Implementing a P2P Social Network - Early Experiences and Insights from PeerSoN

Related publications (30)

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Spikyball Sampling: Exploring Large Networks via an Inhomogeneous Filtered Diffusion

Nicolas Aspert, Volodymyr Miz, Benjamin Ricaud

Studying real-world networks such as social networks or web networks is a challenge. These networks often combine a complex, highly connected structure together with a large size. We propose a new approach for large scale networks that is able to automatic ...

2020

Privacy-preserving photo sharing based on blockchain

Touradj Ebrahimi, Pablo Pfister

Sharing photos online has become an extremely popular activity, raising a wide concern on privacy issues related to the shared content. ProShare, a photo-sharing solution developed by Multimedia Signal Processing Group of EPFL addresses some of these priva ...

SPIE-INT SOC OPTICAL ENGINEERING2020

DELF: Safeguarding deletion correctness in Online Social Networks

Georgios Damaskinos

Deletion is a core facet of Online Social Networks (OSNs). For users, deletion is a tool to remove what they have shared and control their data. For OSNs, robust deletion is both an obligation to their users and a risk when developer mistakes inevitably oc ...

USENIX ASSOC2020

High dynamic range image sharing with privacy protection

Touradj Ebrahimi, Evgeniy Upenik, Pol Fernandez Marti

Sharing pictures has become a very popular practice among consumers. Most recent cameras, displays, and smartphones can capture and display images in high dynamic range and wide colour gamut, contributing to an increase of this type of content. It is a wel ...

SPIE2020

Wet Markets and Food Safety: TripAdvisor for Improved Global Digital Surveillance

Sharada Prasanna Mohanty, Martin Mathias Müller

Background: Wet markets are markets selling fresh meat and produce. Wet markets are critical for food security and sustainable development in their respective regions. Due to their cultural significance, they attract numerous visitors and consequently gene ...

2019

Anomaly detection in the dynamics of web and social networks

Pierre Vandergheynst, Kirell Maël Benzi, Volodymyr Miz, Benjamin Ricaud

In this work, we propose a new, fast and scalable method for anomaly detection in large time-evolving graphs. It may be a static graph with dynamic node attributes (e.g. time-series), or a graph evolving in time, such as a temporal network. We define an an ...

2019

Discovering Interaction Patterns in Online Learning Environments : A Learning Analytics Research

Mina Shirvani Boroujeni

The increasing amount of data collected in online learning environments provides unique opportunities to better understand the learning processes in different educational settings. Learning analytics research aims at understanding and optimizing learning a ...

EPFL2018

A Predictive Model for User Motivation and Utility Implications of Privacy-Protection Mechanisms in Location Check-Ins

Jean-Pierre Hubaux, Kévin Clément Huguenin, Igor Bilogrevic, Reza Shokri, Italo Ivan Dacosta Petrocelli, Joana Catarina Soares Machado, Stefan Mihaila

Location check-ins contain both geographical and semantic information about the visited venues. Semantic information is usually represented by means of tags (e.g., “restaurant”). Such data can reveal some personal information about users beyond what they a ...

2018

Geographical Feature Extraction for Entities in Location-based Social Networks

Pearl Pu Faltings

Location-based embedding is a fundamental problem to solve in location-based social network (LBSN). In this paper, we propose a geographical convolutional neural tensor network (GeoCNTN) as a generic embedding model. GeoCNTN first takes the raw location da ...

ASSOC COMPUTING MACHINERY2018

Automatic Online Fake News Detection Combining Content and Social Signals

Stefano Moret, Gabriele Ballarin

The proliferation and rapid diffusion of fake news on the Internet highlight the need of automatic hoax detection systems. In the context of social networks, machine learning (ML) methods can be used for this purpose. Fake news detection strategies are tra ...

IEEE2018

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