BEvote: Bitcoin-Enabled E-Voting Scheme with Anonymity and Robustness

When building the large-scale distributed decision control system based on mobile terminal devices (MTDs), electronic voting (E-voting) is a necessary technique to settle the dispute among parties. Due to the inherent insecurity of Internet, it is difficult for E-voting to attain complete fairness and robustness. In this study, we argue that Bitcoin blockchain offers better options for a more practical E-voting. We first present a coin mixing-based E-voting system model, which can cut off the relationship between the voter’s real identity and its Bitcoin address to achieve strong anonymity. Moreover, we devise a secret sharing-based E-voting protocol, which can prevent voting number from being leaked ahead and further realize strong robustness. We establish the probable security theory to prove its security. In addition, we use the experimental evaluation to demonstrate its efficiency.

Investigating Usability and User Experience of Individually Verifiable Internet Voting Schemes

Internet voting can afford more inclusive and inexpensive elections. The flip side is that the integrity of the election can be compromised by adversarial attacks and malfunctioning voting infrastructure. Individual verifiability aims to protect against such risks by letting voters verify that their votes are correctly registered in the electronic ballot box. Therefore, voters need to carry out additional tasks making human factors crucial for security. In this article, we establish a categorization of individually verifiable Internet voting schemes based on voter interactions. For each category in our proposed categorization, we evaluate a voting scheme in a user study with a total of 100 participants. In our study, we assessed usability, user experience, trust, and further qualitative data to gain deeper insights into voting schemes. Based on our results, we conclude with recommendations for developers and policymakers to inform the choices and design of individually verifiable Internet voting schemes.

An Improved Coercion-Resistant E-Voting Scheme

E-voting has gradually replaced the traditional voting methods to make it easier for people to conduct an election. Recently, Liu et al. propose an unconditional secure e-voting scheme using secret sharing and k-anonymity. Their scheme achieves correct tallying results without revealing raw voting information. However, in this paper, we observe that Liu et al.’s scheme cannot achieve coercion resistance in e-voting since the voter can prove the content of his ballot to the colluded candidates. Then, we propose an improved e-voting scheme to cover up the ballot of the voter with masked values. In this way, even if the voter colludes with corresponding candidates, he cannot prove which candidate he has voted for. Moreover, comparing with Liu et al.’s scheme, the security analysis shows that our proposed e-voting scheme achieves these security requirements like the coercion resistance, integrity of ballots, privacy of ballots, multiple-voting detection, and fairness. Through performance analysis, the experimental results show that our proposed e-voting scheme has higher time efficiency. Compared with other schemes, our scheme achieves a complete voting process and obtains the correct tallying result without complex computation and intricate communication process.

Secure Blockchain Decentralized Voting for Verified Users

The paper focuses onintroducing a decentralized e-voting scheme that uses blockchain to achieve security and anonymity. A blockchain network based on Ethereum was applied, to provide a decentralized and distributed database based on the Peer-to-Peer architecture. During the implementation, smart contractswere used. Thanks to this, it is possible to code the terms of the contract required to perform the transaction. The proof-of-conceptimplementation uses the blind signature protocol and encryption with the RSA algorithm. Presented in this paper scheme for blockchain decentralized voting is fully implemented and potential issues are analyzed and discussed.

Secure Digital E-Voting System using Blockchain Technology

Technology has great positive impacts on numerous aspects of our social life. Designing a globally connected architecture enables simple access to a range of resources and services. Furthermore, technology rather just like the internet has been a fertile ground for innovation and creativity. The blockchain technology is presented as a game-changer for several existing and emerging technologies. With its immutability property and decentralized architecture, it's taking center stage in many services as an equalization factor to the current parity between consumers and big corporations/governments. one among the fields during which blockchain application is used is E-voting. The target of such a scheme would be to produce a decentralized architecture to run and support a voting scheme that's open, fair, and independently verifiable. this might propose a possible new e-voting protocol that utilizes the blockchain as a transparent box. The protocol helps to attain fundamental e-voting properties additionally as offer a degree of decentralization.

Blockchain Decentralized Voting for Verified Users with a Focus on Anonymity

The paper presents decentralized voting scheme for verified users while maintaining their anonymity. A blockchain network was applied, which is a decentralized and distributed database based on the Peer-to-Peer architecture. During the implementation, the Ethereum network was used. Thanks to this, it is possible to code the terms of the contract required to perform the transaction. Ethereum and the use of smart contracts were also discussed in paper. The implementation uses the blind signature protocol by David Chaum and encryption with the Rivest-Shamir-Adleman (RSA) algorithm. Presented in this paper scheme for blockchain decentralized voting for verified users with focus on anonymity is then fully implemented and identified potential issues are analysed and discussed.

A novel multi-class imbalanced EEG signals classification based on the adaptive synthetic sampling (ADASYN) approach

Background Brain signals (EEG—Electroencephalography) are a gold standard frequently used in epilepsy prediction. It is crucial to predict epilepsy, which is common in the community. Early diagnosis is essential to reduce the treatment process of the disease and to keep the process healthier. Methods In this study, a five-classes dataset was used: EEG signals from different individuals, healthy EEG signals from tumor document, EEG signal with epilepsy, EEG signal with eyes closed, and EEG signal with eyes open. Four different methods have been proposed to classify five classes of EEG signals. In the first approach, the EEG signal was first divided into four different bands (beta, alpha, theta, and delta), and then 25 time-domain features were extracted from each band, and the main EEG signal and these extracted features were combined to obtain 125-time domain features (feature extraction). Using the Random Forests classifier, EEG activities were classified into five classes. In the second approach, each One-Against-One (OVO) approach with 125 attributes was split into ten parts, pairwise, and then each piece was classified with the Random Forests classifier. The majority voting scheme was used to combine decisions from the ten classifiers. In the third proposed method, each One-Against-All (OVA) approach with 125 attributes was divided into five parts, and then each piece was classified with the Random Forests classifier. The majority voting scheme was used to combine decisions from the five classifiers. In the fourth proposed approach, each One-Against-All (OVA) approach with 125 attributes was divided into five parts. Since each piece obtained had an imbalanced data distribution, an adaptive synthetic (ADASYN) sampling approach was used to stabilize each piece. Then, each balanced piece was classified with the Random Forests classifier. To combine the decisions obtanied from each classifier, the majority voting scheme has been used. Results The first approach achieved 71.90% classification success in classifying five-class EEG signals. The second approach achieved a classification success of 91.08% in classifying five-class EEG signals. The third method achieved 89% success, while the fourth proposed approach achieved 91.72% success. The results obtained show that the proposed fourth approach (the combination of the ADASYN sampling approach and Random Forest Classifier) achieved the best success in classifying five class EEG signals. This proposed method could be used in the detection of epilepsy events in the EEG signals.