Stylianos Gisdakis

SPPEAR: security & privacy-preserving architecture for participatory-sensing applications

Recent advances in sensing, computing, and networking have paved the way for the emerging paradigm of participatory sensing (PS). The openness of such systems and the richness of user data they entail raise significant concerns for their security, privacy and resilience. Prior works addressed different aspects of the problem. But in order to reap the benefits of this new sensing paradigm, we need a comprehensive solution. That is, a secure and accountable PS system that preserves user privacy, and enables the provision of incentives to the participants. At the same time, we are after a PS system that is resilient to abusive users and guarantees privacy protection even against multiple misbehaving PS entities (servers). We address these seemingly contradicting requirements with our SPPEAR architecture. Our full blown implementation and experimental evaluation demonstrate that SPPEAR is efficient, practical, and scalable. Last but not least, we formally assess the achieved security and privacy properties. Overall, our system is a comprehensive solution that significantly extends the state-of-the-art and can catalyze the deployment of PS applications.

VeSPA: vehicular security and privacy-preserving architecture

Vehicular Communications (VC) are reaching a near deployment phase and will play an important role in improving road safety, driving efficiency and comfort. The industry and the academia have reached a consensus for the need of a Public Key Infrastructure (PKI), in order to achieve security, identity management, vehicle authentication, as well as preserve vehicle privacy. Moreover, a gamut of proprietary and safety applications, such as location-based services and pay-as-you-drive systems, are going to be offered to the vehicles. The emerging applications are posing new challenges for the existing Vehicular Public Key Infrastructure (VPKI) architectures to support Authentication, Authorization and Accountability (AAA), without exposing vehicle privacy. In this work we present an implementation of a VPKI that is compatible with the VC standards. We propose the use of tickets as cryptographic tokens to provide AAA and also preserve vehicle privacy against adversaries and the VPKI. Finally, we present the efficiency results of our implementation to prove its applicability.

Secure and Privacy-Preserving Smartphone-Based Traffic Information Systems

Increasing smartphone penetration, combined with the wide coverage of cellular infrastructures, renders smartphone-based traffic information systems (TISs) an attractive option. The main purpose of such systems is to alleviate traffic congestion that exists in every major city. Nevertheless, to reap the benefits of smartphone-based TISs, we need to ensure their security and privacy and their effectiveness (e.g., accuracy). This is the motivation of this paper: We leverage state-of-the-art cryptographic schemes and readily available telecommunication infrastructure. We present a comprehensive solution for smartphone-based traffic estimation that is proven to be secure and privacy preserving. We provide a full-blown implementation on actual smartphones, along with an extensive assessment of its accuracy and efficiency. Our results confirm that smartphone-based TISs can offer accurate traffic state estimation while being secure and privacy preserving.

Trustworthy People-Centric Sensing: Privacy, security and user incentives road-map

The broad capabilities of widespread mobile devices have paved the way for People-Centric Sensing (PCS). This emerging paradigm enables direct user involvement in possibly large-scale and diverse data collection and sharing. Unavoidably, this raises significant privacy concerns, as participants may inadvertently reveal a great deal of sensitive information. However, ensuring user privacy, e.g., by anonymizing data they contribute, may cloak faulty (possibly malicious) actions. Thus, PCS systems must not only be privacy-preserving but also accountable and reliable. As an increasing number of applications (e.g., assistive healthcare and public safety systems) can significantly benefit from people-centric sensing, it becomes imperative to meet these seemingly contradicting requirements. In this work, we discuss security, user privacy and incentivization for this sensing paradigm, exploring how to address all aspects of this multifaceted problem. We critically survey the security and privacy properties of state-of-the-art research efforts in the area. Based on our findings, we posit open issues and challenges, and discuss possible ways to address them, so that security and privacy do not hinder the deployment of PCS systems.

