An electronic voting system based on homomorphic encryption and prime numbers

Azougaghe, Ali; Hedabou, Mustapha; Belkasmi, Mostafa

doi:10.1109/isias.2015.7492759

Cited by 12 publications

(7 citation statements)

References 8 publications

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“…In contrast to gain agreement for the high-frequency element of the image, which is the basic principle behind “adaptive contrast enhancement (ACE),” it consists of limitation in terms of high processing time ( 32 – 38 ). In MBD classification, local details are more important than global features and reduce processing time; the proposed architecture uses the contrast limited adaptive histogram equalization (CLAHE) algorithm for limited contrast enrichment of fibroglandular tissues ( 39 – 41 ).…”

Section: Proposed Methodologymentioning

confidence: 99%

Multichannel DenseNet Architecture for Classification of Mammographic Breast Density for Breast Cancer Detection

Pawar

Sharma

Sapate³

et al. 2022

Front. Public Health

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Percentage mammographic breast density (MBD) is one of the most notable biomarkers. It is assessed visually with the support of radiologists with the four qualitative Breast Imaging Reporting and Data System (BIRADS) categories. It is demanding for radiologists to differentiate between the two variably allocated BIRADS classes, namely, “BIRADS C and BIRADS D.” Recently, convolution neural networks have been found superior in classification tasks due to their ability to extract local features with shared weight architecture and space invariance characteristics. The proposed study intends to examine an artificial intelligence (AI)-based MBD classifier toward developing a latent computer-assisted tool for radiologists to distinguish the BIRADS class in modern clinical progress. This article proposes a multichannel DenseNet architecture for MBD classification. The proposed architecture consists of four-channel DenseNet transfer learning architecture to extract significant features from a single patient's two a mediolateral oblique (MLO) and two craniocaudal (CC) views of digital mammograms. The performance of the proposed classifier is evaluated using 200 cases consisting of 800 digital mammograms of the different BIRADS density classes with validated density ground truth. The classifier's performance is assessed with quantitative metrics such as precision, responsiveness, specificity, and the area under the curve (AUC). The concluding preliminary outcomes reveal that this intended multichannel model has delivered good performance with an accuracy of 96.67% during training and 90.06% during testing and an average AUC of 0.9625. Obtained results are also validated qualitatively with the help of a radiologist expert in the field of MBD. Proposed architecture achieved state-of-the-art results with a fewer number of images and with less computation power.

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Section: Proposed Methodologymentioning

confidence: 99%

Multichannel DenseNet Architecture for Classification of Mammographic Breast Density for Breast Cancer Detection

Pawar

Sharma

Sapate³

et al. 2022

Front. Public Health

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“…The large passenger flow and complex environmental characteristics of urban rail transit also affect the application of face recognition technology in urban rail transit. In this regard, the application of a face recognition system in urban rail transit should also pay attention to the following aspects: the uncertainty in the process of video image acquisition [26][27][28][29]. Due to the complex environment of urban rail transit, such as lighting, installation location, occlusion, and human posture, the acquired video and image quality are different, and the face acquisition is unclear or lacking [30][31][32], which affects the recognition rate.…”

Section: Literature Reviewmentioning

confidence: 99%

Design and Study of Urban Rail Transit Security System Based on Face Recognition Technology

Guo

Xiao

Alroobaea

et al. 2022

IJCAI

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In the modern world, it is difficult to prevent terrorism due to the relatively closed environment, dense personnel, large passenger flow, long line and wide coverage of urban rail transit. Identity recognition is a core element of security. The design and study of an urban rail transit security system based on face recognition technology are proposed in this paper. Through the study on the face recognition algorithm of intelligent security systems in urban rail transit, the related introduction of face recognition technology is done. The analysis of the main mode of face recognition is carried out utilizing the practical application design ideas. The results by experimental analysis show that if FAR is set to a very low range (such as 0.1% or even 0.01%) meanwhile FRR can reach a very low level (such as less than 1%). Such a system has practical value and otherwise, it may face a large number of passenger affairs and complaints to be handled. When FAR is set to 0.1% and N is 1.6 million, FRR can reach 2.1%. However, according to the test, when the picture quality deteriorates (during image captured by a webcam), the FRR will increase by 2 to 3 times. If a Webcam is used for recognition in Mugshot, the lowest FRR of the three top algorithms is only 5.21%.Povzetek: Tehnologija prepoznavanja obrazov je uporabljena za nadzor osumljencev -teroristov na vlakih.

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“…We present the running time of Koinonia Library in Table 1 and show that the computation cost of our implementation is small, and the system can be deployed on commodity machine 4 . In terms of storage cost, for an election with one position (e.g.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…Most of these systems focus primarily on integrity and have not considered long-term privacy as their main concerns. E-voting protocols typically utilize the following cryptographic techniques: mix-net [1,3,14,16,18,19,22,29,41,45,56,57,63,66], homomorphic encryption [2,4,25,27,28,40,43,47], blind signature [12,17,44,51,54] and secret sharing based on polynomials [6,7,27,59] or simple (n, n) secret sharing [68]. Despite numerous previous work on everlasting voting privacy [10,11,27,32,33,[46][47][48][49][50]64], we note that the adversary models under which these schemes can achieve privacy could be different.…”

Section: Related Workmentioning

confidence: 99%

Koinonia

Chau

Gonsalves

et al. 2019

Proceedings of the 35th Annual Computer Security Applications Conference

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Despite years of research, many existing e-voting systems do not adequately protect voting privacy. In most cases, such systems only achieve "immediate privacy", that is, they only protect voting privacy against today's adversaries, but not against a future adversary, who may possess better attack technologies like new cryptanalysis algorithms and/or quantum computers. Previous attempts at providing long-term voting privacy (dubbed "everlasting privacy" in the literature) often require additional trusts in parties that do not need to be trusted for immediate privacy.In this paper, we present a framework of adversary models regarding e-voting systems, and analyze possible threats to voting privacy under each model. Based on our analysis, we argue that secret-sharing based voting protocols offer a more natural and elegant privacy-preserving solution than their encryption-based counterparts. We thus design and implement Koinonia, a voting system that provides long-term privacy against powerful adversaries and enables anyone to verify that each ballot is well-formed and the tallying is done correctly. Our experiments show that Koinonia protects voting privacy with a reasonable performance.

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An electronic voting system based on homomorphic encryption and prime numbers

Cited by 12 publications

References 8 publications

Multichannel DenseNet Architecture for Classification of Mammographic Breast Density for Breast Cancer Detection

Multichannel DenseNet Architecture for Classification of Mammographic Breast Density for Breast Cancer Detection

Design and Study of Urban Rail Transit Security System Based on Face Recognition Technology

Koinonia

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