What Is Differential Privacy?

Differential Privacy is an important research branch in AI. It has brought a fundamental change to AI, and continues to morph the AI development. That’s the motive for me to write the series of articles on Differential Privacy and Privacy-preserving Machine Learning (ML). On Differentiated Privacy, Dataman published “You Can Be Identified by Your Netflix Watching History” and “What Is Differential Privacy?” and more to come in the future.

(A) Let’s Start with Data Privacy

Any private information in digital form is at risk. When you open your Facebook account, you gave them your personal identifiable information (PII) such as name, address, date of birth, marital status, etc. This is sensitive and may be compromised.

Even with anonymity of the PII, Your true identification still can be revealed and at risk. In my previous post “You Can Be Identified by Your Netflix Watching History”, two researchers show individuals can be identified by their anonymous Netflix watching history. With all the digital data about you in the modern era — shopping data, medical data, GPS data, your true identification is still at risk even if you use fake user names.

Sensitive survey questions are private data too. Suppose a researcher needs to know the percentage of males that ever had sex with a prostitute. He surveys random people. Does…