Objective Differential privacy is a relatively new method for data privacy that has seen
growing use due its strong protections that rely on added noise. This study assesses the …

Privacy is an important consideration when sharing clinical data, which often contain
sensitive information. Adequate protection to safeguard patient privacy and to increase …

Clinical decision support system, which uses advanced data mining techniques to help
clinician make proper decisions, has received considerable attention recently. The …

Machine learning practitioners frequently seek to leverage the most informative available
data, without violating the data owner's privacy, when building predictive models …

Data mining has heralded the major breakthrough in data analysis, serving as a “super
cruncher” to discover hidden information and valuable knowledge in big data systems. For …

In this paper, we propose a multi-class classification method using kernel supports and a
dynamical system under differential privacy. For small datasets, kernel methods, such as …

Differentially private (DP) machine learning techniques are notorious for their degradation of
model utility (eg, they degrade classification accuracy). A recent line of work has …

Deep learning models leak significant amounts of information about their training datasets.
Previous work has investigated training models with differential privacy (DP) guarantees …

Label differential privacy is a relaxation of differential privacy for machine learning scenarios
where the labels are the only sensitive information that needs to be protected in the training …

Data analysis in modern times involves working with large volumes of data, including time-
series data. This type of data is characterized by its high dimensionality, enormous volume …