3 research outputs found
Grid Cryptographic Simulation: A Simulator to Evaluate the Scalability of the X.509 Standard in the Smart Grid
PKI may be pushed beyond known limits when scaled to some visions of the smart grid; our research developed a simulation, Grid Cryptographic Simulation (GCS), to evaluate these potential issues, identify cryptographic bottlenecks, and evaluate tradeoffs between performance and security. Ultimately, GCS can be used to identify scalability challenges and suggest improvements to make PKI more efficient, effective, and scalable before it is deployed in the envisioned smart grid
Modeling and Evaluation of Certification Path Discovery in the Emerging Global PKI
Abstract. Establishing trust on certificates across multiple domains requires an efficient certification path discovery algorithm. Previously, small exmaples are used to analyze the performance of certification path discovery. In this work, we propose and implement a simulation framework and a probability search tree model for systematic performance evaluation. Built from measurement data collected from current PKI systems in development and deployment over more than 10 countries, our model is (to the best of our knowledge) the largest simulated PKI architecture to-date.
Improving accuracy of recommender systems through triadic closure
The exponential growth of social media services led to the information overload problem
which information filtering and recommender systems deal by exploiting various techniques.
One popular technique for making recommendations is based on trust statements between
users in a social network. Yet explicit trust statements are usually very sparse leading to the
need for expanding the trust networks by inferring new trust relationships. Existing methods
exploit the propagation property of trust to expand the existing trust networks; however, their
performance is strongly affected by the density of the trust network. Nevertheless, the
utilisation of existing trust networks can model the users’ relationships, enabling the inference
of new connections. The current study advances the existing methods and techniques on
developing a trust-based recommender system proposing a novel method to infer trust
relationships and to achieve a fully-expanded trust network. In other words, the current study
proposes a novel, effective and efficient approach to deal with the information overload by
expanding existing trust networks so as to increase accuracy in recommendation systems.
More specifically, this study proposes a novel method to infer trust relationships, called
TriadicClosure. The method is based on the homophily phenomenon of social networks and,
more specifically, on the triadic closure mechanism, which is a fundamental mechanism of link
formation in social networks via which communities emerge naturally, especially when the
network is very sparse. Additionally, a method called JaccardCoefficient is proposed to
calculate the trust weight of the inferred relationships based on the Jaccard Cofficient
similarity measure. Both the proposed methods exploit structural information of the trust
graph to infer and calculate the trust value.
Experimental results on real-world datasets demonstrate that the TriadicClosure method
outperforms the existing state-of-the-art methods by substantially improving prediction
accuracy and coverage of recommendations. Moreover, the method improves the
performance of the examined state-of-the-art methods in terms of accuracy and coverage
when combined with them. On the other hand, the JaccardCoefficient method for calculating
the weight of the inferred trust relationships did not produce stable results, with the majority
showing negative impact on the performance, for both accuracy and coverage