute verification tasks. Recently, the advancement in blockchain technology has offered an opportunity to solve these security challenges. Blockchain is a distributed ledger and decentralized technology that eliminates the need for third-party verification. In recent years, the emergence of innovative applications of verifiable computing techniques within blockchain technology has been witnessed. These applications focus on ensuring secure key management, enhancing smart contracts, and fortifying sybil-resistance. The use of blockchain in the realm of verifiable computing has drawn the attention of many researchers. However, our research into relevant papers revealed a notable lack of comprehensive surveys on blockchain-based verifiable computing in the literature. To overcome this gap, we conducted a comprehensive survey on blockchain-based verifiable computation. First, we address fundamental concepts related to blockchain-based verifiable computation. Afterwards, we offer a series of criteria to evaluate existing blockchain-based verifiable computation techniques. Finally, based on our comprehensive review and evaluation metrics, we explore various open challenges and potential research prospects. These include zero-knowledge proofs (ZKP) integration, addressing privacy preservation, scalability, and traceability. Future research should focus on robust privacy-preserving methods, using ZKP for enhanced security, off-chain computations for scalability, and decentralized file systems like Interplanetary File System (IPFS) to improve traceability.
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Jiao, Zi, Fucai Zhou, Qiang Wang, and Jintong Sun. "RPVC: A Revocable Publicly Verifiable Computation Solution for Edge Computing." Sensors 22, no. 11 (2022): 4012. http://dx.doi.org/10.3390/s22114012.

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With publicly verifiable computation (PVC) development, users with limited resources prefer to outsource computing tasks to cloud servers. However, existing PVC schemes are mainly proposed for cloud computing scenarios, which brings bandwidth consumption or network delay of IoT devices in edge computing. In addition, dishonest edge servers may reduce resource utilization by returning unreliable results. Therefore, we propose a revocable publicly verifiable computation(RPVC) scheme for edge computing. On the one hand, RPVC ensures that users can verify the correct results at a small cost. On the other hand, it can revoke the computing abilities of dishonest edge servers. First, polynomial commitments are employed to reduce proofs’ length and generation speed. Then, we improve revocable group signature by knowledge signatures and subset covering theory. This makes it possible to revoke dishonest edge servers. Finally, theoretical analysis proves that RPVC has correctness and security, and experiments evaluate the efficiency of RPVC.
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Wang, Jianfeng, Xiaofeng Chen, Xinyi Huang, Ilsun You, and Yang Xiang. "Verifiable Auditing for Outsourced Database in Cloud Computing." IEEE Transactions on Computers 64, no. 11 (2015): 3293–303. http://dx.doi.org/10.1109/tc.2015.2401036.

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Xu, Lingling, and Shaohua Tang. "Verifiable computation with access control in cloud computing." Journal of Supercomputing 69, no. 2 (2013): 528–46. http://dx.doi.org/10.1007/s11227-013-1039-z.

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Dissertations / Theses on the topic "Verifiable computing"

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Madi, Abbass. "Secure Machine Learning by means of Homomorphic Encryption and Verifiable Computing." Electronic Thesis or Diss., université Paris-Saclay, 2022. http://www.theses.fr/2022UPASG019.

