Owners of data may wish to share some statistics with others, but they may be worried of privacy of the underlying data. An effective solution to this problem is to employ provable privacy techniques, such as differential privacy, to add noise to the statistics before releasing them. This protection lowers the risk of sharing sensitive data with more or less trusted data sharing partners. Unfortunately, applying differential privacy in its mathematical form requires one to fix certain numeric parameters, which involves subtle computations and expert knowledge that the data owners may lack.In this paper, we first describe a differential privacy parameter selection procedure that minimizes what lay data owners need to know. Second, we describe a user visualization and workflow that makes this procedure available for lay data owners by helping them set the level of noise appropriately to achieve a tolerable risk level. Finally, we describe a user study in which human factors professionals who were native to differential privacy were briefly trained on the concept of using differential privacy for data sharing and then used the visualization to determine an appropriate level of noise.

The growing interest in the Internet of Things (IoT) has resulted in a number of wide-area deployments of IoT subnetworks, where multiple heterogeneous wireless communication solutions coexist: from multiple access technologies such as cellular, WiFi, ZigBee, and Bluetooth, to multi-hop ad-hoc and MANET routing protocols, they all must be effectively integrated to create a seamless communication platform. Managing these open, geographically distributed, and heterogeneous networking infrastructures, especially in dynamic environments, is a key technical challenge. In order to take full advantage of the many opportunities they provide, techniques to concurrently provision the different classes of IoT traffic across a common set of sensors and networking resources must be designed. In this paper, we will design a software-defined approach for the IoT environment to dynamically achieve differentiated quality levels to different IoT tasks in very heterogeneous wireless networking scenarios. For this, we extend the Multinetwork INformation Architecture (MINA), a reflective (self-observing and adapting via an embodied Observe-Analyze-Adapt loop) middleware with a layered IoT SDN controller. The developed IoT SDN controller originally i) incorporates and supports commands to differentiate flow scheduling over task-level, multi-hop, and heterogeneous ad-hoc paths and ii) exploits Network Calculus and Genetic Algorithms to optimize the usage of currently available IoT network opportunities. We have applied the extended MINA SDN prototype in the challenging IoT scenario of wide-scale integration of electric vehicles, electric charging sites, smart grid infrastructures, and a wide set of pilot users, as targeted by the Artemis Internet of Energy and Arrowhead projects. Preliminary simulation performance results indicate that our approach and the extended MINA system can support efficient exploitation of the IoT multinetwork capabilities.

The networking landscape of today is characterized by diverse access technologies including cellular, WiFi, Ethernet, MANETs, and ZigBee, and properly managing this heterogeneous networking infrastructure is a key challenge to take full advantage of its many opportunities. In this paper, we propose MINA (Multinetwork INformation Architecture), a reflective (self-observing and adapting) middleware approach to realize and manage dynamic and heterogeneous multi-networks in pervasive environments. A novel aspect of MINA is that it embodies an Observe-Analyze-Adapt (OAA) loop to i) achieve a reasonably accurate, centralized global view of the multi-network through the design of novel techniques for overlay structuring, network state collection and formal methods-based analysis, and ii) take advantage of the global view for adapting multi-network structure by reallocating application flows across networks and proactively planning and deploying additional network resources.

We present a methodology for the probabilistic modeling of failure dependencies in large, complex systems using Markov Logic Networks (MLNs), a state-of-the-art probabilistic relational modeling technique in machine learning. We illustrate this modeling methodology on example system architectures, and show how the the Probabilistic Consistency Engine (PCE) tool can create and analyze failure-dependency models. We compare MLN-based analysis with analytical symbolic analysis to validate our approach. The latter method yields bounds on the expected system behaviors for different component-failure probabilities, but it requires closed-form representations and is therefore often an impractical approach for complex system analysis. The MLN-based method facilitates techniques of early design analysis for reliability (e.g., probabilistic sensitivity analysis). We analyze two examples - a portion of the Time-Triggered Ethernet (TTEthernet) communication platform used in space, and an architecture based on Honeywell's Cabin Air Compressor(CAC) - that highlight the value of the MLN-based approach for analyzing failure dependencies in complex cyber-physical systems.

For purposes of security analysis, cryptographic protocols can be translated from a high-level message-list language such as CAPSL into a multiset rewriting (MSR) rule language such as CIL. The natural translation creates two rules per message or computational action. We show how to optimize the natural rule set by about 50% into a form similar to the result of hand encoding, and prove that the transformation is sound because it is attack-preserving, and unique because it is terminating and confluent. The optimization has been implemented in Java.

Techniques for the development of reliable information systems on the basis of their formal specification are the main concern in the project. Our work focuses on the specification language TROLL light which allows one to describe the part of the world to be modeled as a community of concurrently existing and communicating objects. Our specification language comes with an integrated, open development environment. The task of this environment is to give support for the creation of correct information systems. Two important ingredients of the environment are the animator and the proof support system.>

Within the compound project KorSo our team is concerned with the research on techniques and methods for the development of reliable information systems on the basis of formal specifications. Our work focuses on the specification language TROLL light which allows to describe the part of the world which is to be modeled as a community of concurrently existing and communicating objects by determining their structure as well as their behavior. Moreover we develop and implement a computer aided specification environment for TROLL light which permits a prototyping animation as well as the proof of properties of specifications. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.