What does differential privacy aim to ensure?

Differential privacy is a statistical technique that seeks to provide a formal framework for ensuring that the privacy of individuals is maintained when their data is included in a dataset. It achieves this by adding a controlled amount of randomness to the data or to the results of queries made on the data. The core idea is to ensure that the risk of exposing an individual's information is minimized, even if an attacker has some background knowledge about the dataset.

The primary goal of differential privacy is to protect individual privacy, meaning it allows researchers or organizations to analyze and derive insights from large sets of data without compromising the privacy of individual data points. This is crucial in today's data-driven world, where data can be sensitive and personally identifiable information needs careful handling.

In contrast, increased data accessibility relates to how easily data can be accessed and used, which may not prioritize privacy at all. Guaranteed data accuracy focuses on ensuring that the data reflects true values without errors, but this doesn't necessarily address how individual pieces of data might expose personal information. Faster data processing speeds concern the efficiency of data handling and do not relate to privacy concerns directly. Thus, the emphasis on the protection of individual privacy within datasets makes this answer the most accurate reflection of the aims of differential privacy.