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L’apprentissage automatique (ou le Machine Learning) est un domaine scientifique très en vogue en raison de sa capacité à résoudre les problèmes automatiquement et de son large spectre d’applications (par exemple, le domaine de la finance, le domaine médical, etc.). Les techniques de Machine Learning (ML) ont attiré mon attention du point de vue cryptographique dans le sens où les travaux de ma thèse ont eu comme objectif une utilisation sécurisée des méthodes de ML. Cette thèse traite l'utilisation sécurisée des techniques de ML sous deux volets : la confidentialité des données d’apprentissage ou des données pour l’inférence et l’intégrité de l’évaluation à distance des différentes méthodes de ML. La plupart des autres travaux traitent que la confidentialité des données et que pour la phase d’inférence. Dans ma thèse, j’ai proposé trois architectures pour assurer une évaluation à distance sécurisée pour les configurations suivantes de ML: la classification à distance grâce à un réseau de neurones (NN), l’apprentissage fédéré (FL) et l’apprentissage par transfert (TL). Notamment, les architectures pour l’apprentissage fédéré et l’apprentissage par transfert sont les premiers approches qui traitent à la fois la confidentialité de données et l'intégrité du calcul. Ces architectures ont été construites en utilisant ou en modifiant un schéma de calcul vérifiable pré-existant pour des données chiffrées en homomorphe. Nos travaux ouvrent des nombreuses perspectives, qui ne concernent pas forcément que les architectures de ML, mais aussi les outils utilisés pour assurer les propriétés de sécurité. Par exemple, une perspective importante est de rajouter de la confidentialité différentielle (DP) à notre architecture d’apprentissage fédéré<br>Machine Learning (ML) represents a new trend in science because of its power to solve problems automatically and its wide spectrum of applications (e.g., business, healthcare domain, etc.). This attractive technology caught our attention from a cryptography point of view in the sense that we worked during this Ph.D. to ensure secure usage of ML setups. Our Ph.D. work proposes a secure remote evaluation over different ML setups (for inference and for training). This thesis addresses two cornerstones: confidentiality of training or inference data and remote evaluation integrity in different ML setups (federated learning or transfer learning-based inference). In contrast, most other works focus only on data confidentiality. In our thesis, we proposed three architectures/frameworks to ensure a secure remote evaluation for the following ML setups: Neural Networks (NN), Federated Learning (FL), and Transfer Learning (TL). Particularly, our FL and TL architectures are the first that treat both the confidentiality and integrity security properties. We built these architectures using or modifying pre-existing VC schemes over homomorphic encrypted data: mainly we use VC protocols for BFV and Paillier schemes. An essential characteristic for our architectures is their generality, in the sense, if there are improvements in VC protocols and HE schemes, our frameworks can easily take into account these new approaches. This work opens up many perspectives, not only in privacy-preserving ML architectures, but also for the tools used to ensure the security properties. For example, one important perspective is to add differential privacy (DP) to our FL architecture
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Sun, Wenhai. "Towards Secure Outsourced Data Services in the Public Cloud." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/84396.

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Past few years have witnessed a dramatic shift for IT infrastructures from a self-sustained model to a centralized and multi-tenant elastic computing paradigm -- Cloud Computing, which significantly reshapes the landscape of existing data utilization services. In truth, public cloud service providers (CSPs), e.g. Google, Amazon, offer us unprecedented benefits, such as ubiquitous and flexible access, considerable capital expenditure savings and on-demand resource allocation. Cloud has become the virtual ``brain" as well to support and propel many important applications and system designs, for example, artificial intelligence, Internet of Things, and so forth; on the flip side, security and privacy are among the primary concerns with the adoption of cloud-based data services in that the user loses control of her/his outsourced data. Encrypting the sensitive user information certainly ensures the confidentiality. However, encryption places an extra layer of ambiguity and its direct use may be at odds with the practical requirements and defeat the purpose of cloud computing technology. We believe that security in nature should not be in contravention of the cloud outsourcing model. Rather, it is expected to complement the current achievements to further fuel the wide adoption of the public cloud service. This, in turn, requires us not to decouple them from the very beginning of the system design. Drawing the successes and failures from both academia and industry, we attempt to answer the challenges of realizing efficient and useful secure data services in the public cloud. In particular, we pay attention to security and privacy in two essential functions of the cloud ``brain", i.e. data storage and processing. Our first work centers on the secure chunk-based deduplication of encrypted data for cloud backup and achieves the performance comparable to the plaintext cloud storage deduplication while effectively mitigating the information leakage from the low-entropy chunks. On the other hand, we comprehensively study the promising yet challenging issue of search over encrypted data in the cloud environment, which allows a user to delegate her/his search task to a CSP server that hosts a collection of encrypted files while still guaranteeing some measure of query privacy. In order to accomplish this grand vision, we explore both software-based secure computation research that often relies on cryptography and concentrates on algorithmic design and theoretical proof, and trusted execution solutions that depend on hardware-based isolation and trusted computing. Hopefully, through the lens of our efforts, insights could be furnished into future research in the related areas.<br>Ph. D.
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Azraoui, Monir. "Vérifiabilité et imputabilité dans le Cloud." Thesis, Paris, ENST, 2016. http://www.theses.fr/2016ENST0032/document.

