Browsing by Author "Masciari, Elio"

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Methodologies and Applications for Big Data Analytics
(Università della Calabria, 2020-05-02) Cassavia, Nunziato; Crupi, Felice; Flesca, Sergio; Masciari, Elio
Due to the emerging Big Data paradigm, driven by the increase availability of users generated data, traditional data management techniques are inadequate in many real life scenarios. The availability of huge amounts of data pertaining to user social interactions calls for advanced analysis strategies in order to extract meaningful information. Furthermore, heterogeneity and high speed of user generated data require suitable data storage and management and a huge amount of computing power. This dissertation presents a Big Data framework able to enhances user quest for information by exploiting previous knowledge about their social environment. Moreover an introduction to Big Data and NoSQL systems is provided and two basic architecture for Big Data analysis are presented. The framework that enhances user quest, leverages the extent of influence that the users are potentially subject to and the influence they may exert on other users. User influence spread, across the network, is dynamically computed as well to improve user search strategy by providing specific suggestions, represented as tailored faceted features. The approach is tested in an important application scenario such as tourist recommendation where several experiment have been performed to assess system scalability and data read/write efficiency. The study of this system and of advanced analysis on Big Data has shown the need for a huge computing power. To this end an high performance computing system named CoremunitiTM is presented. This system represents a P2P solution for solving complex works by using the idling computational resources of users connected to this network. Users help each other by asking the network computational resources when they face high computing demanding tasks. Differently from many proposals available for volunteer computing, users providing their resources are rewarded with tangible credits. This approach is tested in an interesting scenario as 3D rendering where its efficiency has been compared with "traditional" commercial solutions like cloud platforms and render farms showing shorter task completion times at low cost.
Methodologies and Applications for Big Data Analytics
(Università della Calabria, 2020-05-02) Cassavia, Nunziato; Crupi, Felice; Flesca, Sergio; Masciari, Elio;
Due to the emerging Big Data paradigm, driven by the increase availability of users generated data, traditional data management techniques are inadequate in many real life scenarios. The availability of huge amounts of data pertaining to user social interactions calls for advanced analysis strategies in order to extract meaningful information. Furthermore, heterogeneity and high speed of user generated data require suitable data storage and management and a huge amount of computing power. This dissertation presents a Big Data framework able to enhances user quest for information by exploiting previous knowledge about their social environment. Moreover an introduction to Big Data and NoSQL systems is provided and two basic architecture for Big Data analysis are presented. The framework that enhances user quest, leverages the extent of influence that the users are potentially subject to and the influence they may exert on other users. User influence spread, across the network, is dynamically computed as well to improve user search strategy by providing specific suggestions, represented as tailored faceted features. The approach is tested in an important application scenario such as tourist recommendation where several experiment have been performed to assess system scalability and data read/write efficiency. The study of this system and of advanced analysis on Big Data has shown the need for a huge computing power. To this end an high performance computing system named CoremunitiTM is presented. This system represents a P2P solution for solving complex works by using the idling computational resources of users connected to this network. Users help each other by asking the network computational resources when they face high computing demanding tasks. Differently from many proposals available for volunteer computing, users providing their resources are rewarded with tangible credits. This approach is tested in an interesting scenario as 3D rendering where its efficiency has been compared with "traditional" commercial solutions like cloud platforms and render farms showing shorter task completion times at low cost.
Theoretical and Practical Aspects of Trusted Execution Environments in Information Security and Volunteer Computing
(2018-08-06) Ianni, Michele; Pugliese, Andrea; Masciari, Elio
Commodity operating systems, both on desktop and mobile devices, offer rich functionality and consequently a significant attack surface. A compromise of the operating system, however, means that an attacker also has access to any critical assets of the user’s applications. These critical assets include code, which either is part of security-critical functionality, or of commercial value and other sensitive information whose disclosure, even in a minimal part, must be avoided. While many platforms offer support for Trusted Execution Environments (TEEs), these are currently restricted for the use of secure services provided by the operating system or the vendor. Developers have to rely on obfuscation to protect their own code from unauthorized tampering or copying, which only provides an obstacle for an attacker but does not prevent compromise. In collaborative networks, moreover, many problems are usually not handled at all, since it is not possible, in many cases, to hide confidential data from inputs of the subtasks solved by the computers of the network. This thesis proposes to take advantage and extend these TEEs to also offer code protection for arbitrary application and secure data in volunteer computing networks