Differential Privacy is a privacy-enhancing technique that allows organizations to collect and analyze data while preserving the privacy of the individuals in the dataset. Differential Privacy adds noise to the data which makes it harder for attackers to identify individual records while still maintaining the aggregate results.
With Differential Privacy, the goal is to provide accurate results without identifying individual records. To achieve this, a randomized function is used to add noise to the data. The amount of noise added to the data is controlled by a value called the privacy budget, which limits the amount of information that can be revealed about individuals in the data set.
Differential Privacy can be used in a variety of contexts, such as collecting and analyzing medical records, conducting surveys, or tracking usage patterns of mobile phones. It provides a way to share aggregate information without compromising the privacy of individuals in the dataset.
The use of Differential Privacy ensures that individuals can safely share their data without risk of their personal information being compromised. It has become an important tool for organizations dealing with sensitive data and striving to maintain a high level of privacy for their users.
Differential Privacy is implemented by applying a randomized mechanism, ℳ[D], to any information exposed from a dataset, D, to an exterior observer. The mechanism works by introducing controlled randomness or “noise” to the exposed data to protect privacy. A Differential Privacy mechanism can employ a range of techniques such as randomized response, shuffling or additive noise. The particular choice of mechanism is, essentially, tailored to the nature and quality of the information sought by the observer. The mechanism is designed to ensure information-theoretic privacy guarantee that the output of a particular analysis remains fairly the same, whether or not data about a particular individual is included.
Differential Privacy provides many benefits to organizations, including greater control and governance over data, plausible deniability to ensure people are more willing to share their sensitive data, resistance to linking attacks, and regulatory compliance. Limitations include usefulness only for large data sets, risks of privacy leaks, and a lack of end-to-end encryption; there is also no built-in ability to collaborate on multiple data sets.
