Algorithm Poisoning: Post-Lab

Summary of Findings

1. Model Inversion Capability: Prior to implementing DF, the adversary was able to reconstruct the images with a decent degree of accuracy.

2. Impact of Differential Privacy: Using DF, the adversary's ability to reconstruct the target model's inputs was significantly reduced.

3. Privacy-Utility Trade-off: The experiment demonstrated the trade-off between the training of machine learning models and the inherent privacy concerns, particularly in federated learning environments.

4. Quantitative Privacy Measures: The privacy budget (epsilon) provided a quantitative metric for privacy.

5. Adversary's Adaptation: Though this DP implementation was successful in mitigating the ability of the adversary model to reconstruct inputs, it is always possible for adversary strategies to adapt. This highlights the need for continued research in this area.

Implications for Privacy

• Robustness of Privacy Measures: In this example, the implemented DP displayed robustness in its ability to protect individual data points within a dataset; however, this needs to be constantly monitored to prevent the evolution of threats.

• Ethical and Regulatory Considerations: Particularly for spaces in which sensitive information is handled, these results demonstrate the importance of preserving privacy to meet ethical and regulatory standards.

• Need for Ongoing Research: As ML models become more complex and more interwoven into all aspects of daily life, it is critical that continuous research is done into the best possible refences against the attacks that are sure to come on these systems.