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

Systematizing Decentralization and Privacy: Lessons from 15 Years of Research and Deployments

Decentralized systems are a subset of distributed systems where multiple authorities control different components and no authority is fully trusted by all. This implies that any component in a decentralized system is potentially adversarial. We revise fifteen years of research on decentralization and privacy, and provide an overview of key systems, as well as key insights for designers of future systems. We show that decentralized designs can enhance privacy, integrity, and availability but also require careful trade-offs in terms of system complexity, properties provided, and degree of decentralization. These trade-offs need to be understood and navigated by designers. We argue that a combination of insights from cryptography, distributed systems, and mechanism design, aligned with the development of adequate incentives, are necessary to build scalable and successful privacy-preserving decentralized systems

Strategies and challenges to facilitate situated learning in virtual worlds post-Second Life

Virtual worlds can establish a stimulating environment to support a situated learning approach in which students simulate a task within a safe environment. While in previous years Second Life played a major role in providing such a virtual environment, there are now more and more alternative—often OpenSim-based—solutions deployed within the educational community. By drawing parallels to social networks, we discuss two aspects: how to link individually hosted virtual worlds together in order to implement context for immersion and how to identify and avoid “fake” avatars so people behind these avatars can be held accountable for their actions

Crowd-ML: A Privacy-Preserving Learning Framework for a Crowd of Smart Devices

Smart devices with built-in sensors, computational capabilities, and network connectivity have become increasingly pervasive. The crowds of smart devices offer opportunities to collectively sense and perform computing tasks in an unprecedented scale. This paper presents Crowd-ML, a privacy-preserving machine learning framework for a crowd of smart devices, which can solve a wide range of learning problems for crowdsensing data with differential privacy guarantees. Crowd-ML endows a crowdsensing system with an ability to learn classifiers or predictors online from crowdsensing data privately with minimal computational overheads on devices and servers, suitable for a practical and large-scale employment of the framework. We analyze the performance and the scalability of Crowd-ML, and implement the system with off-the-shelf smartphones as a proof of concept. We demonstrate the advantages of Crowd-ML with real and simulated experiments under various conditions