Carmen Fernandez-Gago

Trust management systems for wireless sensor networks: Best practices

Wireless sensor networks (WSNs) have been proven a useful technology for perceiving information about the physical world and as a consequence has been used in many applications such as measurement of temperature, radiation, flow of liquids, etc. The nature of this kind of technology, and also their vulnerabilities to attacks make the security tools required for them to be considered in a special way. The decision making in a WSN is essential for carrying out certain tasks as it aids sensors establish collaborations. In order to assist this process, trust management systems could play a relevant role. In this paper, we list the best practices that we consider are essential for developing a good trust management system for WSN and make an analysis of the state of the art related to these practices.

A Conceptual Framework for Trust Models

During the last twenty years, a huge amount of trust and reputation models have been proposed, each of them with their own particularities and targeting different domains. While much effort has been made in defining ever-increasing complex models, little attention has been paid to abstract away the particularities of these models into a common set of easily understandable concepts. We propose a conceptual framework for computational trust models that will be used for analyzing their features and for comparing heterogeneous and relevant trust models.

A Model for Trust Metrics Analysis

Trust is an important factor in any kind of network essential, for example, in the decision-making process. As important as the definition of trust is the way to compute it. In this paper we propose a model for defining trust based on graph theory and show examples of some simple operators and functions that will allow us to compute trust.

A Metamodel for Measuring Accountability Attributes in the Cloud

Cloud governance, and in particular data governance in the cloud, relies on different technical and organizational practices and procedures, such as policy enforcement, risk management, incident management and remediation. The concept of accountability encompasses such practices, and is essential for enhancing security and trustworthiness in the cloud. Besides this, proper measurement of cloud services, both at a technical and governance level, is a distinctive aspect of the cloud computing model. Hence, a natural problem that arises is how to measure the impact on accountability of the procedures held in practice by organizations that participate in the cloud ecosystem. In this paper, we describe a metamodel for addressing the problem of measuring accountability properties for cloud computing, as discussed and defined by the Cloud Accountability Project (A4Cloud). The goal of this metamodel is to act as a language for describing: (i) accountability properties in terms of actions between entities, and (ii) metrics for measuring the fulfillment of such properties. It also allows the recursive decomposition of properties and metrics, from a high-level and abstract world to a tangible and measurable one. Finally, we illustrate our proposal of the metamodel by modelling the transparency property, and define some metrics for it.

An Early Warning System Based on Reputation for Energy Control Systems

Most of energy control or Supervisory Control and Data Acquisition (SCADA) systems are very dependent on advanced technologies and on traditional security mechanisms for protecting the a system against anomalous events. Security mechanisms are not enough to be used in critical systems, since they can only detect anomalous events occurring at a certain moment in time. For this reason it becomes of paramount importance the usage of intelligent systems with capability for preventing anomalous situations and reacting against them on time. This type of systems are, for example, early warning systems (EWS). In this paper, we propose an EWS based on Wireless Sensor Networks (WSNs) (under the ISA100.11a standard) and reputation for controling network behavior. The WSN are organized into clusters where a cluster head (CH) is designated. This CH will contain a Reputation Manager Module. The usability of this approach is also analyzed considering a smart grid scenario.

A framework for enabling trust requirements in social cloud applications

Cloud applications entail the provision of a huge amount of heterogeneous, geographically distributed resources managed and shared by many different stakeholders who often do not know each other beforehand. This raises numerous security concerns that, if not addressed carefully, might hinder the adoption of this promising computational model. Appropriately dealing with these threats gains special relevance in the social cloud context, where computational resources are provided by the users themselves. We argue that taking trust and reputation requirements into account can leverage security in these scenarios by incorporating the notions of trust relationships and reputation into them. For this reason, we propose a development framework onto which developers can implement trust-aware social cloud applications. Developers can also adapt the framework in order to accommodate their application-specific needs.

Building trust from context similarity measures

Trust is an essential feature of any system where entities have to collaborate. Trust can assist entities making decisions before establishing collaborations. It is desirable to simulate the behaviour of users as in social environments where they tend to trust users who have common interests or share some of their opinions, i.e., users similar to them. In this paper, we introduce the concept of context similarity among entities and derive a similarity network. Then, we define a trust model that allows us to establish trust along a path of entities. We validate our model in a proximity-based trust establishment scenario.

Trust Management VII

More and more personal information is exchanged on-line using communication protocols. This makes it increasingly important that such protocols satisfy privacy by data minimisation. Formal methods have been used to verify privacy properties of protocols; but so far, mostly in an ad-hoc way. In previous work, we provided general definitions for the fundamental privacy concepts of linkability and detectability. However, this approach is only able to verify privacy properties for given protocol instances. In this work, by generalising the approach, we formally analyse privacy of communication protocols independently from any instance. We implement the model; identify its assumptions by relating it to the instantiated model; and show how to visualise results. To demonstrate our approach, we analyse privacy in Identity Mixer.

A scale based trust model for multi-context environments

When interactions among users of a system have to take place, for example, over the internet, establishing trust relationships among these users becomes crucial. However, the way this trust is established depends to a certain extent on the context where the interactions take place. Most of the time, trust is encoded as a numerical value that might not be very meaningful for a not very experienced user. In this paper we propose a model that takes into account the semantic and the computational sides of trust. This avoids users having to deal directly with the computational side; they instead deal with meaningful labels such as Bad or Good in a given context.

Adaptive Dispatching of Incidences Based on Reputation for SCADA Systems

SCADA systems represent a challenging scenario where the management of critical alarms is crucial. Their response to these alarms should be efficient and fast in order to mitigate or contain undesired effects. This work presents a mechanism, the Adaptive Assignment Manager (AAM) that will aid to react to incidences in a more efficient way by dynamically assigning alarms to the most suitable human operator. The mechanism uses various inputs for identifying the operators such as their availability, workload and reputation. In fact, we also define a reputation component that stores the reputation of the human operators and uses feedback from past experiences.