PAgIoT - Privacy-preserving aggregation protocol for internet of things

Modern society highly relies on the use of cyberspace to perform a huge variety of activities, such as social networking or e-commerce, and new technologies are continuously emerging. As such, computer systems may store a huge amount of information, which makes data analysis and storage a challenge. Information aggregation and correlation are two basic mechanisms to reduce the problem size, for example by filtering out redundant data or grouping similar one. These processes require high processing capabilities, and thus their application in Internet of Things (IoT) scenarios is not straightforward due to resource constraints. Furthermore, privacy issues may arise when the data at stake is personal. In this paper we propose PAgIoT, a Privacy-preserving Aggregation protocol suitable for IoT settings. It enables multi-attribute aggregation for groups of entities while allowing for privacy-preserving value correlation. Results show that PAgIoT is resistant to security attacks, it outperforms existing proposals that provide with the same security features, and it is feasible in resource-constrained devices and for aggregation of up to 10 attributes in big networks.This work was partially supported by the MINECO grant TIN2013-46469-R (SPINY: Security and Privacy in the Internet of You) and the CAM grant S2013/ICE-3095 CIBERDINE-CM (CIBERDINE: Cybersecurity, Data, and Risks) funded by the Autonomous Community of Madrid and co-funded by European funds

Security and Privacy for Modern Wireless Communication Systems

The aim of this reprint focuses on the latest protocol research, software/hardware development and implementation, and system architecture design in addressing emerging security and privacy issues for modern wireless communication networks. Relevant topics include, but are not limited to, the following: deep-learning-based security and privacy design; covert communications; information-theoretical foundations for advanced security and privacy techniques; lightweight cryptography for power constrained networks; physical layer key generation; prototypes and testbeds for security and privacy solutions; encryption and decryption algorithm for low-latency constrained networks; security protocols for modern wireless communication networks; network intrusion detection; physical layer design with security consideration; anonymity in data transmission; vulnerabilities in security and privacy in modern wireless communication networks; challenges of security and privacy in node–edge–cloud computation; security and privacy design for low-power wide-area IoT networks; security and privacy design for vehicle networks; security and privacy design for underwater communications networks

Telecommunications Networks

This book guides readers through the basics of rapidly emerging networks to more advanced concepts and future expectations of Telecommunications Networks. It identifies and examines the most pressing research issues in Telecommunications and it contains chapters written by leading researchers, academics and industry professionals. Telecommunications Networks - Current Status and Future Trends covers surveys of recent publications that investigate key areas of interest such as: IMS, eTOM, 3G/4G, optimization problems, modeling, simulation, quality of service, etc. This book, that is suitable for both PhD and master students, is organized into six sections: New Generation Networks, Quality of Services, Sensor Networks, Telecommunications, Traffic Engineering and Routing

Modeling, Predicting and Capturing Human Mobility

Realistic models of human mobility are critical for modern day applications, specifically for recommendation systems, resource planning and process optimization domains. Given the rapid proliferation of mobile devices equipped with Internet connectivity and GPS functionality today, aggregating large sums of individual geolocation data is feasible. The thesis focuses on methodologies to facilitate data-driven mobility modeling by drawing parallels between the inherent nature of mobility trajectories, statistical physics and information theory. On the applied side, the thesis contributions lie in leveraging the formulated mobility models to construct prediction workflows by adopting a privacy-by-design perspective. This enables end users to derive utility from location-based services while preserving their location privacy. Finally, the thesis presents several approaches to generate large-scale synthetic mobility datasets by applying machine learning approaches to facilitate experimental reproducibility

Recent Trends in Communication Networks

In recent years there has been many developments in communication technology. This has greatly enhanced the computing power of small handheld resource-constrained mobile devices. Different generations of communication technology have evolved. This had led to new research for communication of large volumes of data in different transmission media and the design of different communication protocols. Another direction of research concerns the secure and error-free communication between the sender and receiver despite the risk of the presence of an eavesdropper. For the communication requirement of a huge amount of multimedia streaming data, a lot of research has been carried out in the design of proper overlay networks. The book addresses new research techniques that have evolved to handle these challenges

