3,755 research outputs found
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
On the Security and Feasibility of Safebook: A Distributed Privacy-Preserving Online Social Network
International audienceSafebook tackles the security and privacy problems of online social networks. It puts a special emphasis on the privacy of users with respect to the application provider and provides defenses against intruders or malicious users. In order to assure privacy in the face of potential violations by the provider, Safebook is designed in a decentralized architecture. It relies on the cooperation among the independent parties that represent the users of the online social network at the same time. Safebook addresses the problem of building secure and privacypreserving data storage and communication mechanisms in a peer-topeer system by leveraging trust relationships akin to social networks in real life. This paper resumes the contributions of [7, 9, 8], and extends the first performance and security evaluation of Safebook
Decentralized Graph Neural Network for Privacy-Preserving Recommendation
Building a graph neural network (GNN)-based recommender system without
violating user privacy proves challenging. Existing methods can be divided into
federated GNNs and decentralized GNNs. But both methods have undesirable
effects, i.e., low communication efficiency and privacy leakage. This paper
proposes DGREC, a novel decentralized GNN for privacy-preserving
recommendations, where users can choose to publicize their interactions. It
includes three stages, i.e., graph construction, local gradient calculation,
and global gradient passing. The first stage builds a local inner-item
hypergraph for each user and a global inter-user graph. The second stage models
user preference and calculates gradients on each local device. The third stage
designs a local differential privacy mechanism named secure gradient-sharing,
which proves strong privacy-preserving of users' private data. We conduct
extensive experiments on three public datasets to validate the consistent
superiority of our framework
A NOVEL FRAMEWORK FOR SOCIAL INTERNET OF THINGS: LEVERAGING THE FRIENDSHIPS AND THE SERVICES EXCHANGED BETWEEN SMART DEVICES
As humans, we tackle many problems in complex societies and manage the complexities of networked social systems. Cognition and sociability are two vital human capabilities that improve social life and complex social interactions. Adding these features to smart devices makes them capable of managing complex and networked Internet of Things (IoT) settings.
Cognitive and social devices can improve their relationships and connections with other devices and people to better serve human needs. Nowadays, researchers are investigating two future generations of IoT: social IoT (SIoT) and cognitive IoT (CIoT). This study develops a new framework for IoT, called CSIoT, by using complexity science concepts and by integrating social and cognitive IoT concepts. This framework uses a new mechanism to leverage the friendships between devices to address service management, privacy, and security. The framework addresses network navigability, resilience, and heterogeneity between devices in IoT settings. This study uses a new simulation tool for evaluating the new CSIoT framework and evaluates the privacy-preserving ability of CSIoT using the new simulation tool. To address different CSIoT security and privacy issues, this study also proposes a blockchain-based CSIoT. The evaluation results show that CSIoT can effectively preserve the privacy and the blockchain-based CSIoT performs effectively in addressing different privacy and security issues
How Far Removed Are You? Scalable Privacy-Preserving Estimation of Social Path Length with Social PaL
Social relationships are a natural basis on which humans make trust
decisions. Online Social Networks (OSNs) are increasingly often used to let
users base trust decisions on the existence and the strength of social
relationships. While most OSNs allow users to discover the length of the social
path to other users, they do so in a centralized way, thus requiring them to
rely on the service provider and reveal their interest in each other. This
paper presents Social PaL, a system supporting the privacy-preserving discovery
of arbitrary-length social paths between any two social network users. We
overcome the bootstrapping problem encountered in all related prior work,
demonstrating that Social PaL allows its users to find all paths of length two
and to discover a significant fraction of longer paths, even when only a small
fraction of OSN users is in the Social PaL system - e.g., discovering 70% of
all paths with only 40% of the users. We implement Social PaL using a scalable
server-side architecture and a modular Android client library, allowing
developers to seamlessly integrate it into their apps.Comment: A preliminary version of this paper appears in ACM WiSec 2015. This
is the full versio
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