Randomized response: a survey technique for eliminating evasive answer bias.

By S. Warner. 1965

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Citations in the corpus

Advances and Open Problems in Federated Learning (2019, methodology): “This model is referred to as the local model of differential privacy (LDP) [406, 229].”
Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data (2016, background): “Erlingsson et al. (2014) showed that randomized response, introduced by Warner (1965), can protect crowd-sourced data collected from software users to compute statistics about user behaviors.”
Practical and Private (Deep) Learning without Sampling or Shuffling (2021, background): “This improvement is significant because it distinguishes centrally private OCO from locally private (Warner, 1965; Evfimievski et al., 2003; Kasiviswanathan et al., 2008) OCO4.”
The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation (2021, background): “More recently, there has been a great interest in the local model of DP (Kasiviswanathan et al., 2011; Evfimievski et al., 2004; Warner, 1965) where the data is perturbed on the client side before it is collected by a service provider.”
On Distributed Differential Privacy and Counting Distinct Elements (2020, background): “The most well-studied such setting is the local model [31] (also [44]), denoted henceforth by DP local , where the privacy guarantee is enforced at each user’s output (i.e., the protocol transcript).”
Shuffled Model of Federated Learning: Privacy, Communication and Accuracy Trade-offs (2020, background): “The challenge is that the traditional privacy approach to the learning problem uses local diﬀerential privacy (LDP) [War65, ESAG04, DJW13, BNO08, KLN + 11], which is known to give poor learning performance [DJW13,KLN + 11,KBR16].”
Breaking the Communication-Privacy-Accuracy Trilemma (2020, methodology): “In particular, local differential privacy (LDP) [3], [4], [5], [6] is a widely adopted privacy paradigm, which guarantees that the outcome from a privatization mechanism will not release too much individual information statistically.”
Private Counting from Anonymous Messages: Near-Optimal Accuracy with Vanishing Communication Overhead (2020, methodology): “To circumvent this limitation, several works have studied the local model of DP (Kasiviswanathan et al., 2008) (also (Warner, 1965)), which enforces the more stringent constraint that each message sent from a user device to the server is private.”
Secure Federated Submodel Learning (2019, background): “1) Randomized Response: Randomized response, due to Warner in 1965 [61], is a survey technique in the social sciences to collect statistical information about illegal, embarrassing, or sensitive topics, where the respondents want to preserve privacies of their answers.”
Federated Heavy Hitters Discovery with Differential Privacy (2019, background): “The local model of DP [41, 27, 33] addresses the above concerns by requiring users to perturb their data locally before sharing it with a service provider.”
Protection Against Reconstruction and Its Applications in Private Federated Learning (2018, methodology): “In the second step, we employ randomized response [65] over k outcomes.”
The Power of the Hybrid Model for Mean Estimation (2018, methodology): “However, DP mean estimation of distributions under both the TCM and LM has been studied since the models’ introductions [17, 21, 56], and continues to be actively studied to this day [2, 15, 18, 20, 22, 28, 29, 32, 37, 39–42].”
Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity (2018, methodology): “Randomized response , a disclosure control technique from the 1960s [War65], is a particularly simple technique for designing single-round LDP mechanisms.”
Distributed Differential Privacy via Shuffling (2018, background): “In the local model [33, 18, 14], each individual applies a differentially private algorithm locally to their data and shares only the output of the algorithm—called a report or response—with a server that aggregates users’ reports.”
Tight Lower Bounds for Locally Differentially Private Selection (2018, methodology): “d in a differentially private way using n= O(logd "2 + logd), and this bound is tight [BU17, SU17]. In contrast, the natural local model protocol for this algorithm is to use randomized response [War65] to estimate each coordinate of , which requires n= (dlogd 2"2 ) samples—an exponentiall”
Collecting Telemetry Data Privately (2017, methodology): “Our mechanisms are inspired by those in [19, 8, 7, 4], but allow for considerably simpler descriptions and implementations.”
Prochlo: Strong Privacy for Analytics in the Crowd (2017, background): “However, for fragments, or other small, known data domains, users may simply probabilistically report random values instead of true ones—a textbook form of randomized response [75].”