RAPPOR is a privacy-preserving technology for collecting statistics from end-user client software anonymously, with strong privacy guarantees. It allows the study of client data without revealing individual information. RAPPOR uses randomized response techniques to collect data and provides efficient, high-utility analysis. It enables statistics on client-side strings with strong privacy guarantees and prevents linkability of reports. The paper describes RAPPOR, its differential privacy and utility guarantees, practical deployment, and results from synthetic and real-world data. RAPPOR is designed to collect statistics on client-side values and strings, such as categories, frequencies, histograms, and other set statistics. It provides strong deniability guarantees for reporting clients and protects against privacy externalities. Unlike traditional randomized response, RAPPOR offers longitudinal privacy protection and is performed locally on the client without relying on trusted third parties. It also provides a novel decoding framework for learning statistics using hypothesis testing, least-squares solving, and LASSO regression. RAPPOR is applied in the domain of cloud service operators to collect up-to-date statistics about user activity and client-side software. It has been used in Google's Chrome Web browser to improve data sent by users who opt-in to reporting statistics. RAPPOR helps operators address the dilemma of balancing privacy and utility by collecting only necessary high-order statistics and limiting privacy risks. The RAPPOR algorithm generates a bit array of size k, encoding a "noisy" representation of the client's true value. It uses two defense mechanisms: a Permanent randomized response and an Instantaneous randomized response. The Permanent randomized response creates a "noisy" answer that is memoized and reused, while the Instantaneous randomized response reports on the "noisy" answer over time. This ensures longitudinal privacy protection and prevents tracking externalities. RAPPOR provides differential privacy guarantees, with the Permanent randomized response ensuring $ \epsilon_{\infty} $-differential privacy and the Instantaneous randomized response ensuring $ \epsilon_{1} $-differential privacy. The algorithm is modified for different scenarios, such as one-time collection, basic RAPPOR, and basic one-time RAPPOR. It is evaluated using simulations and real-world data, demonstrating its effectiveness in learning string frequencies and detecting malicious activity. RAPPOR's high-utility decoding framework allows the estimation of string frequencies and detection of common processes and Chrome homepage domains. It is effective in identifying strings with high frequencies and protecting privacy by limiting the number of correlated categories and Bloom filter hash functions reported by each client. The algorithm is robust against different attack models, including one-time, windowed, and longitudinal attackers, and provides strong privacy guarantees.RAPPOR is a privacy-preserving technology for collecting statistics from end-user client software anonymously, with strong privacy guarantees. It allows the study of client data without revealing individual information. RAPPOR uses randomized response techniques to collect data and provides efficient, high-utility analysis. It enables statistics on client-side strings with strong privacy guarantees and prevents linkability of reports. The paper describes RAPPOR, its differential privacy and utility guarantees, practical deployment, and results from synthetic and real-world data. RAPPOR is designed to collect statistics on client-side values and strings, such as categories, frequencies, histograms, and other set statistics. It provides strong deniability guarantees for reporting clients and protects against privacy externalities. Unlike traditional randomized response, RAPPOR offers longitudinal privacy protection and is performed locally on the client without relying on trusted third parties. It also provides a novel decoding framework for learning statistics using hypothesis testing, least-squares solving, and LASSO regression. RAPPOR is applied in the domain of cloud service operators to collect up-to-date statistics about user activity and client-side software. It has been used in Google's Chrome Web browser to improve data sent by users who opt-in to reporting statistics. RAPPOR helps operators address the dilemma of balancing privacy and utility by collecting only necessary high-order statistics and limiting privacy risks. The RAPPOR algorithm generates a bit array of size k, encoding a "noisy" representation of the client's true value. It uses two defense mechanisms: a Permanent randomized response and an Instantaneous randomized response. The Permanent randomized response creates a "noisy" answer that is memoized and reused, while the Instantaneous randomized response reports on the "noisy" answer over time. This ensures longitudinal privacy protection and prevents tracking externalities. RAPPOR provides differential privacy guarantees, with the Permanent randomized response ensuring $ \epsilon_{\infty} $-differential privacy and the Instantaneous randomized response ensuring $ \epsilon_{1} $-differential privacy. The algorithm is modified for different scenarios, such as one-time collection, basic RAPPOR, and basic one-time RAPPOR. It is evaluated using simulations and real-world data, demonstrating its effectiveness in learning string frequencies and detecting malicious activity. RAPPOR's high-utility decoding framework allows the estimation of string frequencies and detection of common processes and Chrome homepage domains. It is effective in identifying strings with high frequencies and protecting privacy by limiting the number of correlated categories and Bloom filter hash functions reported by each client. The algorithm is robust against different attack models, including one-time, windowed, and longitudinal attackers, and provides strong privacy guarantees.