Model Checking Social Network Models

A social network service is a platform to build social relations among people sharing similar interests and activities. The underlying structure of a social networks service is the social graph, where nodes represent users and the arcs represent the users' social links and other kind of connections. One important concern in social networks is privacy: what others are (not) allowed to know about us. The "logic of knowledge" (epistemic logic) is thus a good formalism to define, and reason about, privacy policies. In this paper we consider the problem of verifying knowledge properties over social network models (SNMs), that is social graphs enriched with knowledge bases containing the information that the users know. More concretely, our contributions are: i) We prove that the model checking problem for epistemic properties over SNMs is decidable; ii) We prove that a number of properties of knowledge that are sound w.r.t. Kripke models are also sound w.r.t. SNMs; iii) We give a satisfaction-preserving encoding of SNMs into canonical Kripke models, and we also characterise which Kripke models may be translated into SNMs; iv) We show that, for SNMs, the model checking problem is cheaper than the one based on standard Kripke models. Finally, we have developed a proof-of-concept implementation of the model-checking algorithm for SNMs.Comment: In Proceedings GandALF 2017, arXiv:1709.0176

ROSA Analyser: An automatized approach to analyse processes of ROSA

In this work we present the first version of ROSA Analyser, a tool designed to get closer to a fully automatic process of analysing the behaviour of a system specified as a process of the Markovian Process Algebra ROSA. In this first development stage, ROSA Analyser is able to generate the Labelled Transition System, according to ROSA Operational Semantics. ROSA Analyser performance starts with the Syntactic Analysis so generating a layered structure, suitable to then, apply the Operational Semantics Transition rules in the easier way. ROSA Analyser is able to recognize some states identities deeper than the Syntactic ones. This is the very first step in the way to reduce the size of the LTS and then to avoid the state explosion problem, so making this task more tractable. For the sake of better illustrating the usefulness of ROSA Analyser, a case study is also provided within this work.Comment: In Proceedings WS-FMDS 2012, arXiv:1207.1841. Formal model's too

Characterization of Magnetic Phases in Nanostructured Ferrites by Electron Spin Resonance

This chapter is dedicated to the analysis of the spin resonance response (ESR) of different magnetic phases, in nanoparticles (NPs) of magnetic oxides, or ferrites. Evidence of the correlations between resonance spectrum and magnetic structure has been published, of course, in many works; however, to our knowledge, it is somewhat scattered and not easily accessible. We have chosen to carry out this analysis mainly on ferrite NPs because these magnetic materials exhibit a wide variety of magnetic properties, and as a consequence, a large diversity of classic and novel applications in technological fields ranging from electronics to biomedics

Exact and Efficient Bayesian Inference for Privacy Risk Quantification

Data analysis has high value both for commercial and research purposes. However, disclosing analysis results may pose severe privacy risk to individuals. Privug is a method to quantify privacy risks of data analytics programs by analyzing their source code. The method uses probability distributions to model attacker knowledge and Bayesian inference to update said knowledge based on observable outputs. Currently, Privug uses Markov Chain Monte Carlo (MCMC) to perform inference, which is a flexible but approximate solution. This paper presents an exact Bayesian inference engine based on multivariate Gaussian distributions to accurately and efficiently quantify privacy risks. The inference engine is implemented for a subset of Python programs that can be modeled as multivariate Gaussian models. We evaluate the method by analyzing privacy risks in programs to release public statistics. The evaluation shows that our method accurately and efficiently analyzes privacy risks, and outperforms existing methods. Furthermore, we demonstrate the use of our engine to analyze the effect of differential privacy in public statistics

Exact and Efficient Bayesian Inference for Privacy Risk Quantification (Extended Version)

Data analysis has high value both for commercial and research purposes. However, disclosing analysis results may pose severe privacy risk to individuals. Privug is a method to quantify privacy risks of data analytics programs by analyzing their source code. The method uses probability distributions to model attacker knowledge and Bayesian inference to update said knowledge based on observable outputs. Currently, Privug uses Markov Chain Monte Carlo (MCMC) to perform inference, which is a flexible but approximate solution. This paper presents an exact Bayesian inference engine based on multivariate Gaussian distributions to accurately and efficiently quantify privacy risks. The inference engine is implemented for a subset of Python programs that can be modeled as multivariate Gaussian models. We evaluate the method by analyzing privacy risks in programs to release public statistics. The evaluation shows that our method accurately and efficiently analyzes privacy risks, and outperforms existing methods. Furthermore, we demonstrate the use of our engine to analyze the effect of differential privacy in public statistics

A multi-agent traffic simulation framework for evaluating the impact of traffic lights

This is an electronic version of the paper presented at the 3rd International Conference on Agents and Artificial Intelligence, held in Rome on 2011The growing of the number of vehicles cause serious strains on road infrastructures. Traffic jams inevitably occur, wasting time and money for both cities and their drivers. To mitigate this problem, traffic simulation tools based on multiagent techniques can be used to quickly prototype potentially problematic scenarios to better understand their inherent causes. This work centers around the effects of traffic light configuration on the flow of vehicles in a road network. To do so, a Multi-Agent Traffic Simulation Framework based on Particle Swarm Optimization techniques has been designed and implemented. Experimental results from this framework show an improvement in the average speed obtained by traffic controlled by adaptive over static traffic lights.This work has been supported by the Spanish Ministry of Science and Innovation. Grant TIN2010- 1987

A multi-agent simulation platform applied to the study of urban traffic lights

Proceedings of 6th International Conference on Software and Data Technologies, ICSOFT 2011The Multi-Agent system paradigm allows the development of complex software platforms to be used in a wide range of real-world scenarios. One of the most successful areas these technologies have been applied are in the simulation and optimization of complex systems. Traffic simulation/optimization problems are a specially suitable target for such a platform. This paper proposes a new Multi-Agent simulation platform, where agents are based on a Swarm model (lightweight agents with very low autonomy or proactivity). Using this framework, simulation designers are free to configure road networks of arbitrary complexity, by customizing road width, geometry and intersection with other roads. To simulate different traffic flow scenarios, vehicle trajectories can be defined by choosing start and end locations and providing traffic generation functions for each one trajectory defined. Finally, how many vehicles are generated at each time step can be determined by a time series function. The domain of traffic simulation has been selected to investigate the effect of traffic light configuration on the flow of vehicles in a road network. The experimental results from this platform show a strong correlation between traffic light behavior and the flow of traffic through the network that affects the congestion of the road.This work has been partially supported by the Spanish Ministry of Science and Innovation under grant TIN2010-19872 and by Jobssy.com