Publications related to Data re-identification

Fast refacing of MR images with a generative neural network lowers re-identification risk and preserves volumetric consistency

Jean-Philippe Thiran, Tobias Kober, Bénédicte Marie Maréchal, Jonas Richiardi

With the rise of open data, identifiability of individuals based on 3D renderings obtained from routine structural magnetic resonance imaging (MRI) scans of the head has become a growing privacy concern. To protect subject privacy, several algorithms have ...

Wiley2024

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 ...

Dordrecht2024

PRO-Face C: Privacy-Preserving Recognition of Obfuscated Face via Feature Compensation

Touradj Ebrahimi, Lin Yuan, Xiao Pu, Yao Zhang, Hongbo Li

The advancement of face recognition technology has delivered substantial societal advantages. However, it has also raised global privacy concerns due to the ubiquitous collection and potential misuse of individuals' facial data. This presents a notable par ...

Ieee-Inst Electrical Electronics Engineers Inc2024

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

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

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

Open-Set Person Re-Identification through Error Resilient Recurring Gallery Building

Touradj Ebrahimi, Evgeniy Upenik

In person re-identification, individuals must be correctly identified in images that come from different cameras or are captured at different points in time. In the open-set case, the above needs be achieved for people who have not been previously recognis ...

IEEE2021

An End-to-End Data Pipeline for Managing Learning Analytics

Denis Gillet, Juan Carlos Farah, Sandy Ingram, Joana Catarina Soares Machado

Despite the importance of learning analytics in digital education, there is limited support for researchers in education to generate, access, and share experimental data while complying with ethical and privacy legislation. We propose a set of related tool ...

2021

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Data re-identification

Fast refacing of MR images with a generative neural network lowers re-identification risk and preserves volumetric consistency

Differentially private multi-agent constraint optimization

PRO-Face C: Privacy-Preserving Recognition of Obfuscated Face via Feature Compensation

Differentially Private Multi-Agent Constraint Optimization

Utility/privacy trade-off as regularized optimal transport

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

Generating Higher-Fidelity Synthetic Datasets with Privacy Guarantees

Open-Set Person Re-Identification through Error Resilient Recurring Gallery Building

An End-to-End Data Pipeline for Managing Learning Analytics

Private and Secure Distributed Learning

Graph Chatbot

Chat with Graph Search

Differentially Private Multi-Agent Constraint Optimization

Fast refacing of MR images with a generative neural network lowers re-identification risk and preserves volumetric consistency

PRO-Face C: Privacy-Preserving Recognition of Obfuscated Face via Feature Compensation

Open-Set Person Re-Identification through Error Resilient Recurring Gallery Building

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

Private and Secure Distributed Learning

An End-to-End Data Pipeline for Managing Learning Analytics

Differentially private multi-agent constraint optimization

Utility/privacy trade-off as regularized optimal transport

Generating Higher-Fidelity Synthetic Datasets with Privacy Guarantees