RAPPOR (Randomized Aggregatable Privacy-Preserving Ordinal Response) is a technology designed for crowdsourcing statistics from end-user client software while maintaining strong privacy guarantees. It leverages randomized response techniques to allow the collection and analysis of client-side data without revealing individual contributions. RAPPOR ensures differential privacy, providing strong protection against various types of attacks, including one-time and longitudinal attacks. The algorithm involves two main steps: a Permanent randomized response, which creates a noisy version of the true value, and an Instantaneous randomized response, which reports this noisy value over time. This dual approach ensures both privacy and utility, allowing for efficient and accurate statistical analysis. The paper discusses the theoretical foundations of RAPPOR, its practical deployment, and evaluates its performance on synthetic and real-world data, demonstrating its effectiveness in collecting and analyzing sensitive information while preserving user privacy.RAPPOR (Randomized Aggregatable Privacy-Preserving Ordinal Response) is a technology designed for crowdsourcing statistics from end-user client software while maintaining strong privacy guarantees. It leverages randomized response techniques to allow the collection and analysis of client-side data without revealing individual contributions. RAPPOR ensures differential privacy, providing strong protection against various types of attacks, including one-time and longitudinal attacks. The algorithm involves two main steps: a Permanent randomized response, which creates a noisy version of the true value, and an Instantaneous randomized response, which reports this noisy value over time. This dual approach ensures both privacy and utility, allowing for efficient and accurate statistical analysis. The paper discusses the theoretical foundations of RAPPOR, its practical deployment, and evaluates its performance on synthetic and real-world data, demonstrating its effectiveness in collecting and analyzing sensitive information while preserving user privacy.