Bibliographies thématiques / Secure Outsourced Computation

Sommaire

Articles de revues

Littérature scientifique sur le sujet « Secure Outsourced Computation »

Auteur : Grafiati

Publié le 6 septembre 2023

Créez une référence correcte selon les styles APA, MLA, Chicago, Harvard et plusieurs autres

Choisissez une source :

Consultez les listes thématiques d’articles de revues, de livres, de thèses, de rapports de conférences et d’autres sources académiques sur le sujet « Secure Outsourced Computation ».

À côté de chaque source dans la liste de références il y a un bouton « Ajouter à la bibliographie ». Cliquez sur ce bouton, et nous générerons automatiquement la référence bibliographique pour la source choisie selon votre style de citation préféré : APA, MLA, Harvard, Vancouver, Chicago, etc.

Vous pouvez aussi télécharger le texte intégral de la publication scolaire au format pdf et consulter son résumé en ligne lorsque ces informations sont inclues dans les métadonnées.

Articles de revues sur le sujet "Secure Outsourced Computation"

Olakanmi, Oladayo Olufemi, and Adedamola Dada. "An Efficient Privacy-preserving Approach for Secure Verifiable Outsourced Computing on Untrusted Platforms." International Journal of Cloud Applications and Computing 9, no. 2 (2019): 79–98. http://dx.doi.org/10.4018/ijcac.2019040105.

Texte intégral

Résumé :

In outsourcing computation models, weak devices (clients) increasingly rely on remote servers (workers) for data storage and computations. However, most of these servers are hackable or untrustworthy, which makes their computation questionable. Therefore, there is need for clients to validate the correctness of the results of their outsourced computations and ensure that servers learn nothing about their clients other than the outputs of their computation. In this work, an efficient privacy preservation validation approach is developed which allows clients to store and outsource their computations to servers in a semi-honest model such that servers' computational results could be validated by clients without re-computing the computation. This article employs a morphism approach for the client to efficiently perform the proof of correctness of its outsourced computation without re-computing the whole computation. A traceable pseudonym is employed by clients to enforce anonymity.

Styles APA, Harvard, Vancouver, ISO, etc.

Blanton, Marina, and Mehrdad Aliasgari. "Secure outsourced computation of iris matching." Journal of Computer Security 20, no. 2-3 (2012): 259–305. http://dx.doi.org/10.3233/jcs-2012-0447.

Texte intégral

Styles APA, Harvard, Vancouver, ISO, etc.

Sun, Yi, Qiaoyan Wen, Yudong Zhang, Hua Zhang, Zhengping Jin, and Wenmin Li. "Two-Cloud-Servers-Assisted Secure Outsourcing Multiparty Computation." Scientific World Journal 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/413265.

Texte intégral

Résumé :

We focus on how to securely outsource computation task to the cloud and propose a secure outsourcing multiparty computation protocol on lattice-based encrypted data in two-cloud-servers scenario. Our main idea is to transform the outsourced data respectively encrypted by different users’ public keys to the ones that are encrypted by the same two private keys of the two assisted servers so that it is feasible to operate on the transformed ciphertexts to compute an encrypted result following the function to be computed. In order to keep the privacy of the result, the two servers cooperatively produce a custom-made result for each user that is authorized to get the result so that all authorized users can recover the desired result while other unauthorized ones including the two servers cannot. Compared with previous research, our protocol is completely noninteractive between any users, and both of the computation and the communication complexities of each user in our solution are independent of the computing function.

Styles APA, Harvard, Vancouver, ISO, etc.

Shao, Jun, and Guiyi Wei. "Secure Outsourced Computation in Connected Vehicular Cloud Computing." IEEE Network 32, no. 3 (2018): 36–41. http://dx.doi.org/10.1109/mnet.2018.1700345.

Texte intégral

Styles APA, Harvard, Vancouver, ISO, etc.

Treiber, Amos, Andreas Nautsch, Jascha Kolberg, Thomas Schneider, and Christoph Busch. "Privacy-preserving PLDA speaker verification using outsourced secure computation." Speech Communication 114 (November 2019): 60–71. http://dx.doi.org/10.1016/j.specom.2019.09.004.

Texte intégral

Styles APA, Harvard, Vancouver, ISO, etc.

Yang, Yang, Xindi Huang, Ximeng Liu, et al. "A Comprehensive Survey on Secure Outsourced Computation and Its Applications." IEEE Access 7 (2019): 159426–65. http://dx.doi.org/10.1109/access.2019.2949782.

Texte intégral

Styles APA, Harvard, Vancouver, ISO, etc.

Hong, Jun, Tao Wen, Quan Guo, and Zhengwang Ye. "Secure kNN Computation and Integrity Assurance of Data Outsourcing in the Cloud." Mathematical Problems in Engineering 2017 (2017): 1–15. http://dx.doi.org/10.1155/2017/8109730.

