Differential Privacy

What is differential privacy? 👀

Differential Privacy is a cutting-edge technique gaining popularity. It involves adding random noise to queries, protecting individual privacy even in large databases.

Data Perturbation - Adding Noise to Data:

Differential Privacy adds random noise, while extracting information from the data by asking specific queries.

The added noise will obscure the actual results, preserving privacy, making it challenging to determine any specific information about a particular individual.

Weaknesses and Privacy Attacks:

Noisy Results:

Differential privacy may introduce inaccuracies in data analysis due to the added noise, impacting data utility and results.

Query Inference Attacks:

Sophisticated attackers may attempt to infer sensitive information by repeatedly querying the database and analyzing the noisy responses.

Membership Inference Attacks:

Attackers may try to determine if a specific individual’s data is part of the database by analyzing the noisy results.

Composition Attacks:

Combinations of multiple queries or data sources may potentially lead to privacy breaches, even if each individual query or dataset is differentially private.

Differential Privacy Use Cases:

• Apple: Apple uses differential privacy in their “Find My” app. The app uses aggregated and anonymized data from multiple users to detect lost devices without revealing any individual’s location or identity.

• Google: Google uses differential privacy to enhance user privacy in various services like Google Maps and Google Chrome. It helps them analyze trends and improve services while protecting individual data.

• US Census Bureau (2020): The US Census Bureau employed differential privacy to protect individuals’ privacy while providing accurate statistical information during the 2020 census.