Coercion-resistance is a security property of electronic voting, often considered as a must-have for high-stake elections. The JCJ voting scheme, proposed in 2005 by Juels, Catalano and Jakobsson, is still the reference paradigm when designing a coercion-resistant protocol. We highlight a weakness in JCJ that is also present in all the systems following its general structure. This comes from the procedure that precedes the tally, where the trustees remove the ballots that should not be counted. This phase leaks more information than necessary, leading to potential threats for the coerced voters. Fixing this leads to the notion of cleansing-hiding, that we apply to form a variant of JCJ that we call CHide. One reason for the problem not being seen before is the fact that the associated formal definition of coercion-resistance was too weak. We therefore propose a definition that takes into account more behaviors such as revoting or the addition of fake ballots by authorities. We then prove that CHide is coercion-resistant for this definition.

This paper explores the interaction between receipt- freeness and cast-as-intended verifiability, a property that has been overlooked until now or assumed to be granted through procedural means in the context of receipt-free voting protocols.We first demonstrate that it is impossible to obtain a receipt-free voting protocol with cast-as-intended verifiability if the voting process is non-interactive, unless a trusted authority is available. We also demonstrate that, if a trusted voter registration authority is available, then cast-as-intended verifiability and receipt-freeness can be obtained.Furthermore, after extending standard receipt-freeness security definitions to an interactive voting (and corruption) setting, we demonstrate that the same security properties can be obtained using an interactive voting process.Finally, we discuss the performance of our protocols based on a prototype implementation.

Understanding when evolutionary algorithms are efficient or not, and how they efficiently solve problems, is one of the central research tasks in evolutionary computation. In this work, we make progress in understanding the interplay between parent and offspring population size of the (µ, λ) EA. Previous works, roughly speaking, indicate that for λ ≥ (1 + ε)eµ, this EA easily optimizes the OneMax function, whereas an offspring population size λ ≤ (1 - ε)eµ leads to an exponential runtime.