Secure Outsourced Calculations with Homomorphic Encryption

Wang, Qi; Zhou, Dehua; Li, Yanling

doi:10.5121/acij.2018.9601

Cited by 5 publications

(3 citation statements)

References 20 publications

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“…The rapid development of Big Data [10] more and more users decide to outsource their data to the Big Data for warehousing and further outgrowth. [5] The homomorphic encryption phase is semantically secure, like RSA, also have attacks on one way.…”

Section: Related Workmentioning

confidence: 99%

Enhanced Homomorphic Re-Encryption using Laplacian for Preserving the Privacy in Big Data Analytics

2020

IJRTE

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 Abstract: Big data offers various services like storing sensitive, private data and maintaining the data. Big data users may upload encrypted data rather than raw data for preserving data. Processing and analyzing the encrypted data is the primary target for attackers and hackers. Homomorphic Re-Encryption to supports access control, processed cipher-text on encrypted data and ensure data confidentiality. However, the limitation of Homomorphic Re-Encryption is the single-user system, which means it allows the party that owns a homomorphic decryption key to decrypt processed cipher-texts. Original Homomorphic Re-Encryption cannot support multiple users to access the processed cipher texts flexibly. In this paper, propose a Privacy-Preserving Big Data Processing system which support of a Homomorphic Re-Encryption using laplacian phase that extends partially from a single-group user system by offering cipher text re-encryption that allows accessing processed cipher-texts. Through the cooperation of a Data Provider, to increase the flexibility and security of our system, However apply multiple Services to take in charge of the data from their users and design computing operations over cipher-texts belonging to multiple Service. The analysis completed on proves that our Preserving the Privacy of Big Data Processing method's to performance in terms of security is good on some datasets, inefficiency this also ensures the security and user privacy.

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Section: Related Workmentioning

confidence: 99%

Enhanced Homomorphic Re-Encryption using Laplacian for Preserving the Privacy in Big Data Analytics

2020

IJRTE

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“…Authors in [12] discussed the enhanced FHE with a focus on the dynamic structure of Fully HESs that are symmetric in nature. Regarding secure outsourced calculations using HE, the authors in [13] discussed the encrypted data processing protocols by applying HE over addition and proxy cryptography. Their system aimed to attain the secrecy of the user data while preserving the intermediary and final results security under the semi-honest model.…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of RSA-based Secure Cloud Storage Protocol using OpenStack

Hyder

Tooba

Waseemullah

2021

Eng. Technol. Appl. Sci. Res.

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In this paper, the implementation of the General Secure Cloud Storage Protocol is carried out and instantiated by a multiplicatively Homomorphic Encryption Scheme (HES). The protocol provides a system for secure storage of data over the cloud, thereby allowing the client to carry out the operational tasks on it efficiently. The work focuses on the execution of five major modules of the protocol. We also evaluate the performance of the protocol with respect to the computation cost of these modules on the basis of different security parameters and datasets by conducting a series of experiments. The cloud was built using OpenStack and the data were outsourced from the client’s system to the cloud to study the security features and performance metrics when adopting the cloud environment.

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“…It avoids the encryption process on the plain text, which forces the decryption process, which prolongs the steps. Homomorphic encryption result is the same with encryption to plaintext [3]. It allows safe data storage in the database in the form of ciphertext, and it achieves a faster processing time compared to the use of regular cryptographic algorithms.…”

Section: Introductionmentioning

confidence: 99%

Load Balancing Server and Homomorphic Encryption in Internet of Things

Amrullah¹,

Dewanta

Sussi

2021

khif

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User demand for Internet of Things (IoT) services has been increasing. The growing number of user demand can lead to an escalation of server workloads and threat of critical data theft. Consequently, a system is necessary to balance the server load where the data is protected with encryption. In this study, we designed a system to share server workloads using load balancing methods. The load balancing technique uses open-source web server software. The system is equipped with data security using a homomorphic encryption algorithm from AES on the sender's side. The system embeds in an IoT telemedicine apparatus. During testing, we analyze the error requests that arrive at each server for the HTTP GET and POST methods. We also evaluate the speed of data encryption and decryption. The results showed that server load balancing reduces the number of error requests for the GET method by 97%. Meanwhile, the number of error requests for the POST method decreases by 66.75%. Observations reveal that the average homomorphic encryption speed, computation time, and decryption time are 15.66 ms, 764.18 µs, and 362.49 µs, respectively.

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Secure Outsourced Calculations with Homomorphic Encryption

Cited by 5 publications

References 20 publications

Enhanced Homomorphic Re-Encryption using Laplacian for Preserving the Privacy in Big Data Analytics

Enhanced Homomorphic Re-Encryption using Laplacian for Preserving the Privacy in Big Data Analytics

Performance Evaluation of RSA-based Secure Cloud Storage Protocol using OpenStack

Load Balancing Server and Homomorphic Encryption in Internet of Things

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