D2P: Distance-Based Differential Privacy in Recommenders

Publications associées (30)

À propos
Confidentialité
Mentions légales

Graph Chatbot

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

Dynamic Personalized Ranking

Jérémie Rappaz

Personalized ranking methods are at the core of many systems that learn to produce recommendations from user feedbacks. Their primary objective is to identify relevant items from very large vocabularies and to assist users in discovering new content. These ...

EPFL2022

A large scale study of reader interactions with images on Wikipedia

Tiziano Piccardi, Daniele Rama

Wikipedia is the largest source of free encyclopedic knowledge and one of the most visited sites on the Web. To increase reader understanding of the article, Wikipedia editors add images within the text of the article's body. However, despite their widespr ...

SPRINGER2022

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

TEE-based decentralized recommender systems: The raw data sharing redemption

Anne-Marie Kermarrec, Rafael Pereira Pires, Akash Balasaheb Dhasade, Nevena Dresevic

Recommenders are central in many applications today. The most effective recommendation schemes, such as those based on collaborative filtering (CF), exploit similarities between user profiles to make recommendations, but potentially expose private data. Fe ...

2022

Flying in Private Mode: Understanding and Improving the Privacy ICAO Address Program

Guillaume Michel Le Bars

Location privacy of aircraft has recently gained attention as air traffic management was modernized using novel surveillance technologies. Business aviation circles and various military and government entities voiced serious concerns about automated and ub ...

AMER INST AERONAUTICS ASTRONAUTICS2021

Smart Toys plus plus : Exploiting the Social Connectedness for Playing and Learning

Ayberk Özgür, Arzu Güneysu Özgür

Under the umbrella of smart toys, a myriad of interactive systems have addressed a variety of scenarios considering entertainment, education, sustainability, social and environmental learning through play. Tangibles and small toy robots prevail; but intera ...

ASSOC COMPUTING MACHINERY2021

Graph-Homomorphic Perturbations For Private Decentralized Learning

Ali H. Sayed, Stefan Vlaski

Decentralized algorithms for stochastic optimization and learning rely on the diffusion of information through repeated local exchanges of intermediate estimates. Such structures are particularly appealing in situations where agents may be hesitant to shar ...

IEEE2021

A methodology to design measurement systems when multiple model classes are plausible

Ian Smith, Numa Joy Bertola, Sai Ganesh Sarvotham Pai

The management of existing civil infrastructure is challenging due to evolving functional requirements, aging and climate change. Civil infrastructure often has hidden reserve capacity because of conservative approaches used in design and during constructi ...

2021

Data-Aware Privacy-Preserving Machine Learning

Aleksei Triastcyn

In this thesis, we focus on the problem of achieving practical privacy guarantees in machine learning (ML), where the classic differential privacy (DP) fails to maintain a good trade-off between user privacy and data utility. Differential privacy guarantee ...

EPFL2020

Page 3 sur 3