Quantifying Location Privacy: The Case of Sporadic Location Exposure

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Privacy-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 ...

EPFL2023

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

On the Privacy-Robustness-Utility Trilemma in Distributed Learning

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

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

A Practical Influence Approximation for Privacy-Preserving Data Filtering in Federated Learning

Boi Faltings, Ljubomir Rokvic, Panayiotis Danassis

Federated Learning by nature is susceptible to low-quality, corrupted, or even malicious data that can severely degrade the quality of the learned model. Traditional techniques for data valuation cannot be applied as the data is never revealed. We present ...

2023

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

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

Carmela González Troncoso, Boya Wang, Wouter Lueks, Justinas Sukaitis

Humanitarian aid-distribution programs help bring physical goods to people in need. Traditional paper-based solutions to support aid distribution do not scale to large populations and are hard to secure. Existing digital solutions solve these issues, at th ...

IEEE COMPUTER SOC2023

Modeling lightning strikes to tall towers

Farhad Rachidi-Haeri, Marcos Rubinstein

In this chapter, we present a review of recent progress in the modeling of lightning strikes to tall structures. Since some tall structures are struck by lightning several tens of times per year, they can be used as ground-truth to measure and calibrate th ...

IET2023

Efficient and Accurate Peer-to-Peer Training of Machine Learning Based Home Thermal Models

Rachid Guerraoui, Alexandre David Olivier Maurer, Omid Ardakanian

The integration of smart thermostats in home automation systems has created an opportunity to optimize space heating and cooling through the use of machine learning, for example for thermal model identification. Nonetheless, its full potential remains unta ...

Assoc Computing Machinery2023

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

Data Privacy Concerns as a Source of Resistance to Complete Mobile Data Collection Tasks Via a Smartphone App

Daniel Gatica-Perez

Smartphones present many interesting opportunities for survey research, particularly through the use of mobile data collection applications (apps). There is still much to learn, however, about how to integrate apps in general population surveys. Recent stu ...

OXFORD UNIV PRESS INC2022

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