Hiding mobile traffic fingerprints with GLOVE

Proceeding of: 11th ACM Conference on Emerging Networking Experiments and Technologies ACM (CoNEXT 2015), Heidelberg, Germany, 1-4 December 2015Preservation of user privacy is paramount in the publication of datasets that contain fine-grained information about individuals. The problem is especially critical in the case of mobile traffic datasets collected by cellular operators, as they feature high subscriber trajectory uniqueness and they are resistant to anonymization through spatiotemporal generalization. In this work, we first unveil the reasons behind such undesirable features of mobile traffic datasets, by leveraging an original measure of the anonymizability of users' mobile fingerprints. Building on such findings, we propose GLOVE, an algorithm that grants k-anonymity of trajectories through specialized generalization. We evaluate our methodology on two nationwide mobile traffic datasets, and show that it achieves k-anonymity while preserving a substantial level of accuracy in the data.This work was supported by the French National Research Agency under grant ANR-13-INFR-0005 ABCD and by the EU FP7 ERA-NET program under grant CHIST-ERA-2012 MACACO

On the level of detail of synthetic highway traffic necessary to vehicular networking studies

Proceeding of: 2015 IEEE Vehicular Networking Conference (VNC), Kyoto, Japan, 16-18 December, 2015The proper modeling of road traffic is paramount to the dependability of studies on vehicular networking solutions intended for highway environments. Yet, it is not clear which is the actual level of detail in the mobility representation that is sufficient and necessary to such studies. This uncertainty results into a variety of approaches being adopted in the literature, and ultimately undermines the reliability and reproducibility of research outcomes. We explore the space of possible mobility models and performance metrics, and pinpoint the level of detail needed for different types of vehicular networking studies.The research leading to these results was carried out while Marco Gramaglia was at CNR-IEIIT, and has received funding from the People Programme (Marie Curie Actions) of the European Unions Seventh Framework Programme (FP7/2007-2013) under REA grant agreement n.630211 ReFleX.Publicad

Temporal connectivity of vehicular networks: the power of store-carry-and-forward

Proceeding of: 2015 IEEE Vehicular Networking Conference (VNC), Kyoto, Japan, 16-18 December, 2015Store-carry-and-forward is extensively used in vehicular environments for many and varied purposes, including routing, disseminating, downloading, uploading, or offloading delay-tolerant content. The performance gain of store-carry-and-forward over traditional connected forwarding is primarily determined by the fact that it grants a much improved network connectivity. Indeed, by letting vehicles physically carry data, store-carry-and-forward adds a temporal dimension to the (typically fragmented) instantaneous network topology that is employed by connected forwarding. Temporal connectivity has thus a important role in the operation of a wide range of vehicular network protocols. Still, our understanding of the dynamics of the temporal connectivity of vehicular networks is extremely limited. In this paper, we shed light on this underrated aspect of vehicular networking, by exploring a vast space of scenarios through an evolving graph-theoretical approach. Our results show that using store-carry-and-forward greatly increases connectivity, especially in very sparse networks. Moreover, using store-carry-and-forward mechanisms to share content within a geographically-bounded area can be very efficient, i.e., new entering vehicles can be reached rapidly.This work was done while Marco Gramaglia was at CNR-IEIIT. The research leading to these results has received funding from the People Programme (Marie Curie Actions) of the European Unions Seventh Framework Programme (FP7/2007-2013) under REA grant agreement n.630211 ReFleX. The work of Christian Glacet was carried out during the tenure of an ERCIM “Alain Bensoussan” Fellowship Programme.Publicad

GLOVE: towards privacy-preserving publishing of record-level-truthful mobile phone trajectories

Datasets of mobile phone trajectories collected by network operators offer an unprecedented opportunity to discover new knowledge from the activity of large populations of millions. However, publishing such trajectories also raises significant privacy concerns, as they contain personal data in the form of individual movement patterns. Privacy risks induce network operators to enforce restrictive confidential agreements in the rare occasions when they grant access to collected trajectories, whereas a less involved circulation of these data would fuel research and enable reproducibility in many disciplines. In this work, we contribute a building block toward the design of privacy-preserving datasets of mobile phone trajectories that are truthful at the record level. We present GLOVE, an algorithm that implements k-anonymity, hence solving the crucial unicity problem that affects this type of data while ensuring that the anonymized trajectories correspond to real-life users. GLOVE builds on original insights about the root causes behind the undesirable unicity of mobile phone trajectories, and leverages generalization and suppression to remove them. Proof-of-concept validations with large-scale real-world datasets demonstrate that the approach adopted by GLOVE allows preserving a substantial level of accuracy in the data, higher than that granted by previous methodologies.This work was supported by the Atracción de Talento Investigador program of the Comunidad de Madrid under Grant No. 2019-T1/TIC-16037 NetSense

