Illustrative examples highlighting impact of method on privacy guarantees:
1. Laplace Noise vs. Gaussian Noise: Laplace noise addition is commonly used for differential privacy. If two queries are executed sequentially, each with Laplace noise addition, the cumulative privacy loss can be calculated using sequential composition. In contrast, Gaussian noise addition, while less common, could have a different impact on privacy bounds due to its distinct noise distribution.
2. Exponential Mechanism: The exponential mechanism introduces randomness to select outputs that maximize utility. If two queries use the exponential mechanism in parallel, their combined effect is determined by parallel composition. The choice of mechanism here influences how parallel composition treats different queries.
3. Dynamic Sensitivity Mechanism: Differential privacy mechanisms like the dynamic sensitivity mechanism adapt noise levels based on query sensitivity. This dynamic behavior can interact with advanced composition theorems, influencing how tighter bounds are achieved.
4. Adaptive Noise Mechanisms: Adaptive noise mechanisms adjust the amount of noise based on the data and previous outcomes. The interplay between adaptive noise mechanisms and advanced composition techniques requires careful analysis to ensure the cumulative privacy guarantees hold.
Overall, the relationship between composition techniques and differential privacy mechanisms is intricate. The choice of mechanism directly affects the application of composition techniques and can impact the accuracy of privacy guarantees. Organizations must understand these interactions and tailor their approaches to achieve the desired level of privacy protection while optimizing utility.
Table 2.Comparative Study of Popular Composition Algorithms12, 13