A Modified Symmetric Key Fully Homomorphic Encryption Scheme Based on Read-Muller Code

Challa, Ratnakumari; Gunta, VijayaKumari

doi:10.21123/bsj.2021.18.2(suppl.).0899

Cited by 5 publications

(6 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The output of these computations was also encrypted in [17]. It became a common method to secure data in the cloud [18].…”

Section: Homomorphic Encryptionmentioning

confidence: 99%

Preserving Genotype Privacy Using AES and Partially Homomorphic Encryption

Yousif,

Hameed

2024

Iraqi Journal of Science

View full text Add to dashboard Cite

Increasingly, the availability of personal genomic data in cloud servers hosted by hospitals and research centers has incentivized researchers to turn to research that deals with analyzing genomic data. This is due to its importance in detecting diseases caused by genetic mutations, detecting genes that carry genetic diseases, and attempting to treat them in future generations. Secure query execution on encrypted data is considered an active research area in which encryption is used to ensure the confidentiality of genomic data while restricting the ability to process such data without first decrypting it. To provide a secure framework and future insight into the potential contributions of homomorphic encryption to the field of genomic data, this paper proposes a framework for guaranteeing genomic data privacy using various partial homomorphic encryption techniques. By examining the characteristics of the three partial homomorphic encryptions based on different parameters. The framework has been online tested and compared based on different parameters. Three homomorphic encryption algorithms were adopted to ensure genomic data privacy by employing homomorphic operations in the query matching process. Experiments on real datasets, specifically MERS and SARSr-COV, showed that the proposed framework is efficient and improves query execution time by an average of 96% compared to existing work.

show abstract

“…The output of these computations was also encrypted in [17]. It became a common method to secure data in the cloud [18].…”

Section: Homomorphic Encryptionmentioning

confidence: 99%

Preserving Genotype Privacy Using AES and Partially Homomorphic Encryption

Yousif,

Hameed

2024

Iraqi Journal of Science

View full text Add to dashboard Cite

show abstract

“…Concurrently, the Vigenère cipher, known for its encryption strength, guards message confidentiality. Performance enhancements with respect to this aspect would involve evaluating the algorithm's processing speed and resource utilization to gauge its contribution to the system's overall performance improvements discerption regarding the reference [21][22][23] .…”

Section: Figure 3 Proposed Algorithmmentioning

confidence: 99%

تعزيز الأمن السيبراني من خلال التشفير الهجين: الجمع بين خوارزميات RSA وVigenère في نظام Cypher-X

AlSideiri,

AlShamsi,

AlBreiki

et al. 2024

Baghdad Sci.J

View full text Add to dashboard Cite

لا يزال أمن البيانات مصدر قلق في عصرنا الرقمي، مع فشل العديد من أنظمة التشفير بسبب تعقيدها وقابليتها للتهديدات السيبرانية. Cypher-X، هو نظام تشفير وفك تشفير متطور، يدمج الخوارزميات الكلاسيكية، RSA وVigenère، لحماية سرية البيانات. ويسعى مشروعنا إلى معالجة هذه المخاوف من خلال التركيز على ثلاثة أهداف رئيسية: تعزيز أمن البيانات، وتقييم كفاءة النظام، وتحديد نقاط الضعف التي يستغلها القراصنة. تؤكد النتائج التي توصلنا إليها قدرات التشفير القوية لـ Cypher-X، مع احتمالات تعزيزها بشكل أكبر من خلال تكامل المصادقة متعددة العوامل وآليات التحكم في الوصول. بينما نتعامل مع المشكلة المتمثلة في أمن البيانات، تبرز Cypher-X كمنارة للأمل، حيث تقدم حلاً واعدًا لحماية المعلومات الحساسة في المشهد الرقمي.

show abstract

“…Challa et al 26 suggested a modified Reed-Muller Code-based symmetric key fully homomorphic encryption that enables both (MOD 2) additive and multiplication operations limitlessly. The proof of security provides a mathematical analysis and difficulty level.…”

Section: Related Workmentioning

confidence: 99%

Retrieving Encrypted Images Using Convolution Neural Network and Fully Homomorphic Encryption

Alsaedi,

Farhan

2023

Baghdad Sci.J

View full text Add to dashboard Cite

A content-based image retrieval (CBIR) is a technique used to retrieve images from an image database. However, the CBIR process suffers from less accuracy to retrieve images from an extensive image database and ensure the privacy of images. This paper aims to address the issues of accuracy utilizing deep learning techniques as the CNN method. Also, it provides the necessary privacy for images using fully homomorphic encryption methods by Cheon, Kim, Kim, and Song (CKKS). To achieve these aims, a system has been proposed, namely RCNN_CKKS, that includes two parts. The first part (offline processing) extracts automated high-level features based on a flatting layer in a convolutional neural network (CNN) and then stores these features in a new dataset. In the second part (online processing), the client sends the encrypted image to the server, which depends on the CNN model trained to extract features of the sent image. Next, the extracted features are compared with the stored features using a Hamming distance method to retrieve all similar images. Finally, the server encrypts all retrieved images and sends them to the client. Deep-learning results on plain images were 97.94% for classification and 98.94% for retriever images. At the same time, the NIST test was used to check the security of CKKS when applied to Canadian Institute for Advanced Research (CIFAR-10) dataset. Through these results, researchers conclude that deep learning is an effective method for image retrieval and that a CKKS method is appropriate for image privacy protection.

show abstract

A Modified Symmetric Key Fully Homomorphic Encryption Scheme Based on Read-Muller Code

Cited by 5 publications

References 24 publications

Preserving Genotype Privacy Using AES and Partially Homomorphic Encryption

Preserving Genotype Privacy Using AES and Partially Homomorphic Encryption

تعزيز الأمن السيبراني من خلال التشفير الهجين: الجمع بين خوارزميات RSA وVigenère في نظام Cypher-X

Retrieving Encrypted Images Using Convolution Neural Network and Fully Homomorphic Encryption

Contact Info

Product

Resources

About