## [Algorithmic Foundations] #6. A game theoretic view of query release

I describe the interaction between data stewards and data analysts regarding query release. I analyse different strategies and explain the considerations.

I describe the interaction between data stewards and data analysts regarding query release. I analyse different strategies and explain the considerations.

I present mathematical guarantees for how differential privacy provides truthfulness, limited incentives to lie, and collusion resistance in a game-theoretic context. These properties provide mechanism designers with powerful tools to control the strategic behaviour of agents while preserving privacy.

I explain the Laplace distribution and its mechanism.

I provide basic probability tools that are essential for understanding and proving the effectiveness of differential privacy mechanisms.

I discuss whether differential privacy can be free from harm and its security.

I explain a proposal for post-processing differential privacy, and a theorem for size k.

I explain the concepts among the algorithmic foundations of differential privacy.

Mechanism Design via Differential Privacy

Algorithmic Game Theory

Randomized response techniques have been developed and applied, allowing researchers to gather sensitive information without compromising respondent privacy or truthfulness.