A collective intelligence approach for building student's trustworthiness profile in online learning

(c) 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Information and communication technologies have been widely adopted in most of educational institutions to support e-Learning through different learning methodologies such as computer supported collaborative learning, which has become one of the most influencing learning paradigms. In this context, e-Learning stakeholders, are increasingly demanding new requirements, among them, information security is considered as a critical factor involved in on-line collaborative processes. Information security determines the accurate development of learning activities, especially when a group of students carries out on-line assessment, which conducts to grades or certificates, in these cases, IS is an essential issue that has to be considered. To date, even most advances security technological solutions have drawbacks that impede the development of overall security e-Learning frameworks. For this reason, this paper suggests enhancing technological security models with functional approaches, namely, we propose a functional security model based on trustworthiness and collective intelligence. Both of these topics are closely related to on-line collaborative learning and on-line assessment models. Therefore, the main goal of this paper is to discover how security can be enhanced with trustworthiness in an on-line collaborative learning scenario through the study of the collective intelligence processes that occur on on-line assessment activities. To this end, a peer-to-peer public student's profile model, based on trustworthiness is proposed, and the main collective intelligence processes involved in the collaborative on-line assessments activities, are presented.Peer ReviewedPostprint (author's final draft

Dynamic Information Flow Analysis in Ruby

With the rapid increase in usage of the internet and online applications, there is a huge demand for applications to handle data privacy and integrity. Applications are already complex with business logic; adding the data safety logic would make them more complicated. The more complex the code becomes, the more possibilities it opens for security-critical bugs. To solve this conundrum, we can push this data safety handling feature to the language level rather than the application level. With a secure language, developers can write their application without having to worry about data security. This project introduces dynamic information flow analysis in Ruby. I extend the JRuby implementation, which is a widely used implementation of Ruby written in Java. Information flow analysis classifies variables used in the program into different security levels and monitors the data flow across levels. Ruby currently supports data integrity by a tainting mechanism. This project extends this tainting mechanism to handle implicit data flows, enabling it to protect confidentiality as well as integrity. Experimental results based on Ruby benchmarks are presented in this paper, which show that: This project protects confidentiality but at the cost of 1.2 - 10 times slowdown in execution time

Systematizing Decentralization and Privacy: Lessons from 15 Years of Research and Deployments

Decentralized systems are a subset of distributed systems where multiple authorities control different components and no authority is fully trusted by all. This implies that any component in a decentralized system is potentially adversarial. We revise fifteen years of research on decentralization and privacy, and provide an overview of key systems, as well as key insights for designers of future systems. We show that decentralized designs can enhance privacy, integrity, and availability but also require careful trade-offs in terms of system complexity, properties provided, and degree of decentralization. These trade-offs need to be understood and navigated by designers. We argue that a combination of insights from cryptography, distributed systems, and mechanism design, aligned with the development of adequate incentives, are necessary to build scalable and successful privacy-preserving decentralized systems