India is one of the world’s largest democracies with a community of 1.1 billion; India has an electorate of 714 million persons over 828 thousands polling stations, 1.37 million voting machines and 5.5 million polling officials cover 543 parliamentary constituencies. Past experience of electoral process enforced us to focus on the use of latest technology in E-voting process. The current voting...

This paper examines the property of the m votes to win mechanism. Voting is an effective way to make a collective decision but voting behaviors, e.g., monitoring the voting process, may incur a cost, that is, voting is often costly. In this case, compulsory voting incurs a larger cost. Random decision making can reduce the cost for voting but is skeptical in the quality of decision making. That...

In recent years, computer and network-based voting technologies have been gradually adopted for various elections. However, due to the fragile nature of electronic ballots and voting software, computer voting has posed serious security challenges. This chapter studies the security of computer voting and focuses on a cryptographic solution based on mix-nets. Like traditional voting systems, mix-...

In this paper, we propose and have implemented the first verifiable classroom voting system. The subject of secure classroom voting has so far received almost no attention from the security community. Though several commercial classroom voting systems have been available, none of them is verifiable. State-of-the-art verifiable voting protocols all rely on finding a set of trustworthy tallying a...

Malicious software (malware) represents a threat to the security and the privacy of computer users. Traditional signature-based and heuristic-based methods are inadequate for detecting some forms of malware. This paper presents a malware detection method based on supervised learning. The main contributions of the paper are two ensemble learning algorithms, two pre-processing techniques, and an ...

We address instance-based learning from a perceptual organization standpoint and present methods for dimensionality estimation, manifold learning and function approximation. Under our approach, manifolds in high-dimensional spaces are inferred by estimating geometric relationships among the input instances. Unlike conventional manifold learning, we do not perform dimensionality reduction, but i...