Concept
Privacy by design
Related publications (203)
Differentially private multi-agent constraint optimization
Boi Faltings, Sujit Prakash Gujar, Aleksei Triastcyn, Sankarshan Damle
Distributed constraint optimization (DCOP) is a framework in which multiple agents with private constraints (or preferences) cooperate to achieve a common goal optimally. DCOPs are applicable in several multi-agent coordination/allocation problems, such as ...
Dordrecht2024Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning
Distributed learning is the key for enabling training of modern large-scale machine learning models, through parallelising the learning process. Collaborative learning is essential for learning from privacy-sensitive data that is distributed across various ...
EPFL2024When Your AI Becomes a Target: AI Security Incidents and Best Practices
Alexandre Massoud Alahi, Kathrin Grosse
In contrast to vast academic efforts to study AI security, few real-world reports of AI security incidents exist. Released incidents prevent a thorough investigation of the attackers' motives, as crucial information about the company and AI application is ...
AAAI Press2024The Privacy Power of Correlated Noise in Decentralized Learning
Rachid Guerraoui, Martin Jaggi, Anastasiia Koloskova, Youssef Allouah, Aymane El Firdoussi
Decentralized learning is appealing as it enables the scalable usage of large amounts of distributed data and resources (without resorting to any central entity), while promoting privacy since every user minimizes the direct exposure of their data. Yet, wi ...
PMLR2024A Privacy-Preserving Querying Mechanism with High Utility for Electric Vehicles
Electric vehicles (EVs) are becoming more popular due to environmental consciousness. The limited availability of charging stations (CSs), compared to the number of EVs on the road, has led to increased range anxiety and a higher frequency of CS queries du ...
Piscataway2024Personalized Privacy-Preserving Distributed Learning on Heterogeneous Data
Michael Christoph Gastpar, Aditya Pradeep
One major challenge in distributed learning is to efficiently learn for each client when the data across clients is heterogeneous or non iid (not independent or identically distributed). This provides a significant challenge as the data of the other client ...
2023Challenging 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 ...
EPFL2023On 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 SOC2023Privacy-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 York2023