Fair and Secure Multi-Party Computation with Cheater Detection

Secure multi-party computation (SMC) is a cryptographic protocol that allows participants to compute the desired output without revealing their inputs. A variety of results related to increasing the efficiency of SMC protocol have been reported, and thus, SMC can be used in various applications. With the SMC protocol in smart grids, it becomes possible to obtain information for load balancing and various statistics, without revealing sensitive user information. To prevent malicious users from tampering with input values, SMC requires cheater detection. Several studies have been conducted on SMC with cheater detection, but none of these has been able to guarantee the fairness of the protocol. In such cases, only a malicious user can obtain a correct output prior to detection. This can be a critical problem if the result of the computation is real-time information of considerable economic value. In this paper, we propose a fair and secure multi-party computation protocol, which detects malicious parties participating in the protocol before computing the final output and prevents them from obtaining it. The security of our protocol is proven in the universal composability framework. Furthermore, we develop an enhanced version of the protocol that is more efficient when computing an average after detecting cheaters. We apply the proposed protocols to a smart grid as an application and analyze their efficiency in terms of computational cost.

Cheater Detection Using the Unrelated Question Model

Randomized response techniques (RRTs) are useful survey tools for estimating the prevalence of sensitive issues, such as the prevalence of doping in elite sports. One type of RRT, the unrelated question model (UQM), has become widely used because of its psychological acceptability for study participants and its favorable statistical properties. One drawback of this model, however, is that it does not allow for detecting cheaters—individuals who disobey the survey instructions and instead give self-protecting responses. In this article, we present refined versions of the UQM designed to detect the prevalence of cheating responses. We provide explicit formulas to calculate the parameters of these refined UQM versions and show how the empirical adequacy of these versions can be tested. The Appendices contain R-code for all necessary calculations.

Leadership, credibility and persuasion

This short paper makes a tentative attempt to capture the most salient of persuasion strategies engaged in the construction of leadership in three different yet apparently interrelated domains of public life and public policy, political communication, management/business discourse, and academic communication. It explores the cognitive underpinnings, as well as linguistic realizations, of such concepts/phenomena/mechanisms as consistency-building, source-tagging, forced conceptualizations by metaphor, and discursive neutralization of the cheater detection module in the discourse addressee. A preliminary conclusion from the analysis of these mechanisms is that the three discourses under investigation reveal striking conceptual similarities with regard to the main strategies of credibility-building and enactment of leadership. At the same time, they reveal differences at the linguistic level, i.e. regarding the types of lexical choices applied to realize a given strategy.

The neuroscience of vision and pain: evolution of two disciplines

Research in the neuroscience of pain perception and visual perception has taken contrasting paths. The contextual and the social aspects of pain judgements predisposed pain researchers to develop computational and functional accounts early, while vision researchers tended to simple localizationist or descriptive approaches first. Evolutionary thought was applied to distinct domains, such as game-theoretic approaches to cheater detection in pain research, versus vision scientists' studies of comparative visual ecologies. Both fields now contemplate current motor or decision-based accounts of perception, particularly predictive coding. Vision researchers do so without the benefit of earlier attention to social and motivational aspects of vision, while pain researchers lack a comparative behavioural ecology of pain, the normal incidence and utility of responses to tissue damage. Hybrid hypotheses arising from predictive coding as used in both domains are applied to some perplexing phenomena in pain perception to suggest future directions. The contingent and predictive interpretation of complex sensations, in such domains as ‘runner's high’, multiple cosmetic procedures, self-harm and circadian rhythms in pain sensitivity is one example. The second, in an evolutionary time frame, considers enhancement of primary perception and expression of pain in social species, when expressions of pain might reliably elicit useful help. This article is part of the Theo Murphy meeting issue ‘Evolution of mechanisms and behaviour important for pain’.

Your Cheatin’ Voice Will Tell on You: Detection of Past Infidelity from Voice

Evidence suggests that many physical, behavioral, and trait qualities can be detected solely from the sound of a person’s voice, irrespective of the semantic information conveyed through speech. This study examined whether raters could accurately assess the likelihood that a person has cheated on committed, romantic partners simply by hearing the speaker’s voice. Independent raters heard voice samples of individuals who self-reported that they either cheated or had never cheated on their romantic partners. To control for aspects that may clue a listener to the speaker’s mate value, we used voice samples that did not differ between these groups for voice attractiveness, age, voice pitch, and other acoustic measures. We found that participants indeed rated the voices of those who had a history of cheating as more likely to cheat. Male speakers were given higher ratings for cheating, while female raters were more likely to ascribe the likelihood to cheat to speakers. Additionally, we manipulated the pitch of the voice samples, and for both sexes, the lower pitched versions were consistently rated to be from those who were more likely to have cheated. Regardless of the pitch manipulation, speakers were able to assess actual history of infidelity; the one exception was that men’s accuracy decreased when judging women whose voices were lowered. These findings expand upon the idea that the human voice may be of value as a cheater detection tool and very thin slices of vocal information are all that is needed to make certain assessments about others.