An efficient and provably-secure coercion-resistant e-voting protocol

Author(s):  
Alireza Toroghi Haghighat ◽  
Mohammad Sadeq Dousti ◽  
Rasool Jalili
Keyword(s):  
Cybersecurity ◽  
2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Jingdian Ming ◽  
Yongbin Zhou ◽  
Huizhong Li ◽  
Qian Zhang

AbstractDue to its provable security and remarkable device-independence, masking has been widely accepted as a noteworthy algorithmic-level countermeasure against side-channel attacks. However, relatively high cost of masking severely limits its applicability. Considering the high tackling complexity of non-linear operations, most masked AES implementations focus on the security and cost reduction of masked S-boxes. In this paper, we focus on linear operations, which seems to be underestimated, on the contrary. Specifically, we discover some security flaws and redundant processes in popular first-order masked AES linear operations, and pinpoint the underlying root causes. Then we propose a provably secure and highly efficient masking scheme for AES linear operations. In order to show its practical implications, we replace the linear operations of state-of-the-art first-order AES masking schemes with our proposal, while keeping their original non-linear operations unchanged. We implement four newly combined masking schemes on an Intel Core i7-4790 CPU, and the results show they are roughly 20% faster than those original ones. Then we select one masked implementation named RSMv2 due to its popularity, and investigate its security and efficiency on an AVR ATMega163 processor and four different FPGA devices. The results show that no exploitable first-order side-channel leakages are detected. Moreover, compared with original masked AES implementations, our combined approach is nearly 25% faster on the AVR processor, and at least 70% more efficient on four FPGA devices.


2011 ◽  
Vol 61 (7) ◽  
pp. 1705-1723 ◽  
Author(s):  
Wei Wu ◽  
Yi Mu ◽  
Willy Susilo ◽  
Xinyi Huang

2015 ◽  
Vol 26 (02) ◽  
pp. 169-194 ◽  
Author(s):  
Koji Nuida ◽  
Takuro Abe ◽  
Shizuo Kaji ◽  
Toshiaki Maeno ◽  
Yasuhide Numata

In this paper, we specify a class of mathematical problems, which we refer to as “Function Density Problems” (FDPs, in short), and point out novel connections of FDPs to the following two cryptographic topics; theoretical security evaluations of keyless hash functions (such as SHA-1), and constructions of provably secure pseudorandom generators (PRGs) with some enhanced security property introduced by Dubrov and Ishai (STOC 2006). Our argument aims at proposing new theoretical frameworks for these topics (especially for the former) based on FDPs, rather than providing some concrete and practical results on the topics. We also give some examples of mathematical discussions on FDPs, which would be of independent interest from mathematical viewpoints. Finally, we discuss possible directions of future research on other crypto-graphic applications of FDPs and on mathematical studies on FDPs themselves.


2022 ◽  
Vol 16 (1) ◽  
pp. 0-0

Secure and efficient authentication mechanism becomes a major concern in cloud computing due to the data sharing among cloud server and user through internet. This paper proposed an efficient Hashing, Encryption and Chebyshev HEC-based authentication in order to provide security among data communication. With the formal and the informal security analysis, it has been demonstrated that the proposed HEC-based authentication approach provides data security more efficiently in cloud. The proposed approach amplifies the security issues and ensures the privacy and data security to the cloud user. Moreover, the proposed HEC-based authentication approach makes the system more robust and secured and has been verified with multiple scenarios. However, the proposed authentication approach requires less computational time and memory than the existing authentication techniques. The performance revealed by the proposed HEC-based authentication approach is measured in terms of computation time and memory as 26ms, and 1878bytes for 100Kb data size, respectively.


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