A Privacy-Aware Framework for Decentralized Online Social Networks

Online social networks based on a single service provider suffer several drawbacks, first of all the privacy issues arising from the delegation of user data to a single entity. Distributed online social networks (DOSN) have been recently proposed as an alternative solution allowing users to keep control of theirprivate data. However, the lack of a centralized entity introduces new problems, like the need of defining proper privacy policies for data access and of guaranteeing the availability of user\u27s data when the user disconnects from the social network. This paper introduces a privacy-aware support for DOSN enabling users to define a set of privacy policies which describe who is entitled to access the data in their social profile. These policies are exploited by the DOSN support to decide the re-allocation of the profile when the user disconnects from the socialnetwork.The proposed approach is validated through a set of simulations performed on real traces logged from Facebook

Evaluation of Structural and Temporal Properties of Ego Networks for Data Availability in DOSNs

The large diffusion of Online Social Networks (OSNs) has influenced the way people interact with each other. OSNs present several drawbacks, one of the most important is the problem of privacy disclosures. Distributed Online Social Networks (DOSNs) have been proposed as a valid alternative solution to solve this problem. DOSNs are Online Social Networks implemented on a distributed platform, such as a P2P system or a mobile network. However, the decentralization of the control presents several challenges, one of the main ones is guaranteeing data availability without relying on a central server. To this aim, users’ data allocation strategies have to be defined and this requires the knowledge of both structural and temporal characteristics of ego networks which is a difficult task due to the lack of real datasets limiting the research in this field. The goal of this paper is the study of the behaviour of users in a real social network in order to define proper strategies to allocate the users’ data on the DOSN nodes. In particular, we present an analysis of the temporal affinity and the structure of communities and their evolution over the time by using a real Facebook dataset

Persistenza dei dati in P2P Dunbar-based social overlay

La tesi propone ed esamina in modo sperimentale un algoritmo distribuito per la selezione di social cache, in un nuovo modello di DOSN con P2P Dunbar-based social overlay, prendendo in considerazione caratteristiche strutturali (Ego Betweenness Centrality) e temporali dei nodi

Characterization of water-air dispersed two phase flow

The analysis of two phase flow has a great relevance in many industrial sectors such as nuclear and process industry. The study and the measurement of the related phenomena are particularly dicult due to the variety of the parameters that aect the flow (void fraction, flow regime, orientation, etc.). Measurement instrumentation for two phase flow is nowadays very limited even if it would be highly useful in the industrial field. Before approaching the research and development toward the realization of innovative instrumentation for two-phase flow measurement, it is fundamental a precise description of the particular flow regime of interest. Dispersed water droplets in air/gas is a possible flow regime at high gas void fractions; therefore it is important to characterize this particular flow pattern in a detailed way. To reproduce this condition in a laboratory environment it is possible to use nozzles with very small outlet diameters and high pressure water supply. In this paper the characterization of dispersed flow is performed as function of the nozzle characteristic and water inlet pressure. The tests are performed using an experimental setup realized at the Energy Department at Politecnico di Torino. The water jet is observed in a PMMA (PolyMethylMethacrylate) pipe 1.8 meters long with an inner diameter of 78 mm. High pressure water is obtained using a plunger pump and pump inlet water pressure is adjustable in the range 1-4 bar.Water pressure upstream the nozzle and air entrainment flow rate are measured and used as primary parameters of the study. A sensitivity analysis on these two parameters is performed with the purpose to find the conditions that are optimal to reproduce a dispersed flow

Preserving privacy of contents in Decentralized Online Social Networks

Traditional Online Social Networks (OSNs), which are based on a single service provider, suffer several drawbacks, first of all the privacy issues arising from the delegation of user data to a single entity, the OSN provider. In the last years, Distributed Online Social Networks (DOSNs) have been proposed to shift the control over user data from the OSN provider to the users of the DOSN themselves. Indeed, in contrast to centralized OSNs (such as Facebook), DOSNs are not based on centralized storage services which decide the term of service and the contents shared by the users are stored on the devices of the users themselves. However, the lack of a centralized entity introduces new interesting challenges, like that of guaranteeing the availability of user’s contents when the user disconnects from the DOSN and the privacy of the user’s contents. In this dissertation we investigate the problem of preserving the privacy of the contents shared by users of DOSNs by focusing on two different aspects: i) the need of defining proper privacy policies for content access and ii) the storage (allocation and replication) of these contents on the nodes which build up the DOSN system. When efficiency has to be taken into account, new solutions have to be devised that minimize as much as possible the overhead introduced by the enforcement of the privacy policy and enable a higher contents availability through distribution and replication. Current solutions fall short in meeting the above criteria, while in this dissertation we proposed two approaches which adopt two very different solutions to guarantee the protection of the contents according to the privacy preferences of users and efficiently reduce the overhead introduced by privacy enforcement mechanisms. The proposed approaches are validated by an experimental campaign based on data obtained from a real OSN which have enabled the definition of a set of simulations taking into account realistic scenarios. The experimental results obtained from a set of simulations performed on real traces show the effectiveness of our approaches

Studying micro-communities in facebook communities

In the visionary view of the future Internet, named the Next Generation Internet, a current idea is to have a user-centric approach where human behavior models will be used to define the networks or to manage services. During the last years, a great trend in current Social Media platforms is to offer the opportunity to establish and join groups of people online. Despite human behaviour in current Online Social Media have been studied in depth, characteristics of these aggregations of people in content-based communities are still unknown. In this paper, we propose an evaluation of micro-communities of users inside the big network of Facebook groups to understand how and when users are active, and to evaluate the evolution of these micro-communities over time. Results show that almost all groups showed interactions-based communities. We found out that in all cases there is one massive core community which attracts small communities

Evaluating the impact of friend in predicting user's availability in Online Social Networks

n recent years, Online Social Networks (OSNs) have changed the way people connect and interact with each other. Indeed, most peo- ple have registered an account on some popular OSNs (such as Facebook, or Google+) which is used to access the system at different times of the days, depending on their life and habits. In this context, understanding how users connect to the OSNs is of paramount importance for both the protection of their privacy and the OSN’s provider (or third-party appli- cations) that want to exploit this information. In this paper, we study the task of predicting the availability status (online/offline) of the OSNs’ users by exploiting the availability information of their friends. The basic idea is to evaluate how the knowledge about availability status of friends can help in predicting the availability status of the center-users. For this purpose, we exploit several learning algorithms to find interesting rela- tionships between the availability status of the users and those of their friends. The extensive validation of the results, by using a real Facebook dataset, indicates that the availability status of the users’ friends can help in predicting whether the central user is online or offlin

Evaluating the impact of friends in predicting user’s availability in online social networks

In recent years, Online Social Networks (OSNs) have changed the way people connect and interact with each other. Indeed, most people have registered an account on some popular OSNs (such as Facebook, or Google+) which is used to access the system at different times of the days, depending on their life and habits. In this context, understanding how users connect to the OSNs is of paramount importance for both the protection of their privacy and the OSN’s provider (or third-party applications) that want to exploit this information. In this paper, we study the task of predicting the availability status (online/offline) of the OSNs’ users by exploiting the availability information of their friends. The basic idea is to evaluate how the knowledge about availability status of friends can help in predicting the availability status of the center-users. For this purpose, we exploit several learning algorithms to find interesting relationships between the availability status of the users and those of their friends. The extensive validation of the results, by using a real Facebook dataset, indicates that the availability status of the users’ friends can help in predicting whether the central user is online or offline