Abstract
We present Dolphin, an extensible programming language for autonomous vehicle networks. A Dolphin program expresses an orchestrated execution of tasks defined compositionally for multiple vehicles. Building upon the base case of elementary one-vehicle tasks, the built-in operators include support for composing tasks in several forms, for instance according to concurrent, sequential, or event-based task flow. The language is implemented as a Groovy DSL, facilitating extension and integration with external software packages, in particular robotic toolkits. The paper describes the Dolphin language, its integration with an open-source toolchain for autonomous vehicles, and results from field tests using unmanned underwater vehicles (UUVs) and unmanned aerial vehicles (UAVs).

Abstract
Some governments do not consider metadata as personal data, and so not in the scope of privacy regulations. However, often, metadata gives more relevant information than the actual content itself. Metadata can be very useful to identify, locate, understand and manage personal data, i.e., information that is eminently private in nature and under most privacy regulation should be anonymized or deleted if users have not give their consent. In voice calls, we are facing a critical situation in terms of privacy, as metadata can identify who calls to whom and the duration of the call, for example. In this work, we investigate privacy properties of voice calls metadata, in particular when using secure VoIP, giving evidence of the ability to extract sensitive information from its ("secure") metadata. We find that ZRTP metadata is freely available to any client on the network, and that users can be re-identified by any user with access to the network. Also, we propose a solution for this problem, suitable for all the ZRTP-based implementations.

Abstract
Cloud storage allows users to remotely store their data, giving access anywhere and to anyone with an Internet connection. The accessibility, lack of local data maintenance and absence of local storage hardware are the main advantages of this type of storage. The adoption of this type of storage is being driven by its accessibility. However, one of the main barriers to its widespread adoption is the sovereignty issues originated by lack of trust in storing private and sensitive information in such a medium. Recent attacks to cloud-based storage show that current solutions do not provide adequate levels of security and subsequently fail to protect users' privacy. Usually, users rely solely on the security supplied by the storage providers, which in the presence of a security breach will ultimate lead to data leakage. In this paper, we propose and implement a broker (ARGUS) that acts as a proxy to the existing public cloud infrastructures by performing all the necessary authentication, cryptography and erasure coding. ARGUS uses erasure code as a way to provide efficient redundancy (opposite to standard replication) while adding an extra layer to data protection in which data is broken into fragments, expanded and encoded with redundant data pieces that are stored across a set of different storage providers (public or private). The key characteristics of ARGUS are confidentiality, integrity and availability of data stored in public cloud systems.

Abstract
The increasing capabilities of smartphones is paving way to novel applications through the crowd-sourcing of these untapped resources, to form hyperlocal meshes commonly known as edge-clouds. While a relevant body-of-work is already available for the underlying networking, computing and storage facilities, security and privacy remain second class citizens. In this paper we present Panoptic, an edge-cloud system that enables the search for missing people, similar to the commonly known Amber alert system, in high density scenarios where wireless infrastructure might be limited (WiFi and LTE), e.g. concerts, while featuring privacy and security by design. Since the limited resources present in the mobile devices, namely battery capacity, Panoptic offers a computing offloading that tries to minimize data leakage while offering acceptable levels of performance. Our results show that it is achievable to run these algorithms in an edge-cloud configuration and that it is beneficial to use this architecture to lower data transfer through the wireless infrastructure while enforcing privacy. Results from our experimental evaluation show that the security layer does not impose a significant overhead, and only accounts for 2% of the total execution time for an edge cloud comprised by, but not limited to, 8 devices.

Abstract
This paper presents a testbed implementation and evaluation of coding for secrecy schemes in a real environment through software defined radio platforms. These coding schemes rely on interleaving and scrambling with randomly generated keys to shuffle information before transmission. These keys are then encoded jointly with data and then hidden (erased) before transmission, thus only being retrievable through parity information resulting from encoded data. An advantage of the legitimate receiver (e.g. a better signal-to-noise ratio) on the reception of those keys provides the means to achieve secrecy against an adversary eavesdropper. Through this testbed implementation, we show the practical feasibility of coding for secrecy schemes in real-world environments, unveiling the usefulness of interleaving and scrambling with a hidden key to reduce the required advantage over an eavesdropper. We further describe and present solutions to a set of issues that appear when doing practical implementations of security schemes in software defined radio platforms. © 2018 IEEE.

Abstract
Sharing location data is becoming more popular as mobile devices become ubiquitous. Location-based service providers use this type of data to provide geographically contextualized services to their users. However, sharing exact locations with possibly untrustworthy entities poses a thread to privacy. Geo-indistinguishability has been recently proposed as a formal notion based on the concept of differential privacy to design location privacy-preserving mechanisms in the context of sporadic release of location data. While adaptations for the case of continuous location updates have been proposed, the study on how the frequency of updates impacts the privacy and utility level is yet to be made. In this paper we address this issue, by analyzing the effect of frequency updates on the privacy and utility levels of four mechanisms: the standard planar Laplacian mechanism suitable for sparse locations, and three variants of an adaptive mechanism that is an adaptation of the standard mechanism for continuous location updates. Results show that the frequency of updates largely impacts the correlation between points. As the frequency of updates decreases, the correlation also decreases. The adaptive mechanism is able to adjust the privacy and utility levels accordingly to the correlation between past positions and current position. However, the estimator function that is used to predict the current location has a great influence in the obtained results.