Privacy-preserving spatiotemporal multicast for mobile information services

Mobile devices have become essential for accessing information services anywhere at any time. While the so-called geographic multicast (geocast) has been considered in detail in existing research, it only focuses on delivering messages to all mobile devices that are currently residing within a certain geographic area. This thesis extends this notion by introducing a Spatiotemporal Multicast (STM), which can informally be described as a "geocast into the past". Instead of addressing users based on their current locations, this concept relates to the challenge of sending a message to all devices that have resided within a geographic area at a certain time in the past. While a wide variety of applications can be envisioned for this concept, it presents several challenges to be solved. In order to deliver messages to all past visitors of a certain location, an STM service would have to fully track all user movements at all times. However, collecting this kind of information is not desirable considering the underlying privacy implications, i.e., users may not wish to be identified by the sender of a message as this can disclose sensitive personal information. Consequently, this thesis aims to provide a privacy-preserving notion of STM. In order to realize such a service, this work first presents a detailed overview of possible applications. Based on those, functional, non-functional, as well as security and privacy objectives are proposed. These objectives provide the foundation for an in-depth literature review of potential mechanisms for realizing an STM service. Among the suggested options, the most promising relies on Rendezvous Points (RPs) for datagram delivery. In simple terms, RPs represent "anonymous mailboxes" that are responsible for certain spatiotemporal regions. Messages are deposited at RPs so that users can retrieve them later on. Protecting the privacy of users then translates to obfuscating the responsibilities of RPs for specific spatiotemporal regions. This work proposes two realizations: CSTM, which relies on cryptographic hashing, and OSTM, which considers the use of order-preserving encryption in a CAN overlay. Both approaches are evaluated and compared in detail with respect to the given objectives. While OSTM yields superior performance-related properties, CSTM provides an increased ability of protecting the privacy of users.Mobilgeräte bilden heute die Grundlage allgegenwärtiger Informationsdienste. Während der sogenannte geografische Multicast (Geocast) hier bereits ausführlich erforscht worden ist, so bezieht sich dieser nur auf Geräte, welche sich aktuell innerhalb einer geografischen Zielregion befinden. Diese Arbeit erweitert dieses Konzept durch einen räumlich-zeitlichen Multicast, welcher sich informell als "Geocast in die Vergangenheit" beschreiben lässt. Dabei wird die Zustellung einer Nachricht an alle Nutzer betrachtet, die sich in der Vergangenheit an einem bestimmten Ort aufgehalten haben. Während eine Vielzahl von Anwendungen für dieses Konzept denkbar ist, so ergeben sich hier mehrere Herausforderungen. Um Nachrichten an ehemalige Besucher eines Ortes senden zu können, müsste ein räumlich-zeitlicher Multicast-Dienst die Bewegungen aller Nutzer vollständig erfassen. Aus Gründen des Datenschutzes ist das zentralisierte Sammeln solch sensibler personenbezogener Daten jedoch nicht wünschenswert. Diese Arbeit befasst sich daher insbesondere mit dem Schutz der Privatsphäre von Nutzern eines solchen Dienstes. Zur Entwicklung eines räumlich-zeitlichen Multicast-Dienstes erörtert diese Arbeit zunächst mögliche Anwendungen. Darauf aufbauend werden funktionale, nicht-funktionale, sowie Sicherheits- und Privatsphäre-relevante Anforderungen definiert. Diese bilden die Grundlage einer umfangreichen Literaturrecherche relevanter Realisierungstechniken. Der vielversprechendste Ansatz basiert hierbei auf der Hinterlegung von Nachrichten in sogenannten Rendezvous Points. Vereinfacht betrachtet stellen diese "anonyme Briefkästen" für bestimmte räumlich-zeitliche Regionen dar. Nachrichten werden in diesen so hinterlegt, dass legitime Empfänger sie dort später abholen können. Der Schutz der Nutzer-Privatsphäre entspricht dann der Verschleierung der Zuständigkeiten von Rendezvous Points für verschiedene räumlich-zeitliche Regionen. Diese Arbeit schlägt zwei Ansätze vor: CSTM, welches kryptografische Hashfunktionen nutzt, sowie OSTM, welches ordnungserhaltende Verschlüsselung in einem CAN Overlay einsetzt. Beide Optionen werden detailliert analytisch sowie empirisch bezüglich ihrer Diensteigenschaften untersucht und verglichen. Dabei zeigt sich, dass OSTM vorteilhaftere Leistungseigenschaften besitzt, während CSTM einen besseren Schutz der Nutzer-Privatsphäre bietet