Enhancing Privacy in Attribution Metrics

Different companies like Apple, Google, and Meta have various approaches to privacy-enhancing attribution using techniques like differential privacy and algorithms to protect individual identities. Comparing these approaches is challenging due to their differences. Working groups aim to develop metrics for comparison that are not only understandable to privacy professionals but also to laypeople. One proposed metric is the probability of being re-identified in a dataset, providing a clear and relatable measure of privacy protection.