Publications related to Internet privacy

On the Privacy-Robustness-Utility Trilemma in Distributed Learning

Rachid Guerraoui, Nirupam Gupta, John Stephan, Youssef Allouah, 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

Networked Signal and Information Processing: Learning by multiagent systems

Ali H. Sayed, Stefan Vlaski

This article reviews significant advances in networked signal and information processing (SIP), which have enabled in the last 25 years extending decision making and inference, optimization, control, and learning to the increasingly ubiquitous environments ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

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 ...

Piscataway2023

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

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

A Modular Multimodal Architecture for Gaze Target Prediction: Application to Privacy-Sensitive Settings

Jean-Marc Odobez, Anshul Gupta, Samy Tafasca

Predicting where a person is looking is a complex task, requiring to understand not only the person's gaze and scene content, but also the 3D scene structure and the person's situation (are they manipulating? interacting or observing others? attentive?) to ...

IEEE2022

Synthetic Data - Anonymisation Groundhog Day

Carmela González Troncoso, Theresa Stadler

Synthetic data has been advertised as a silver-bullet solution to privacy-preserving data publishing that addresses the shortcomings of traditional anonymisation techniques. The promise is that synthetic data drawn from generative models preserves the stat ...

USENIX ASSOC2022

Utility/privacy trade-off as regularized optimal transport

Etienne Patrice Boursier

Strategic information is valuable either by remaining private (for instance if it is sensitive) or, on the other hand, by being used publicly to increase some utility. These two objectives are antagonistic and leaking this information by taking full advant ...

SPRINGER HEIDELBERG2022

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Internet privacy

On the Privacy-Robustness-Utility Trilemma in Distributed Learning

Networked Signal and Information Processing: Learning by multiagent systems

Enforcing Privacy in Distributed Learning With Performance Guarantees

Protecting privacy through metadata analysis

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

Differentially Private Multi-Agent Constraint Optimization

A Modular Multimodal Architecture for Gaze Target Prediction: Application to Privacy-Sensitive Settings

Synthetic Data - Anonymisation Groundhog Day

Utility/privacy trade-off as regularized optimal transport

Generating Higher-Fidelity Synthetic Datasets with Privacy Guarantees

Graph Chatbot

Chat with Graph Search

Protecting privacy through metadata analysis

Networked Signal and Information Processing: Learning by multiagent systems

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

Differentially Private Multi-Agent Constraint Optimization

A Modular Multimodal Architecture for Gaze Target Prediction: Application to Privacy-Sensitive Settings

Synthetic Data - Anonymisation Groundhog Day

Utility/privacy trade-off as regularized optimal transport

Generating Higher-Fidelity Synthetic Datasets with Privacy Guarantees

On the Privacy-Robustness-Utility Trilemma in Distributed Learning

Enforcing Privacy in Distributed Learning With Performance Guarantees