Cryptography from Anonymity

By Yuval Ishai, Eyal Kushilevitz, Rafail M. Ostrovsky, Amit Sahai. 2006

168 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Privacy & Security

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Citations in the corpus

The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation (2021, methodology): “For more information on the shuffled model of DP, we refer the reader to Ghazi et al. (2020b; 2021; 2020c;a); Ishai et al. (2006); Balle et al. (2019; 2020); Balcer & Cheu (2020); Balcer et al. (2021); Girgis et al. (2020).”
On Distributed Differential Privacy and Counting Distinct Elements (2020, methodology): “We remark that certain deﬁnitions could lead to the model being as powerful as the central model (in terms of achievable accuracy and putting aside communication constraints); see e.g., [29] on how to perform secure computations under a certain deﬁnition of the shuﬄe model.”
Private Counting from Anonymous Messages: Near-Optimal Accuracy with Vanishing Communication Overhead (2020, methodology): “While the shuffled model was originally studied in the field of cryptography by Ishai et al. (2006) in their work on cryptography from anonymity, it was first suggested as a framework for privacy-preserving computations by Bittau et al. (2017) in their Encode-Shuffle-Analyze architecture.”
Pure Differentially Private Summation from Anonymous Messages (2020, methodology): “While an analogous setup was ﬁrst introduced in crytpography by Ishai et al. in their work on cryptography from anonymity [IKOS06], the shuﬄed model was ﬁrst proposed for privacy-preserving computations by Bittau et al. [BEM + 17] in their Encode-Shuﬄe-Analyze architecture.”
The power of synergy in differential privacy: Combining a small curator with local randomizers (2019, methodology): “New models for di ﬀ erentially private computation have recently emerged to alleviate the (inevitable) low accuracy of the local model, of which we will discuss the shu ﬄ e model [38, 14, 19, 6, 5, 32] and the hybrid model [2].”
Improved Summation from Shuffling (2019, background): “[6] showed how to use anonymous communications as a building block for a variety of tasks, including secure computation of n-party summation.”
Scalable and Differentially Private Distributed Aggregation in the Shuffled Model (2019, methodology): “Their algorithm is similar to ours and, as they point out, a similar algorithm was used by Ishai et al. in their work on cryptography from anonymity [12].”
The Privacy Blanket of the Shuffle Model (2019, background): “[18] showing how to combine secret sharing with secure shuffling to implement distributed summation, as it allows to simulate the Laplace mechanism of the curator model.”
Encode, Shuffle, Analyze Privacy Revisited: Formalizations and Empirical Evaluation (2020, background): “Such anonymous summaries are less general than shufﬂed reports (from which they can be constructed by post-processing), but they can be practically computed using cryptographic means [10], [11], [30] and have seen formal analysis [19], [31].”
Connecting Robust Shuffle Privacy and Pan-Privacy (2020, background): “Ishai, Kushilevitz, Ostrovsky, and Sahai [27] provide a solution using additive shares:”
Searchable symmetric encryption: Improved definitions and efficient constructions (2006, unknown): “The ﬁrst, which we refer to as non-adaptive , only considers adversaries that make their search queries without taking into account the trapdoors and search outcomes of previous searches.”
Privacy-preserving multi-keyword ranked search over encrypted cloud data (2011, unknown): “, [17] has been proposed to utilize private information retrieval (PIR) technique [27], to hide the access pattern, our proposed schemes are not designed to protect the access pattern for the efficiency concerns.”
Differentially Private Aggregation in the Shuffle Model: Almost Central Accuracy in Almost a Single Message (2021, methodology): “We compare our “correlated noise” -summation protocol (Algorithms 1, 6) against known algorithms in the literature, namely the IKOS “split-and-mix” protocol (Ishai et al., 2006; Balle et al., 2020; Ghazi et al., 2020c) and the fragmented10 version of RAPPOR (Erlingsson et al.”
On the Power of Multiple Anonymous Messages (2019, unknown): “In this work, we treat the shuﬄer as a black box, but note that various eﬃcient cryptographic implementations of the shuﬄer have been considered, including onion routing, mixnets, third-party servers, and secure hardware (see, e.g., [68,16]).”
Can We Access a Database Both Locally and Privately? (2017, background): “The question of reducing the amortized computational cost of multi-query PIR was subsequently studied in [IKOS04, IKOS06].”