Differential privacy for deep and federated learning: A survey
A El Ouadrhiri, A Abdelhadi - IEEE access, 2022 - ieeexplore.ieee.org
Users' privacy is vulnerable at all stages of the deep learning process. Sensitive information
of users may be disclosed during data collection, during training, or even after releasing the …
of users may be disclosed during data collection, during training, or even after releasing the …
Trustworthy ai: From principles to practices
The rapid development of Artificial Intelligence (AI) technology has enabled the deployment
of various systems based on it. However, many current AI systems are found vulnerable to …
of various systems based on it. However, many current AI systems are found vulnerable to …
Differential privacy techniques for cyber physical systems: a survey
MU Hassan, MH Rehmani… - … Communications Surveys & …, 2019 - ieeexplore.ieee.org
Modern cyber physical systems (CPSs) has widely being used in our daily lives because of
development of information and communication technologies (ICT). With the provision of …
development of information and communication technologies (ICT). With the provision of …
Synthetic Data--what, why and how?
This explainer document aims to provide an overview of the current state of the rapidly
expanding work on synthetic data technologies, with a particular focus on privacy. The …
expanding work on synthetic data technologies, with a particular focus on privacy. The …
Differentially private data publishing and analysis: A survey
Differential privacy is an essential and prevalent privacy model that has been widely
explored in recent decades. This survey provides a comprehensive and structured overview …
explored in recent decades. This survey provides a comprehensive and structured overview …
Petuum: A new platform for distributed machine learning on big data
How can one build a distributed framework that allows efficient deployment of a wide
spectrum of modern advanced machine learning (ML) programs for industrial-scale …
spectrum of modern advanced machine learning (ML) programs for industrial-scale …
Privacy enhancing technologies for solving the privacy-personalization paradox: Taxonomy and survey
Personal data are often collected and processed in a decentralized fashion, within different
contexts. For instance, with the emergence of distributed applications, several providers are …
contexts. For instance, with the emergence of distributed applications, several providers are …
[PDF][PDF] Dependence makes you vulnberable: Differential privacy under dependent tuples.
C Liu, S Chakraborty, P Mittal - NDSS, 2016 - princeton.edu
Differential privacy (DP) is a widely accepted mathematical framework for protecting data
privacy. Simply stated, it guarantees that the distribution of query results changes only …
privacy. Simply stated, it guarantees that the distribution of query results changes only …
Sok: differential privacies
D Desfontaines, B Pejó - arXiv preprint arXiv:1906.01337, 2019 - arxiv.org
Shortly after it was first introduced in 2006, differential privacy became the flagship data
privacy definition. Since then, numerous variants and extensions were proposed to adapt it …
privacy definition. Since then, numerous variants and extensions were proposed to adapt it …
Correlated differential privacy: Hiding information in non-IID data set
Privacy preserving on data mining and data release has attracted an increasing research
interest over a number of decades. Differential privacy is one influential privacy notion that …
interest over a number of decades. Differential privacy is one influential privacy notion that …