Foundations of Cryptography: Volume 1, Basic Tools

By Oded Goldreich. 2001

2,159 citations recorded by the source corpus.

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In Privacy & Security

Citations in the corpus

Secure Single-Server Aggregation with (Poly)Logarithmic Overhead (2020, methodology): “note that the proof of security uses a standard simulation-based approach [20, 28] similar to the one by Bonawitz et al.”
QUOTIENT: Two-Party Secure Neural Network Training and Prediction (2019, background): “As common in MPC, this is formalized in the simulation framework (see [17] for details).”
Privacy-Preserving Distributed Linear Regression on High-Dimensional Data (2017, methodology): “To prove security, we use a simulation-based approach [30, 47] where one argues that a protocol is secure if it can be simulated in an ideal environment in which there is no data leakage by definition.”
A Comprehensive Comparison of Multiparty Secure Additions with Differential Privacy (2015, methodology): “We use the formal security notion to present the secure multiparty computation protocols [30], [39], [55].”
A Hybrid Approach to Privacy-Preserving Federated Learning (2018, methodology): “Aggregator The honest-but-curious or semi-honest adversarial model is commonly used in the ﬁeld of SMC since its introduction in (Goldreich 2009) and application to data mining in (Lindell and Pinkas 2000).”
Generalization in anti-causal learning (2018, neutral): “Those are functions that can be computed in polynomial time, but the probability for any randomized algorithm to ﬁnd an inverse is negligible, see e.g., (Goldreich, 2001).”
Chameleon: A Hybrid Secure Computation Framework for Machine Learning Applications (2018, neutral): “The Goldreich-Micali-Wigderson (GMW) protocol is an interactive secure multi-party computation protocol [40, 41].”
Secure Multiparty Computation for Privacy-Preserving Data Mining (2009, neutral): “For those interested in going a step further, we recommend (28) for a general introduction to cryptography, and (20; 21) for a rigorous and in-depth study of the foundations of cryptography.”
Blind Justice: Fairness with Encrypted Sensitive Attributes (2018, background): “In MPC, privacy in the case of semi-honest adversaries is argued in the simulation paradigm (see (Goldreich, 2004) or (Lin-dell, 2016) for formal deﬁnitions and detailed proofs).”
Secure two-party computation in sublinear (amortized) time (2012, neutral): “We use a standard simulation-based deﬁnition of secure computation [5], comparing a real execution to that of an ideal (reactive) functionality F .”
Fairplay - Secure Two-Party Computation System (2004, methodology): “They both wish to learn & ( ' ¤ ¥ ) ¤ 1 0 for some publicly known function & , without revealing any information on their inputs that cannot be inferred from & ( ' ¤ ¥ ) ¤ 1 0 .”
CryptDB: protecting confidentiality with encrypted query processing (2011, neutral): “For example, for table t , column c , onion o , and encryption layer l , the proxy uses the key where PRP is a pseudorandom permutation (e.g., AES).”
Computationally Secure Oblivious Transfer (2004, neutral): “(See [30], [28], [37], [46], and [47] for precise definitions and various constructions.”
On the Utility of Privacy-Preserving Histograms (2004, background): “…aggregation and perturbation of contingency tables [16] [22], input perturbation (see, for example, [32] [33] [28] in the statistics literature and [3] [2] [17] from the data mining literature), imputation [29], k-anonymity [30], and cryptographic approaches to privacy (see, e.g., [20] and [21]).”