VANET-based optimization of infotainment and traffic efficiency vehicular services

The design, standardization and future deployment of vehicular communications systems have been driven so far by safety applications. There are two more aspects of the vehicular networking that have increased their importance in the last years: infotainment and traffic efficiency, as they can improve drivers’ experience, making vehicular communications systems more attractive to end-users. In this thesis we propose optimization mechanisms for both types of vehicular services. Infotainment services are related to the provision of classic IP applications, like browsing, reading e-mail or using social networks. Traffic efficiency services are those accessing new capabilities to the car-navigation systems, aiming at optimizing the usage of road infrastructures, reducing travel times and therefore minimizing the ecological footprint. Bringing infotainment services to the vehicular environment requires to comply with standard protocols and mechanisms that allow heterogeneous networks to be interconnected in the Internet. There are three main functionalities that have to be provided: i) address autoconfiguration, ii) efficient routing and iii) mobility management. Regarding infotainment services, this thesis proposes mechanisms tackling the abovenamed aspects: an overhearing technique to improve an already standardized address autoconfiguration protocol; a tree-based routing algorithm especially tailored for vehicleto- Internet communications and an optimized mobility management approach for vehicular environments. Regarding traffic efficiency, this thesis proposes two algorithms that make use of vehicular communication techniques to monitor and forecast short-term traffic conditions. We first improved our knowledge on drivers’ behavior by analyzing real vehicular data traces, and proposes a mixture model for the vehicles interarrival time. This outcome was used for validating the proposed infotainment optimization as well. All the algorithms and analytical models described in this thesis have been validated by simulations and/or implementations using standard hardware. ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------El diseño, normalización y futuro despliegue de los sistemas de comunicación vehiculares han sido principalmente impulsados hasta el momento por las aplicaciones de seguridad vial. Hay dos aspectos adicionales de las redes vehiculares que han visto crecer su relevancia en los últimos años: los servicios de Infotainment y los de eficiencia del tráfico. Estos servicios pueden mejorar la experiencia de los conductores y hacer que los sistemas de comunicación vehiculares resulten más atractivos para los usuarios finales. En esta tesis, se proponen mecanismos de optimización para ambos tipos de servicios vehiculares. Los servicios de Infotainment están relacionados con la provisión de las clásicas aplicaciones IP tales como, navegar, acceder al correo electrónico, o a las redes sociales. Los servicios de eficiencia de tráfico permiten añadir nuevas funcionalidades a los sistemas de navegación con los objetivos de: optimizar el uso de las infraestructuras viarias, reducir los tiempos de viaje y consecuentemente, minimizar el impacto ambiental. Acceder a los servicios de Infotainment desde redes vehiculares conlleva cumplir con los protocolos y mecanismos estandarizados que permiten la interconexión de redes heterogéneas a Internet. Hay tres funcionalidades principales que tienen que ser proporcionadas: configuración automática de direcciones, encaminamiento eficaz y gestión de la movilidad. Esta tesis propone mecanismos para hacer frente a los tres aspectos mencionados: una técnica basada en overhearing que mejora un protocolo de configuración automática de direcciones ya estandarizado, un algoritmo de encaminamiento basado en árboles especialmente diseñado para las comunicaciones desde el vehículo a Internet y, un algoritmo de gestión de la movilidad optimizado para entornos vehiculares. En cuanto a los servicios de eficiencia de tráfico, esta tesis propone dos algoritmos que utilizando las técnicas de comunicación vehículo a vehículo permiten monitorizar y pronosticar a corto plazo las condiciones en el tráfico, como es el caso de posibles atascos. Todos los algoritmos y modelos analíticos descritos en esta tesis han sido validados a través de simulaciones y/o implementaciones usando hardware estándar

