Differential privacy

Differential Privacy, commonly abbreviated as DP, is a branch of mathematics and computer science that aims to protect the privacy of individuals in databases of data. It is used to achieve a strong notion of data privacy for a user in a database of research and other data. It is used in many of the popular programs that assess different data, such as government agencies, healthcare organizations, and many online platforms. It has become more popular as the value of privacy grows downstream of personal data.

Differential privacy, commonly used in cryptography and other techniques, aims to prevent possible attacks on data and to protect individual’s right to privacy. It works to give no single data point in a database the ability to match with an individual, even if an attacker had access to the entire database. In other words, it pertains to obfuscating individual data points so that it gives no insight into sensitive information, like an individual’s identity or records.

The implementation of differential privacy typically requires a carefully chosen combination of techniques from other privacy and security methods, like encryption, anonymization, pseudonymization, data harmonization and others. Differential privacy is used in many applications due to its ability to protect personal or sensitive data, while still allowing researchers to make valid conclusions from the collected data. A key aspect of differential privacy is that it limits the amount of privacy risk that a person may have when their data is chosen to be used for research.

Differential privacy is commonly associated with artificial intelligence projects and Big Data analysis, but it is also used elsewhere, such as in healthcare systems, election surveys, banking, and other data-collecting organizations.

In the end, Differential Privacy gives individuals the ability to protect their sensitive data, and lets research from Big Data be conducted without the fear of unintended consequences. It is a much needed security measure to ensure the safety and privacy of individuals in a world that is increasingly data-driven.