Concept
Data re-identification
Related publications (28)
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 ...
Wiley2024Differentially 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 ...
Dordrecht2024PRO-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 Inc2024Differentially 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 ...
ACM2022Utility/privacy trade-off as regularized optimal transport
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 HEIDELBERG2022TEE-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 ...
2022Generating 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 ...
MDPI2022Open-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 ...
IEEE2021An 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 ...
2021Private and Secure Distributed Learning
The ever-growing number of edge devices (e.g., smartphones) and the exploding volume of sensitive data they produce, call for distributed machine learning techniques that are privacy-preserving. Given the increasing computing capabilities of modern edge de ...
EPFL2020