Texte intégral

Résumé :

As cloud computing has been popularized massively and rapidly, individuals and enterprises prefer outsourcing their databases to the cloud service provider (CSP) to save the expenditure for managing and maintaining the data. The outsourced databases are hosted, and query services are offered to clients by the CSP, whereas the CSP is not fully trusted. Consequently, the security shall be violated by multiple factors. Data privacy and query integrity are perceived as two major factors obstructing enterprises from outsourcing their databases. A novel scheme is proposed in this paper to effectuate k-nearest neighbors (kNN) query and kNN query authentication on an encrypted outsourced spatial database. An asymmetric scalar-product-preserving encryption scheme is elucidated, in which data points and query points are encrypted with diverse encryption keys, and the CSP can determine the distance relation between encrypted data points and query points. Furthermore, the similarity search tree is extended to build a novel verifiable SS-tree that supports efficient kNN query and kNN query verification. It is indicated from the security analysis and experiment results that our scheme not only maintains the confidentiality of outsourced confidential data and query points but also has a lower kNN query processing and verification overhead than the MR-tree.

Styles APA, Harvard, Vancouver, ISO, etc.

Yang, Guangcan, Jiayang Li, Yunhua He, et al. "A Security-Enhanced Query Result Verification Scheme for Outsourced Data in Location-Based Services." Applied Sciences 12, no. 16 (2022): 8126. http://dx.doi.org/10.3390/app12168126.

Texte intégral

Résumé :

Location-based services (LBSs) facilitate people’s lives; location-based service providers (LBSPs) usually outsource services to third parties to provide better services. However, the third party is a dishonest entity that might return incorrect or incomplete query results under the consideration of saving storage space and computation resources. In this paper, we propose a security-enhanced query result verification scheme (SEQRVS) for the outsourced data in a LBS. Specifically, while retaining fine-grained query result verification, we improve the construction process of verification objects to enhance the security of the outsourced data. To prevent the third party from deducing the knowledge of the outsourced data stored in itself (statistically), our scheme designs a novel storage structure to enhance the ability of privacy preservation for the outsourced data. Furthermore, based on the secure keyword search and query result verification mode proposed in our scheme, the user cannot only verify the correctness and completeness of the query result but also achieve consistency verification by the blockchain. Finally, the security analysis and extensive simulation results show the security and practicality of the proposed scheme.

Styles APA, Harvard, Vancouver, ISO, etc.

Zhu, Youwen, Xingxin Li, Jian Wang, Yining Liu, and Zhiguo Qu. "Practical Secure Naïve Bayesian Classification Over Encrypted Big Data in Cloud." International Journal of Foundations of Computer Science 28, no. 06 (2017): 683–703. http://dx.doi.org/10.1142/s0129054117400135.

Texte intégral

Résumé :

Cloud can provide much convenience for big data storage and analysis. To enjoy the advantage of cloud service with privacy preservation, huge data is increasingly outsourced to cloud in encrypted form. Unfortunately, encryption may impede the analysis and computation over the outsourced dataset. Naïve Bayesian classification is an effective algorithm to predict the class label of unlabeled samples. In this paper, we investigate naïve Bayesian classification on encrypted large-scale dataset in cloud, and propose a practical and secure scheme for the challenging problem. In our scheme, all the computation task of naïve Bayesian classification are completed by the cloud, which can dramatically reduce the burden of data owner and users. We give a formal security proof for our scheme. Based on the theoretical proof, we can strictly guarantee the privacy of both input dataset and output classification results, i.e., the cloud can learn nothing useful about the training data of data owner and the test samples of users throughout the computation. Additionally, we not only theoretically analyze our computation complexity and communication overheads, but also evaluate our implementation cost by leveraging extensive experiments over real dataset, which shows our scheme can achieve practical efficiency.

Styles APA, Harvard, Vancouver, ISO, etc.

Song, Mingyang, and Yingpeng Sang. "Secure Outsourcing of Matrix Determinant Computation under the Malicious Cloud." Sensors 21, no. 20 (2021): 6821. http://dx.doi.org/10.3390/s21206821.

Texte intégral

Résumé :

Computing the determinant of large matrix is a time-consuming task, which is appearing more and more widely in science and engineering problems in the era of big data. Fortunately, cloud computing can provide large storage and computation resources, and thus, act as an ideal platform to complete computation outsourced from resource-constrained devices. However, cloud computing also causes security issues. For example, the curious cloud may spy on user privacy through outsourced data. The malicious cloud violating computing scripts, as well as cloud hardware failure, will lead to incorrect results. Therefore, we propose a secure outsourcing algorithm to compute the determinant of large matrix under the malicious cloud mode in this paper. The algorithm protects the privacy of the original matrix by applying row/column permutation and other transformations to the matrix. To resist malicious cheating on the computation tasks, a new verification method is utilized in our algorithm. Unlike previous algorithms that require multiple rounds of verification, our verification requires only one round without trading off the cheating detectability, which greatly reduces the local computation burden. Both theoretical and experimental analysis demonstrate that our algorithm achieves a better efficiency on local users than previous ones on various dimensions of matrices, without sacrificing the security requirements in terms of privacy protection and cheating detectability.

Styles APA, Harvard, Vancouver, ISO, etc.

Plus de sources

Nous offrons des réductions sur tous les plans premium pour les auteurs dont les œuvres sont incluses dans des sélections littéraires thématiques. Contactez-nous pour obtenir un code promo unique!