Differential Privacy is a framework designed to ensure that the privacy of individuals in a dataset is protected when statistical analyses are published. It works by adding a certain amount of noise to the data or to the statistical queries made on the data, such that the output does not significantly compromise any individual's privacy. This approach allows researchers and companies to glean useful insights from data and share them without risking the exposure of sensitive individual information. It's especially significant in fields where data privacy is crucial, such as healthcare and finance, enabling the use of data while adhering to privacy regulations like GDPR in Europe.
Historical overview: The concept of Differential Privacy was formally introduced by Cynthia Dwork and her colleagues around 2006. It gained prominence as concerns over data privacy grew with the increasing availability of large datasets and the capabilities to process them.
Key contributors: Cynthia Dwork is one of the key figures in the development of Differential Privacy, contributing foundational research that has shaped the field. Her work, along with that of her collaborators, laid the theoretical groundwork for how privacy can be quantified and protected in the context of statistical databases.