The growth of data publishing, sharing, and mining mechanisms in various fields of industry
and science has led to an increase in the flow of data, making it an important asset that …

Differential privacy has been a de facto privacy standard in defining privacy and handling
privacy preservation. It has had great success in scenarios of local data privacy and …

Mobile Edge Computing (MEC) is a new paradigm where mobile users can offload
computation tasks to the nearby MEC server to reduce their resource consumption. Some …

We consider a platform's problem of collecting data from privacy sensitive users to estimate
an underlying parameter of interest. We formulate this question as a Bayesian-optimal …

Federated learning (FL) is a newly emerging distributed machine learning paradigm,
whereby a server can coordinate multiple clients to jointly train a learning model by using …

In this paper, we focus on the privacy preserving mechanism design for crowdsourced
Federated Learning (FL), where a requester can outsource its model training task to some …

Since the emergence of coronavirus disease–2019 (COVID-19) outbreak, every country has
implemented digital solutions in the form of mobile applications, web-based frameworks …

When training a machine learning model with differential privacy, one sets a privacy budget.
This uniform budget represents an overall maximal privacy violation that any user is willing …

Applying machine learning (ML) to sensitive domains requires privacy protection of the
underlying training data through formal privacy frameworks, such as differential privacy (DP) …

The shuffle model of local differential privacy is an advanced method of privacy amplification
designed to enhance privacy protection with high utility. It achieves this by randomly …