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-## Privacy Metrics Task Force
-
-Comparable Privacy Metrics: Defining Empirical Privacy
-
-Numerous mechanisms, such as differential privacy and k-anonymity, have been developed to safeguard sensitive data. Within PATCG and across the various private attribution solutions, both proposed and implemented, we’ve seen a number of proposals employing different techniques to create user privacy.  While each method offers unique protection mechanisms, comparing their efficacy in real-world scenarios remains challenging. The distinct nuances of each can make it hard to understand the tradeoffs we may be making in privacy or utility. This highlights the need for a way to consistently evaluate and compare the privacy levels across different methods. Our Task Force goal is to investigate, develop and compare metrics for measuring the privacy-loss of Ad Measurement systems.
-
-In summary, our goal is to develop empirical privacy metrics that should:
-1. Provide a clear and intuitive explanation of the privacy we’ve created, making it more comprehensible for all stakeholders.
-2. Offer a standardized metric or framework for evaluating the efficacy of different privacy models in real-world scenarios and under specific threat models
-3. Facilitate easier comparisons between various protection mechanisms, assisting in choosing the most appropriate model and parameters for datasets.
-4. Promote transparency in the application of privacy measures, ensuring that all users can understand and trust the privacy protections in place.
+Notes of Task Force meetings, as well as periodic update summarys of products and future plans for distribution.