Publications associées à Not Yet Another Digital ID: Privacy-Preserving Humanitarian Aid Distribution

Publications associées (35)

A 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 ...

Ieee-Inst Electrical Electronics Engineers Inc2024

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 ...

Springer2024

The Privacy Power of Correlated Noise in Decentralized Learning

Rachid Guerraoui, Martin Jaggi, Youssef Allouah, Anastasiia Koloskova

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 ...

PMLR2024

Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning

Anastasiia Koloskova

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 ...

EPFL2024

Automated Formal Verification of Software Network Functions

Solal Vincenzo Pirelli

Formally verifying the correctness of software is necessary to merit the trust people put in software systems. Currently, formal verification requires human effort to prove that a piece of code matches its specification and code changes to improve verifiab ...

EPFL2024

Analytical Computation of the Sensitivity Coefficients in Hybrid AC/DC Networks

Mario Paolone, Willem Lambrichts

In this paper, we present a model for the analytical computation of the power flow sensitivity coefficients (SCs) for hybrid AC/DC networks. The SCs are defined as the partial derivates of the nodal voltages with respect to the active and reactive power in ...

2024

Bridging the gap between theoretical and practical privacy technologies for at-risk populations

Kasra Edalatnejadkhamene

With the pervasive digitalization of modern life, we benefit from efficient access to information and services. Yet, this digitalization poses severe privacy challenges, especially for special-needs individuals. Beyond being a fundamental human right, priv ...

EPFL2023

Recursive Control Variates for Inverse Rendering

Wenzel Alban Jakob, Baptiste Alexandre Marie Philippe Claude Nicolet

We present a method for reducing errors-variance and bias-in physically based differentiable rendering (PBDR). Typical applications of PBDR repeatedly render a scene as part of an optimization loop involving gradient descent. The actual change introduced b ...

2023

Arbitrary Decisions are a Hidden Cost of Differentially Private Training

Carmela González Troncoso, Bogdan Kulynych

Mechanisms used in privacy-preserving machine learning often aim to guarantee differential privacy (DP) during model training. Practical DP-ensuring training methods use randomization when fitting model parameters to privacy-sensitive data (e.g., adding Ga ...

Assoc Computing Machinery2023

Privacy-Preserving Machine Learning on Graphs

Sina Sajadmanesh

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 ...

EPFL2023

Challenging the Assumptions: Rethinking Privacy, Bias, and Security in Machine Learning

Bogdan Kulynych

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 ...

EPFL2023

P3LI5: Practical and confidEntial Lawful Interception on the 5G core

Francesco Intoci, Apostolos Pyrgelis

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 ...

IEEE2023

A comprehensive review of digital twin-part 2: roles of uncertainty quantification and optimization, a battery digital twin, and perspectives (vol 66, 1, 2022)

Olga Fink

SPRINGER2023

Enforcing Privacy in Distributed Learning With Performance Guarantees

Ali H. Sayed, Stefan Vlaski, Elsa Rizk

We study the privatization of distributed learning and optimization strategies. We focus on differential privacy schemes and study their effect on performance. We show that the popular additive random perturbation scheme degrades performance because it is ...

Ieee-Inst Electrical Electronics Engineers Inc2023

Personalized 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 ...

2023

Sustainability and Ethicality are Peripheral to Students’ Software Design

Bryan Alexander Ford, Siara Ruth Isaac, Aditi Kothiyal, Pierluca Borsò

The conceptual design phase is a fascinating moment to observe how a design task is interpreted, as the (often implicit) relative importance students accord to the various requirements and constraints offers a window into the thinking underpinning their de ...

Tempus Publications2023

On the Privacy-Robustness-Utility Trilemma in Distributed Learning

Rachid Guerraoui, Nirupam Gupta, Rafaël Benjamin Pinot, Youssef Allouah, John Stephan

The ubiquity of distributed machine learning (ML) in sensitive public domain applications calls for algorithms that protect data privacy, while being robust to faults and adversarial behaviors. Although privacy and robustness have been extensively studied ...

2023

Differentially Private Multi-Agent Constraint Optimization

Boi Faltings, Sujit Prakash Gujar, Aleksei Triastcyn, Sankarshan Damle

Several optimization scenarios involve multiple agents that desire to protect the privacy of their preferences. There are distributed algorithms for constraint optimization that provide improved privacy protection through secure multiparty computation. How ...

ACM2022

Protecting privacy through metadata analysis

Sandra Deepthy Siby

Although encryption hides the content of communications from third parties, metadata, i.e., the information attached to the content (such as the size or timing of communication) can be a rich source of details and context. In this dissertation, we demonstr ...

EPFL2022

Generating Higher-Fidelity Synthetic Datasets with Privacy Guarantees

Boi Faltings, Aleksei Triastcyn

We consider the problem of enhancing user privacy in common data analysis and machine learning development tasks, such as data annotation and inspection, by substituting the real data with samples from a generative adversarial network. We propose employing ...

MDPI2022