Grassroots Social Networking: Where People have Agency over their Personal Information and Social Graph
- URL: http://arxiv.org/abs/2306.13941v5
- Date: Wed, 1 May 2024 12:02:47 GMT
- Title: Grassroots Social Networking: Where People have Agency over their Personal Information and Social Graph
- Authors: Ehud Shapiro,
- Abstract summary: We present a grassroots architecture for serverless, permissionless, peer-to-peer social networks termed Grassroots Social Networking.
The architecture incorporates (i) a decentralized social graph, where each person controls, maintains and stores only their local neighborhood in the graph.
We provide two example Grassroots Social Networking protocols -- Twitter-like and WhatsApp-like -- and address their security (safety, liveness and privacy), spam/bot/deep-fake resistance, and implementation.
- Score: 2.06682776181122
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Offering an architecture for social networking in which people have agency over their personal information and social graph is an open challenge. Here we present a grassroots architecture for serverless, permissionless, peer-to-peer social networks termed Grassroots Social Networking that aims to address this challenge. The architecture is geared for people with networked smartphones -- roaming (address-changing) computing devices communicating over an unreliable network (e.g., using UDP). The architecture incorporates (i) a decentralized social graph, where each person controls, maintains and stores only their local neighborhood in the graph; (iii) personal feeds, with authors and followers who create and store the feeds; and (ii) a grassroots dissemination protocol, in which communication among people occurs only along the edges of their social graph. The architecture realizes these components using the blocklace data structure -- a partially-ordered conflict-free counterpart of the totally-ordered conflict-based blockchain. We provide two example Grassroots Social Networking protocols -- Twitter-like and WhatsApp-like -- and address their security (safety, liveness and privacy), spam/bot/deep-fake resistance, and implementation, demonstrating how server-based social networks could be supplanted by a grassroots architecture.
Related papers
- Architecture for Protecting Data Privacy in Decentralized Social Networks [5.874802930380899]
This paper proposes a novel Decentralized Social Network employing comprehensive technology and Decentralized Networks completed by Access Control Smart Contracts.
In conclusion, the principal results highlight the benefit of our decentralized social network to protect user privacy.
arXiv Detail & Related papers (2024-09-27T00:35:02Z) - Cross-Network Social User Embedding with Hybrid Differential Privacy
Guarantees [81.6471440778355]
We propose a Cross-network Social User Embedding framework, namely DP-CroSUE, to learn the comprehensive representations of users in a privacy-preserving way.
In particular, for each heterogeneous social network, we first introduce a hybrid differential privacy notion to capture the variation of privacy expectations for heterogeneous data types.
To further enhance user embeddings, a novel cross-network GCN embedding model is designed to transfer knowledge across networks through those aligned users.
arXiv Detail & Related papers (2022-09-04T06:22:37Z) - Exploiting Social Graph Networks for Emotion Prediction [2.7376140293132667]
We utilize mobile sensing data to predict happiness and stress.
In addition to a person's physiological features, we also incorporate the environment's impact through weather and social network.
We propose an architecture that automates the integration of a user's social network affect prediction.
arXiv Detail & Related papers (2022-07-12T20:24:39Z) - Privatized Graph Federated Learning [57.14673504239551]
We introduce graph federated learning, which consists of multiple units connected by a graph.
We show how graph homomorphic perturbations can be used to ensure the algorithm is differentially private.
arXiv Detail & Related papers (2022-03-14T13:48:23Z) - Federated Social Recommendation with Graph Neural Network [69.36135187771929]
We propose fusing social information with user-item interactions to alleviate it, which is the social recommendation problem.
We devise a novel framework textbfFedrated textbfSocial recommendation with textbfGraph neural network (FeSoG)
arXiv Detail & Related papers (2021-11-21T09:41:39Z) - Harnessing the Power of Ego Network Layers for Link Prediction in Online
Social Networks [0.734084539365505]
Predictions are typically based on unsupervised or supervised learning.
We argue that richer information about personal social structure of individuals might lead to better predictions.
We show that social-awareness generally provides significant improvements in the prediction performance.
arXiv Detail & Related papers (2021-09-19T18:49:10Z) - GAEA: Graph Augmentation for Equitable Access via Reinforcement Learning [50.90625274621288]
Disparate access to resources by different subpopulations is a prevalent issue in societal and sociotechnical networks.
We introduce a new class of problems, Graph Augmentation for Equitable Access (GAEA), to enhance equity in networked systems by editing graph edges under budget constraints.
arXiv Detail & Related papers (2020-12-07T18:29:32Z) - Interpretable Signed Link Prediction with Signed Infomax Hyperbolic
Graph [54.03786611989613]
signed link prediction in social networks aims to reveal the underlying relationships (i.e. links) among users (i.e. nodes)
We develop a unified framework, termed as Signed Infomax Hyperbolic Graph (textbfSIHG)
In order to model high-order user relations and complex hierarchies, the node embeddings are projected and measured in a hyperbolic space with a lower distortion.
arXiv Detail & Related papers (2020-11-25T05:09:03Z) - An Enhanced Geo Location Technique for Social Network Communication
System [0.0]
This paper advocates for an advanced and secured approach for improving communication in a social Network with the use of geo-location technique.
The proposed system will help the government and security agencies fight recent security challenges in the country.
arXiv Detail & Related papers (2020-09-05T19:57:44Z) - TIES: Temporal Interaction Embeddings For Enhancing Social Media
Integrity At Facebook [9.023847175654602]
We present a novel Temporal Interaction EmbeddingS model that is designed to capture rogue social interactions and flag them for further suitable actions.
TIES is a supervised, deep learning, production ready model at Facebook-scale networks.
To show the real-world impact of TIES, we present a few applications especially for preventing spread of misinformation, fake account detection, and reducing ads payment risks.
arXiv Detail & Related papers (2020-02-18T22:56:40Z) - DiffNet++: A Neural Influence and Interest Diffusion Network for Social
Recommendation [50.08581302050378]
Social recommendation has emerged to leverage social connections among users for predicting users' unknown preferences.
We propose a preliminary work of a neural influence diffusion network (i.e., DiffNet) for social recommendation (Diffnet)
In this paper, we propose DiffNet++, an improved algorithm of Diffnet that models the neural influence diffusion and interest diffusion in a unified framework.
arXiv Detail & Related papers (2020-01-15T08:45:34Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.