Trading accuracy for privacy in machine learning tasks: an empirical analysis

Proceeding of: IEEE Conference on Communications and Network Security (CNS 2021), 4-6 Oct. 2021, Tempe, AZ, USA (Virtual conference)Different kinds of user-generated data are increasingly used to tailor and optimize, through Machine Learning, the operation of online services and infrastructures. This typically requires sharing data among different partners, often including private data of individuals or business confidential data. While this poses privacy issues, the current state-of-the-art solutions either impose strong assumptions on the usage scenario or drastically reduce the data quality. In this paper, we evaluate through a generic framework the trade-offs between the accuracy of Machine Learning tasks and the achieved privacy (measured as similarity) on the input data, discussing trends and ways forward.The work of University Carlos III of Madrid was supported by the H2020 5G-TOURS project (grant no. 856950)

Off-line incentive mechanism for long-term P2P backup storage

This paper presents a micro-payment-based incentive mechanism for long-term peer-to-peer storage systems. The main novelty of the proposed incentive mechanism is to allow users to be off-line for extended periods of time without updating or renewing their information by themselves. This feature is enabled through a digital cheque, issued by the user, which is later employed by the peers to get a gratification for storing the user's information when the user is off-line. The proposed P2P backup system also includes a secure and lightweight data verification mechanism. Moreover, the proposed incentive also contributes to improve the availability of the stored information and the scalability of the whole system. The paper details the verification and cheque-based incentive mechanisms in the context of a P2P backup service and analyzes its scalability and security properties. The system is furthermore validated by means of simulation, proving the effectiveness of the proposed incentive.This work has been funded by the Regional Government of Madrid under the MEDIANET project (S2009/TIC-1468) and has also received funding from the Ministry of Science and Innovation of Spain, under the QUARTET project (TIN2009-13992-C02-01).Publicad

Network intelligence in 6G: challenges and opportunities

Proceeding of: the 16th ACM Workshop on Mobility in the Evolving Internet Architecture (in conjunction with MobiCom 2021: The 27th Annual International Conference On Mobile Computing And Networking, January 31-February 04, 2022, New Orleans, United States)The success of the upcoming 6G systems will largely depend on the quality of the Network Intelligence (NI) that will fully automate network management. Artificial Intelligence (AI) models are commonly regarded as the cornerstone for NI design, as they have proven extremely successful at solving hard problems that require inferring complex relationships from entangled, massive (network traffic) data. However, the common approach of plugging "vanilla" AI models into controllers and orchestrators does not fulfil the potential of the technology. Instead, AI models should be tailored to the specific network level and respond to the specific needs of network functions, eventually coordinated by an end-to-end NI-native architecture for 6G. In this paper, we discuss these challenges and provide results for a candidate NI-driven functionality that is properly integrated into the proposed architecture: network capacity forecasting.The authors of this paper have received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101017109 (DAEMON Network intelligence for aDAptive and sElf-Learning MObile Networks). This paper is also funded by the Spanish State Research Agency (TRUE5G project, PID2019-108713RB-C52PID2019-108713RB-C52 /AEI / 10.13039/501100011033)Publicad

Are crowd-sourced CTI datasets ready for supporting anti-cybercrime intelligence?

Cyber crimes rapidly increased over the past years, with attackers performing large-scale activities, using sophisticated and complex tactics and techniques, that have targeted governments, companies, and even strategic infrastructures. To tackle these attacks, the cyber-security community usually shares Cyber Threat Intelligence (CTI) that includes the collected Indicators of Compromise (IoC) using several open or private sharing platforms. In this paper, we study the informativeness and relevance of the IoCs related to cyber crimes following a major real-world event such as the war in Ukraine, which started in February 2022. To this end, we analyze different kinds of attacks available in a crowd-sourced dataset of Cyber Threat Intelligence (CTI) reports. Our analysis shows that while this data is able to capture major trends such as the ones following major events, the degree of miscellaneous information inside the reports makes it difficult to discern the association of a specific trace unequivocally.The work of UC3M has been supported by the Spanish Ministry of Economic Affairs and Digital Transformation and the European UnionNextGenerationEU through the UNICO 5G I+D project 6G-RIEMANN. The work of NEC Laboratories Europe has been supported by the EU research projects MARSAL (Grant Agreement 101017171) and DESIRE6G (Grant Agreement 101096466)Publicad