Marinella Petrocchi

CNL4DSA: a controlled natural language for data sharing agreements

A Data Sharing Agreement (DSA) is an agreement among contracting parties regulating how they share data. A DSA represents a flexible mean to assure privacy of data exchanged on the Web. As an example, a set of intelligent user agents may interact with each other, and by means of DSA, may negotiate privacy requirements on behalf of human users. However, a key factor for the adoption of privacys technologies is not only their reliability, but also their usability. Here, we propose CNL4DSA, a Controlled Natural Language for DSA aiming at lowering the barrier to adoption of DSA, and, at the same time, ensuring mapping to formal languages that enable the automatic verification of agreements.

Combining declarative and procedural views in the specification and analysis of product families

We introduce the feature-oriented language FLan as a proof of concept for specifying both declarative aspects of product families, namely constraints on their features, and procedural aspects, namely feature configuration and run-time behaviour. FLan is inspired by the concurrent constraint programming paradigm. A store of constraints allows one to specify in a declarative way all common constraints on features, including inter-feature constraints. A standard yet rich set of process-algebraic operators allows one to specify in a procedural way the configuration and behaviour of products. There is a close interaction between both views: (i) the execution of a process is constrained by its store to forbid undesired configurations; (ii) a process can query a store to resolve design and behavioural choices; (iii) a process can update the store by adding new features. An implementation in the Maude framework allows for a variety of formal automated analyses of product families specified in FLan, ranging from consistency checking to model checking.

Fine grained access control with trust and reputation management for globus

We propose an integrated architecture, extending a framework for fine grained access control of Grid computational services, with an inference engine managing reputation and trust management credentials. Also, we present the implementation of the proposed architecture, with preliminary performance figures.

Fame for sale

Fake followers are those Twitter accounts specifically created to inflate the number of followers of a target account. Fake followers are dangerous for the social platform and beyond, since they may alter concepts like popularity and influence in the Twittersphere-hence impacting on economy, politics, and society. In this paper, we contribute along different dimensions. First, we review some of the most relevant existing features and rules (proposed by Academia and Media) for anomalous Twitter accounts detection. Second, we create a baseline dataset of verified human and fake follower accounts. Such baseline dataset is publicly available to the scientific community. Then, we exploit the baseline dataset to train a set of machine-learning classifiers built over the reviewed rules and features. Our results show that most of the rules proposed by Media provide unsatisfactory performance in revealing fake followers, while features proposed in the past by Academia for spam detection provide good results. Building on the most promising features, we revise the classifiers both in terms of reduction of overfitting and cost for gathering the data needed to compute the features. The final result is a novel Class A classifier, general enough to thwart overfitting, lightweight thanks to the usage of the less costly features, and still able to correctly classify more than 95% of the accounts of the original training set. We ultimately perform an information fusion-based sensitivity analysis, to assess the global sensitivity of each of the features employed by the classifier.The findings reported in this paper, other than being supported by a thorough experimental methodology and interesting on their own, also pave the way for further investigation on the novel issue of fake Twitter followers.

Prioritized Execution of Privacy Policies

This paper addresses the issue of solving conflicts occurring in the authorization decision process among policies applicable to an access request. We propose a strategy for conflict resolution based on the evaluation of the specificity level of the elements constituting the policies. Operatively, the strategy is implemented by exploiting a well known decision making technique. Two practical examples of use in the healthcare scenario are given.

A prototype for solving conflicts in XACML-based e-Health policies

The Electronic Patient Record (EPR) enables the sharing of medical documents among several and widespread healthcare organizations. To guarantee privacy properties, access control policies should be defined, regulating how the documents can be shared. Conflicts may occur among policies applicable to the same access request. We present a running prototype, based on an XACML engine, that implements a conflict resolution strategy as an extension to the standard combining algorithms of the XACML engine. We evaluate the efficiency of our proposal in terms of execution time, on a variable number of conflicting rules.

A design phase for data sharing agreements

The number of factories, service providers, retailers, and final users that create networks and establish collaborations for increasing their productivity and competitiveness is constantly growing, especially by effect of the globalization and outsourcing of industrial activities. This trend introduces new complexities in the value supply chain, not last the need for secure and private data sharing among the collaborating parties. A Data Sharing Agreement (DSA) represents a flexible means to assure privacy and security of electronic data exchange. DSA is a formal document regulating data exchange in a controlled manner, by defining a set of policies specifying what parties are allowed, or required, or denied to do with respect to data covered by the agreement. A key factor in the adoption of DSAs is their usability. Here, we propose an approach for a consistent and automated design phase of the agreements. In particular, we present an authoring tool for a user-friendly and cooperative editing of DSA and an analysis tool to identify possible conflicts or incompatibilities among the DSA policies.

The Paradigm-Shift of Social Spambots: Evidence, Theories, and Tools for the Arms Race

Recent studies in social media spam and automation provide anecdotal argumentation of the rise of a new generation of spambots, so-called social spambots. Here, for the first time, we extensively study this novel phenomenon on Twitter and we provide quantitative evidence that a paradigm-shift exists in spambot design. First, we measure current Twitters capabilities of detecting the new social spambots. Later, we assess the human performance in discriminating between genuine accounts, social spambots, and traditional spambots. Then, we benchmark several state-of-the-art techniques proposed by the academic literature. Results show that neither Twitter, nor humans, nor cutting-edge applications are currently capable of accurately detecting the new social spambots. Our results call for new approaches capable of turning the tide in the fight against this raising phenomenon. We conclude by reviewing the latest literature on spambots detection and we highlight an emerging common research trend based on the analysis of collective behaviors. Insights derived from both our extensive experimental campaign and survey shed light on the most promising directions of research and lay the foundations for the arms race against the novel social spambots. Finally, to foster research on this novel phenomenon, we make publicly available to the scientific community all the datasets used in this study.

DNA-Inspired Online Behavioral Modeling and Its Application to Spambot Detection

A novel, simple, and effective approach to modeling online user behavior extracts and analyzes digital DNA sequences from user online actions and uses Twitter as a benchmark to test the proposal. Specifically, the model obtains an incisive and compact DNA-inspired characterization of user actions. Then, standard DNA analysis techniques discriminate between genuine and spambot accounts on Twitter. An experimental campaign supports the proposal, showing its effectiveness and viability. Although Twitter spambot detection is a specific use case on a specific social media platform, the proposed methodology is platform and technology agnostic, paving the way for diverse behavioral characterization tasks.

Mobile Implementation and Formal Verification of an e-Voting System

We propose a mobile implementation of an e-voting protocol. We also provide a formal analysis to validate a security property of our system.