What is Differential Privacy ?

Differential privacy is a mathematical definition of privacy, originally introduced in a 2006 paper by Cynthia Dwork. It states that a data release from a dataset is considered private if the likelihood of any given person’s inclusion in the release is roughly the same, regardless of whether or not that person is actually in the dataset. In other words, each person’s privacy should not be able to be compromised by their inclusion in the data release.

In cryptography, differential privacy is a privacy protocol that allows users to share information while still guaranteeing that no individual can learn information that would allow them to identify all of the participants in a communication.

The basic idea behind differential privacy is that it is possible to divide a message into pieces, where each piece is treated as a private individual. This allows different participants to share pieces of the message without fear that the other participants will be able to learn information that would allow them to identify all of the participants. Data ownership is a key component of differential privacy, as it allows individuals to own their data and control who has access to it

The key to differential privacy is the use of cryptographic hash functions. Hash functions are used to create a unique fingerprint for each piece of data. This fingerprint is used to create a cryptographic hash value. The hash value is used to identify the data, but it does not reveal any information about the data.

Source : OpenMined

Differential privacy protocol allows individuals to share data without disclosing any information that could allow attackers to identify them. The key to differential privacy is the use of “privacy-preserving” algorithms. These algorithms prevent attackers from learning any information that could allow them to identify the individuals involved in the data sharing.

Differential privacy has become a hot topic in the past few years as companies increasingly collect and store large amounts of data. Many companies say they are using differential privacy to protect user data, but there is often skepticism about how well they are actually doing so. In 2018, Apple made headlines when it announced that it would be using differential privacy to collect data from users’ iPhones. However, some privacy advocates criticized the company for not being transparent enough about how it was using the technique.

Differential privacy has the potential to revolutionize the way we share data. It allows us to share data without sacrificing our privacy, and allows us to control who has access to it. differential privacy is a powerful security technique that has the potential to protect the privacy of individuals everywhere.

Differential privacy is a powerful tool for protecting privacy, but it is not without its limitations. One challenge is that it can be difficult to achieve a high level of privacy while still allowing for useful data to be collected. Another challenge is that the technique is still relatively new and there is not yet a lot of agreement on the best way to implement it.