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Cette thèse propose de nouveaux protocoles cryptographiques, plus efficaces que l’existant, et qui permettent aux utilisateurs du nuage informatique (le cloud) de vérifier (i) la bonne conservation des données externalisées et (ii) l'exécution correcte de calculs externalisés. Nous décrivons d'abord un protocole cryptographique qui génère des preuves de récupérabilité, qui permettent aux propriétaires de données de vérifier que le cloud stocke leurs données correctement. Nous détaillons ensuite trois schémas cryptographiques pour vérifier l’exactitude des calculs externalisés en se focalisant sur trois opérations fréquentes dans les procédures de traitement de données, à savoir l’évaluation de polynômes, la multiplication de matrices et la recherche de conjonction de mots-clés. La sécurité de nos solutions est analysée dans le cadre de la sécurité prouvable et nous démontrons également leur efficacité grâce à des prototypes. Nous présentons également A-PPL, un langage de politiques pour l’imputabilité qui permet l'expression des obligations de responsabilité et de traçabilité dans un format compréhensible par la machine. Nous espérons que nos contributions pourront encourager l'adoption du cloud par les entreprises encore réticentes à l’idée d'utiliser ce paradigme prometteur<br>This thesis proposes more efficient cryptographic protocols that enable cloud users to verify (i) the correct storage of outsourced data and (ii) the correct execution of outsourced computation. We first describe a cryptographic protocol that generates proofs of retrievability, which enable data owners to verify that the cloud correctly stores their data. We then detail three cryptographic schemes for verifiable computation by focusing on three operations frequent in data processing routines, namely polynomial evaluation, matrix multiplication and conjunctive keyword search. The security of our solutions is analyzed in the provable security framework and we also demonstrate their efficiency thanks to prototypes. We also introduce A-PPL, an accountability policy language that allows the expression of accountability obligations into machine-readable format. We expect our contributions to foster cloud adoption by organizations still wary of using this promising paradigm
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Azraoui, Monir. "Vérifiabilité et imputabilité dans le Cloud." Electronic Thesis or Diss., Paris, ENST, 2016. http://www.theses.fr/2016ENST0032.

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Cette thèse propose de nouveaux protocoles cryptographiques, plus efficaces que l’existant, et qui permettent aux utilisateurs du nuage informatique (le cloud) de vérifier (i) la bonne conservation des données externalisées et (ii) l'exécution correcte de calculs externalisés. Nous décrivons d'abord un protocole cryptographique qui génère des preuves de récupérabilité, qui permettent aux propriétaires de données de vérifier que le cloud stocke leurs données correctement. Nous détaillons ensuite trois schémas cryptographiques pour vérifier l’exactitude des calculs externalisés en se focalisant sur trois opérations fréquentes dans les procédures de traitement de données, à savoir l’évaluation de polynômes, la multiplication de matrices et la recherche de conjonction de mots-clés. La sécurité de nos solutions est analysée dans le cadre de la sécurité prouvable et nous démontrons également leur efficacité grâce à des prototypes. Nous présentons également A-PPL, un langage de politiques pour l’imputabilité qui permet l'expression des obligations de responsabilité et de traçabilité dans un format compréhensible par la machine. Nous espérons que nos contributions pourront encourager l'adoption du cloud par les entreprises encore réticentes à l’idée d'utiliser ce paradigme prometteur<br>This thesis proposes more efficient cryptographic protocols that enable cloud users to verify (i) the correct storage of outsourced data and (ii) the correct execution of outsourced computation. We first describe a cryptographic protocol that generates proofs of retrievability, which enable data owners to verify that the cloud correctly stores their data. We then detail three cryptographic schemes for verifiable computation by focusing on three operations frequent in data processing routines, namely polynomial evaluation, matrix multiplication and conjunctive keyword search. The security of our solutions is analyzed in the provable security framework and we also demonstrate their efficiency thanks to prototypes. We also introduce A-PPL, an accountability policy language that allows the expression of accountability obligations into machine-readable format. We expect our contributions to foster cloud adoption by organizations still wary of using this promising paradigm
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Rathi, Nilesh. "Scaling Blockchains Using Coding Theory and Verifiable Computing." Thesis, 2021. https://etd.iisc.ac.in/handle/2005/5203.

