Concept
Privacy concerns with social networking services
Related publications (217)
ProGAP: Progressive Graph Neural Networks with Differential Privacy Guarantees
Daniel Gatica-Perez, Sina Sajadmanesh
Graph Neural Networks (GNNs) have become a popular tool for learning on graphs, but their widespread use raises privacy concerns as graph data can contain personal or sensitive information. Differentially private GNN models have been recently proposed to p ...
Assoc Computing Machinery2024PRO-Face C: Privacy-Preserving Recognition of Obfuscated Face via Feature Compensation
Touradj Ebrahimi, Lin Yuan, Xiao Pu, Yao Zhang, Hongbo Li
The advancement of face recognition technology has delivered substantial societal advantages. However, it has also raised global privacy concerns due to the ubiquitous collection and potential misuse of individuals' facial data. This presents a notable par ...
Ieee-Inst Electrical Electronics Engineers Inc2024Challenging the Assumptions: Rethinking Privacy, Bias, and Security in Machine Learning
Predictive models based on machine learning (ML) offer a compelling promise: bringing clarity and structure to complex natural and social environments. However, the use of ML poses substantial risks related to the privacy of their training data as well as ...
EPFL2023Privacy-Preserving Machine Learning on Graphs
Graph Neural Networks (GNNs) have emerged as a powerful tool for learning on graphs, demonstrating exceptional performance in various domains. However, as GNNs become increasingly popular, new challenges arise. One of the most pressing is the need to ensur ...
EPFL2023P3LI5: Practical and confidEntial Lawful Interception on the 5G core
Apostolos Pyrgelis, Francesco Intoci
Lawful Interception (LI) is a legal obligation of Communication Service Providers (CSPs) to provide interception capabilities to Law Enforcement Agencies (LEAs) in order to gain insightful data from network communications for criminal proceedings, e.g., ne ...
New York2023Taking Back Our Commons: Social Media APIs as Subversive Tools
Our actual internet landscape is dominated by a handful of private actors we use on a daily basis: Twitter, Facebook, Instagram, amongst others. These actors, in constant search of an optimization of their data transmission processes and user experiences, ...
20232XTWEETSXMODEMSXTXTXTWEET (2X): Combining Media archeology and electronic literature to support societal change through design
Through the case study of the 2XTWEETSXMODEMSXTXTXTWEET (abbreviated 2X), this contribution will situate the potential of using Twitter’s publically available data streams as inputs for the creation of media archeological (Hertz and Parikka, 2012) textual ...
2023TEEzz: Fuzzing Trusted Applications on COTS Android Devices
Mathias Josef Payer, Marcel Busch
Security and privacy-sensitive smartphone applications use trusted execution environments (TEEs) to protect sensitive operations from malicious code. By design, TEEs have privileged access to the entire system but expose little to no insight into their inn ...
IEEE COMPUTER SOC2023Deplatforming did not decrease Parler users' activity on fringe social media
Robert West, Manoel Horta Ribeiro
Online platforms have banned ("deplatformed") influencers, communities, and even entire websites to reduce content deemed harmful. Deplatformed users often migrate to alternative platforms, which raises concerns about the effectiveness of deplatforming. He ...
OXFORD UNIV PRESS2023On the (In)security of Peer-to-Peer Decentralized Machine Learning
Carmela González Troncoso, Mathilde Aliénor Raynal, Dario Pasquini
In this work, we carry out the first, in-depth, privacy analysis of Decentralized Learning-a collaborative machine learning framework aimed at addressing the main limitations of federated learning. We introduce a suite of novel attacks for both passive and ...
IEEE COMPUTER SOC2023