Academic literature on the topic 'Secure Outsourced Computation'

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Secure Outsourced Computation.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Secure Outsourced Computation"

1

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.

Full text
Abstract:
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 computat
APA, Harvard, Vancouver, ISO, and other styles
2

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

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.

Full text
Abstract:
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 pr
APA, Harvard, Vancouver, ISO, and other styles
4

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

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.

Full text
Abstract:
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
APA, Harvard, Vancouver, ISO, and other styles
8

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.

Full text
Abstract:
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
APA, Harvard, Vancouver, ISO, and other styles
9

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.

Full text
Abstract:
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 c
APA, Harvard, Vancouver, ISO, and other styles
10

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.

Full text
Abstract:
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 resu
APA, Harvard, Vancouver, ISO, and other styles
More sources
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!