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The issue of scalability has been restricting blockchain from its widespread adoption. The current transaction rate of bitcoin is around seven transactions/second while its size has crossed the 300 GB mark. Although many approaches propose different ways to scale blockchain, e.g., sharding, lightning network, etc., we focus our analysis on methods utilizing ideas from coding theory. We first consider SeF, a blockchain archiving architecture utilizing LT codes to reduce storage constraints per node up to 1000x. SeF enables full nodes to store only a small number of encoded blocks or droplets instead of an entire blockchain. Although efficient in the average case, the architecture sometimes requires large bandwidth (many droplets) to reconstruct blockchain. While other rate-less coding strategies utilizing two encoding levels are proven better than LT codes, we investigate their suitability in the proposed architecture. We propose and simulate three techniques about how to incorporate these coding strategies. The results show that precode-based rate-less coding schemes provide similar storage savings with reduced bandwidth variance for recovery. The other work we examine is PolyShard, which introduces the notion of coded-sharding. Coded sharding exports block verification of sub-ledger to the whole network instead of nodes handling that sub-ledger, making sharding resilient even to an adaptive adversary, i.e., adversary having the power to corrupt nodes after their assignment to shards. However innovative, PolyShard requires decoding of Reed-Solomon codes over large fields for block verification in real-world settings, making it computationally intensive and less practical. We propose replacing the decoding phase with verifiable computing, which reduces the bottleneck and makes the system practical for light verification functions.
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Books on the topic "Verifiable computing"

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Demirel, Denise, Lucas Schabhüser, and Johannes Buchmann. Privately and Publicly Verifiable Computing Techniques. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53798-6.

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Schabhüser, Lucas, Johannes Buchmann, and Denise Demirel. Privately and Publicly Verifiable Computing Techniques: A Survey. Springer International Publishing AG, 2017.

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Book chapters on the topic "Verifiable computing"

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Xu, Cheng, Ce Zhang, and Jianliang Xu. "Verifiable Cloud Computing." In Encyclopedia of Wireless Networks. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-319-78262-1_299.

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Xu, Cheng, Ce Zhang, and Jianliang Xu. "Verifiable Cloud Computing." In Encyclopedia of Wireless Networks. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-32903-1_299-1.

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Demirel, Denise, Lucas Schabhüser, and Johannes Buchmann. "Verifiable Computing for Specific Applications." In Privately and Publicly Verifiable Computing Techniques. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53798-6_7.

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Demirel, Denise, Lucas Schabhüser, and Johannes Buchmann. "Proof and Argument Based Verifiable Computing." In Privately and Publicly Verifiable Computing Techniques. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53798-6_3.

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Demirel, Denise, Lucas Schabhüser, and Johannes Buchmann. "Verifiable Computing from Fully Homomorphic Encryption." In Privately and Publicly Verifiable Computing Techniques. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53798-6_4.

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Demirel, Denise, Lucas Schabhüser, and Johannes Buchmann. "Verifiable Computing Frameworks from Functional Encryption and Functional Signatures." In Privately and Publicly Verifiable Computing Techniques. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53798-6_6.