SEROSA: SERvice oriented security architecture for Vehicular Communications

Modern vehicles are no longer mere mechanical devices; they comprise dozens of digital computing platforms, coordinated by an in-vehicle network, and have the potential to significantly enhance the digital life of individuals on the road. While this transformation has driven major advancements in road safety and transportation efficiency, significant work remains to be done to support the security and privacy requirements of the envisioned ecosystem of commercial services and applications (i.e., Internet access, video streaming, etc.). In the era when “service is everything and everything is a service”, Vehicular Communication (VC) systems cannot escape from this ongoing trend towards multi-service environments accessible from anywhere. To meet the diverse requirements of vehicle operators and Service Providers (SPs), we present SEROSA, a service-oriented security and privacy-preserving architecture for VC. By synthesizing existing VC standards and Web Services (WS), our architecture provides comprehensive identity and service management while ensuring interoperability with existing SPs. We fully implement our system and extensively assess its efficiency, practicality, and dependability. Overall, SEROSA significantly extends the state of the art and serves as a catalyst for the integration of vehicles into the vast domain of Internet-based services.

Security, Privacy, and Incentive Provision for Mobile Crowd Sensing Systems

Recent advances in sensing, computing, and networking have paved the way for the emerging paradigm of mobile crowd sensing (MCS). The openness of such systems and the richness of data MCS users are expected to contribute to them raise significant concerns for their security, privacy-preservation and resilience. Prior works addressed different aspects of the problem. But in order to reap the benefits of this new sensing paradigm, we need a holistic solution. That is, a secure and accountable MCS system that preserves user privacy, and enables the provision of incentives to the participants. At the same time, we are after an MCS architecture that is resilient to abusive users and guarantees privacy protection even against multiple misbehaving and intelligent MCS entities (servers). In this paper, we meet these challenges and propose a comprehensive security and privacy-preserving architecture. With a full blown implementation, on real mobile devices, and experimental evaluation we demonstrate our systems efficiency, practicality, and scalability. Last but not least, we formally assess the achieved security and privacy properties. Overall, our system offers strong security and privacy-preservation guarantees, thus, facilitating the deployment of trustworthy MCS applications.

Towards a secure and privacy-preserving multi-service vehicular architecture

Intensive efforts in industry, academia and standardization bodies have brought vehicular communications (VC) one step before commercial deployment. In fact, future vehicles will become significant mobile platforms, extending the digital life of individuals with an ecosystem of applications and services. To secure these services and to protect the privacy of individuals, it is necessary to revisit and extend the vehicular Public Key Infrastructure (PKI)-based approach towards a multi-service security architecture. This is exactly what this work does, providing a design and a proof-of-concept implementation. Our approach, inspired by long-standing standards, is instantiated for a specific service, the provision of short-term credentials (pseudonyms). Moreover, we elaborate on its operation across multiple VC system domains, and craft a roadmap for further developments and extensions that leverage Web-based approaches. Our current results already indicate our architecture is efficient and can scale, and thus can meet the needs of the foreseen broad gamut of applications and services, including the transportation and safety ones.

On the optimal allocation of adversarial resources

Security is important for mission-critical wireless sensor networks (WSNs). This is especially so because powerful adversaries could compromise and control a significant fraction of the network nodes. A plethora of schemes has been developed to secure wireless sensor networks and resilience to sophisticated attacks has been analyzed. However, the question of how the adversary could deploy her resources to maximally affect the attacked system has remained largely unaddressed. This is the problem this paper is concerned with: Given a number of compromised entities (nodes) and cryptographic keys, how can the adversary devise a close-to-optimal attack tactic? To the best of our knowledge, this is the first investigation of its kind: while the basic adversarial behavior is well-known, the problem of how the adversary can optimally deploy her resources to maximize the attack impact has not been considered for WSNs. We consider an abstract model of the mission-critical WSN and the adversary, and we find that the determination of an optimal attack is computationally hard, thus, we devise an efficient heuristic approach. An intelligent adversarial resource allocation indeed yields disproportional gains for the attacker. Our analysis is the first necessary step to comprehend how to best address vulnerabilities.