CRNov 20, 2018
Killing the Password and Preserving Privacy with Device-Centric and Attribute-based AuthenticationKostantinos Papadamou, Savvas Zannettou, Bogdan Chifor et al.
Current authentication methods on the Web have serious weaknesses. First, services heavily rely on the traditional password paradigm, which diminishes the end-users' security and usability. Second, the lack of attribute-based authentication does not allow anonymity-preserving access to services. Third, users have multiple online accounts that often reflect distinct identity aspects. This makes proving combinations of identity attributes hard on the users. In this paper, we address these weaknesses by proposing a privacy-preserving architecture for device-centric and attribute-based authentication based on: 1) the seamless integration between usable/strong device-centric authentication methods and federated login solutions; 2) the separation of the concerns for Authorization, Authentication, Behavioral Authentication and Identification to facilitate incremental deployability, wide adoption and compliance with NIST assurance levels; and 3) a novel centralized component that allows end-users to perform identity profile and consent management, to prove combinations of fragmented identity aspects, and to perform account recovery in case of device loss. To the best of our knowledge, this is the first effort towards fusing the aforementioned techniques under an integrated architecture. This architecture effectively deems the password paradigm obsolete with minimal modification on the service provider's software stack.
NIJun 22, 2017
D-STREAMON: from middlebox to distributed NFV framework for network monitoringPier Luigi Ventre, Alberto Caponi, Giuseppe Siracusano et al.
Many reasons make NFV an attractive paradigm for IT security: lowers costs, agile operations and better isolation as well as fast security updates, improved incident responses and better level of automation. On the other side, the network threats tend to be increasingly complex and distributed, implying huge traffic scale to be monitored and increasingly strict mitigation delay requirements. Considering the current trend of the net- working and the requirements to counteract to the evolution of cyber-threats, it is expected that also network monitoring will move towards NFV based solutions. In this paper, we present D- StreaMon an NFV-capable distributed framework for network monitoring realized to face the above described challenges. It relies on the StreaMon platform, a solution for network monitoring originally designed for traditional middleboxes. An evolution path which migrates StreaMon from middleboxes to Virtual Network Functions (VNFs) has been realized.
LGSep 18, 2015
BLC: Private Matrix Factorization Recommenders via Automatic Group LearningAlessandro Checco, Giuseppe Bianchi, Doug Leith
We propose a privacy-enhanced matrix factorization recommender that exploits the fact that users can often be grouped together by interest. This allows a form of "hiding in the crowd" privacy. We introduce a novel matrix factorization approach suited to making recommendations in a shared group (or nym) setting and the BLC algorithm for carrying out this matrix factorization in a privacy-enhanced manner. We demonstrate that the increased privacy does not come at the cost of reduced recommendation accuracy.
NINov 11, 2013
StreaMon: a data-plane programming abstraction for Software-defined Stream MonitoringGiuseppe Bianchi, Marco Bonola, Giulio Picierro et al.
The fast evolving nature of modern cyber threats and network monitoring needs calls for new, "software-defined", approaches to simplify and quicken programming and deployment of online (stream-based) traffic analysis functions. StreaMon is a carefully designed data-plane abstraction devised to scalably decouple the "programming logic" of a traffic analysis application (tracked states, features, anomaly conditions, etc.) from elementary primitives (counting and metering, matching, events generation, etc), efficiently pre-implemented in the probes, and used as common instruction set for supporting the desired logic. Multi-stage multi-step real-time tracking and detection algorithms are supported via the ability to deploy custom states, relevant state transitions, and associated monitoring actions and triggering conditions. Such a separation entails platform-independent, portable, online traffic analysis tasks written in a high level language, without requiring developers to access the monitoring device internals and program their custom monitoring logic via low level compiled languages (e.g., C, assembly, VHDL). We validate our design by developing a prototype and a set of simple (but functionally demanding) use-case applications and by testing them over real traffic traces.