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Madi, Abbass, Renaud Sirdey, and Oana Stan. "Computing Neural Networks with Homomorphic Encryption and Verifiable Computing." In Lecture Notes in Computer Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61638-0_17.

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Demirel, Denise, Lucas Schabhüser, and Johannes Buchmann. "Introduction." In Privately and Publicly Verifiable Computing Techniques. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53798-6_1.

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Demirel, Denise, Lucas Schabhüser, and Johannes Buchmann. "Preliminaries." In Privately and Publicly Verifiable Computing Techniques. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53798-6_2.

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Demirel, Denise, Lucas Schabhüser, and Johannes Buchmann. "Homomorphic Authenticators." In Privately and Publicly Verifiable Computing Techniques. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53798-6_5.

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Conference papers on the topic "Verifiable computing"

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Pei, Xintao, Yuling Chen, Yun Luo, Zaidong Li, and Jianqi Wei. "Lightweight IoT-Oriented Verifiable Computing Scheme in Cloud Computing Circumstance." In 2024 IEEE Cyber Science and Technology Congress (CyberSciTech). IEEE, 2024. https://doi.org/10.1109/cyberscitech64112.2024.00017.

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Mazzocca, Carlo, Stefano Allevi, and Rebecca Montanari. "Certifying IoT Data with Verifiable Credentials." In 2024 22nd International Symposium on Network Computing and Applications (NCA). IEEE, 2024. https://doi.org/10.1109/nca61908.2024.00022.

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Xu, Ye, and Takashi Nishide. "Verifiable Homomorphic Secret Sharing for SIMD Operations." In 2024 Twelfth International Symposium on Computing and Networking Workshops (CANDARW). IEEE, 2024. https://doi.org/10.1109/candarw64572.2024.00058.

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Xiong, Keqi, Zehua Liu, Jiayong Wei, and Huimin Gong. "A Blockchain-Based Verifiable Data Quality Assessment Scheme." In 2025 International Wireless Communications and Mobile Computing (IWCMC). IEEE, 2025. https://doi.org/10.1109/iwcmc65282.2025.11059446.

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Tabaeiaghdaei, Seyedali, Filippo Costa, Jonghoon Kwon, Patrick Bamert, Yih-Chun Hu, and Adrian Perrig. "Debuglet: Programmable and Verifiable Inter-Domain Network Telemetry." In 2024 IEEE 44th International Conference on Distributed Computing Systems (ICDCS). IEEE, 2024. http://dx.doi.org/10.1109/icdcs60910.2024.00032.

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Castellano, Dario, Roberto De Prisco, and Pompeo Faruolo. "Login System for OpenID Connect with Verifiable Credentials." In 2024 22nd International Symposium on Network Computing and Applications (NCA). IEEE, 2024. https://doi.org/10.1109/nca61908.2024.00027.

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Wang, Yalan, Liqun Chen, Long Meng, and Christopher J. P. Newton. "VCaDID: Verifiable Credentials with Anonymous Decentralized Identities." In 2024 IEEE 23rd International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). IEEE, 2024. https://doi.org/10.1109/trustcom63139.2024.00086.

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Zhang, Chi, Peng Jiang, Zijian Zhang, and Liehuang Zhu. "Verifiable Predicate-based Access Control Encryption with Dynamic Revocation." In 2025 International Wireless Communications and Mobile Computing (IWCMC). IEEE, 2025. https://doi.org/10.1109/iwcmc65282.2025.11059613.

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Liu, Shaojie, Hongbo Zhao, and Han Liu. "Demo: Specy Network - Trusted Multichain Automation with Verifiable Specifications." In 2024 IEEE 44th International Conference on Distributed Computing Systems (ICDCS). IEEE, 2024. http://dx.doi.org/10.1109/icdcs60910.2024.00134.

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Shaik, Matheen Basha, and Roopa Vishwanathan. "Verifiable Computation in Smart Grids Using Dynamic Slicing." In 2025 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops). IEEE, 2025. https://doi.org/10.1109/percomworkshops65533.2025.00101.

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