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Differential privacy: an information-theoretic approach to preserve privacy in datasets
, 2022 reservedIn large datasets, anonymization may be not enough to preserve privacy. In recent years to tackle privacy preservation in datasets, it has been proposed a mathematical approach called differential privacy, which is the topic of this thesis.
FABRIS, GIULIA
core
A multimodal differential privacy framework based on fusion representation learning
, 2022 Differential privacy mechanisms vary in modalities, and there have been many methods implementing differential privacy on unimodal data. Few studies focus on unifying them to protect multimodal data, though privacy protection of multimodal data is of ...
Chaoxin Cai +5 more
core +1 more source
FL-ODP: An Optimized Differential Privacy Enabled Privacy Preserving Federated Learning
IEEE Access, 2023 Privacy-preserving methods and techniques aim to safeguard the privacy of individuals and groups while facilitating data sharing for specific purposes.
Maria Iqbal +4 more
doaj +1 more source
Differential privacy, federated learning, and privacy-preserving credit risk modeling
, 2023 Fang, XiaoGiven the sheer size of the consumer credit market and the huge number of consumer credit users, credit risk modeling, or predicting delinquent (or default) probabilities of borrowers to aid financial institutions in granting and managing ...
Zhang, Hongzhe
core +1 more source
Privacy-aware eye tracking using differential privacy
, 2019 With eye tracking being increasingly integrated into virtual and augmented reality (VR/AR) head-mounted displays, preserving users’ privacy is an ever more important, yet under-explored, topic in the eye tracking community. We report a large-scale online
Andreas Bulling (5099876) +3 more
core +1 more source
Discrete Gaussian for Differential Privacy
Journal of Privacy and Confidentiality, 2022 A key tool for building differentially private systems is adding Gaussian noise to the output of a function evaluated on a sensitive dataset. Unfortunately, using a continuous distribution presents several practical challenges. First and foremost, finite computers cannot exactly represent samples from continuous distributions, and previous work has ...
Clément L. Canonne +2 more
openaire +5 more sources
Continual Learning with Differential Privacy [PDF]
, 2021 The paper will appear at ...
Pradnya Desai +3 more
openaire +2 more sources
The Protection of Data Sharing for Privacy in Financial Vision
Applied Sciences, 2022 The primary motivation is to address difficulties in data interpretation or a reduction in model accuracy. Although differential privacy can provide data privacy guarantees, it also creates problems.
Yi-Ren Wang, Yun-Cheng Tsai
doaj +1 more source
, 2021 Enterprises and governments around the world have been attempting to leverage intelligence from the community by making formally in-house database available to the public for analyzing.
Tai, Bo-Chen;Li, Szu-Chuang;Huang, Yennun
core +1 more source
Boosting and Differential Privacy [PDF]
2010 IEEE 51st Annual Symposium on Foundations of Computer Science, 2010 Boosting is a general method for improving the accuracy of learning algorithms. We use boosting to construct improved {\em privacy-preserving synopses} of an input database. These are data structures that yield, for a given set $\Q$ of queries over an input database, reasonably accurate estimates of the responses to every query in~$\Q$, even when the ...
Dwork, Cynthia +2 more
openaire +2 more sources