Sharemind: A Framework for Fast Privacy-Preserving Computations

By Dan Bogdanov, Sven Laur, Jan Willemson. 2008

836 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

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Citations in the corpus

QUOTIENT: Two-Party Secure Neural Network Training and Prediction (2019, background): “In this work, we focus on the twoparty computation setting, which excludes solutions that rely on a majority of honest parties [6, 40].”
Federated Machine Learning (2019, background): “One of the state-of-the-art SMC framework is Sharemind [8].”
Privacy-Preserving Distributed Linear Regression on High-Dimensional Data (2017, methodology): “Several generic techniques have been developed for MPC including Yao’s garbled circuits protocol [70], the GMW protocol [31], and other secret-sharing based techniques [7, 41, 50].”
Chameleon: A Hybrid Secure Computation Framework for Machine Learning Applications (2018, methodology): “(iii) All operations in A-SS are performed in the ring Z2l which is proven to be secure in [18].”
New Primitives for Actively-Secure MPC over Rings with Applications to Private Machine Learning (2019, background): “Of particular interest is Sharemind [32], as this scheme also allows mixing boolean and arithmetic operations.”
From Keys to Databases - Real-World Applications of Secure Multi-Party Computation (2018, methodology): “The design was proposed in a Master’s thesis in 2007 [2] and refined in follow-up publications [3,4].”
CrypTen: Secure Multi-Party Computation Meets Machine Learning (2021, background): “The proof of this lemma follows trivially from [9, 11, 21, 24], and is given in the appendix.”
Secure Multiparty Sorting Protocols with Covert Privacy (2016, background): “the Sharemind SMC platform [6], which is the basis of the work reported here, has three computing parties, one of which may be passively corrupted.”
A survey on federated learning: challenges and applications (2022, background): “Advantage Disadvantage Secure multi-party computing [35] Allow users to process data without seeing it Throughout the calculation process, parties always have absolute control over the data they own Cause huge communication cost in multi-party interaction [36] Use SMC technology to achieve a robust…”
Secure Multiparty Computation from SGX (2017, methodology): “Sharemind [10] is a secure service platform for data collection and analysis, employing a 3-party additive secret sharing scheme and provably secure protocols in the honest-but-curious security model with no more than one passively corrupted party.”
Combining Differential Privacy and Secure Multiparty Computation (2015, methodology): “The Sharemind SMC framework [4, 5] that we are using employs protocols working on data secret-shared between several computing parties.”
Smart meter aggregation via secret-sharing (2013, background): “Other general-purpose implementations of secret-sharing schemes and multi-party computation [2, 18] also embed integrity-protection mechanisms, and sometimes support faster comparisons.”
High-Throughput Semi-Honest Secure Three-Party Computation with an Honest Majority (2016, methodology): “We remark that our method for generating correlated randomness can be used to reduce the number of elements sent in the Sharemind protocol from 5 to 2 and to reduce the number of communication rounds to 1.”
SEPIA: Privacy-Preserving Aggregation of Multi-Domain Network Events and Statistics (2010, methodology): “Sharemind [6] is another interesting MPC framework using additive secret sharing to implement multiplications and greater-or-equal (GTE) comparison.”
Obliv-C: A Language for Extensible Data-Oblivious Computation (2015, methodology): “int main (int argc, char ∗argv[]) { ProtocolDesc pd; ProtocolIO io; int p = (argv[1] == ’1’ ? 1 : 2); sscanf(argv[2], "%d", &io.”
Falcon: Honest-Majority Maliciously Secure Framework for Private Deep Learning (2020, background): “Though MPC is not a broadly deployed technology yet, the 3PC adversarial model has enjoyed adoption [31–33] due to their efficiency and simplicity of protocols.”
UnLynx: A Decentralized System for Privacy-Conscious Data Sharing (2017, background): “based on secret sharing (SS) [3, 14, 26, 49] or secure multiparty computation (SMPC) [11, 28, 33, 40, 42, 50] have intrinsic limitations in terms of control over data and scalability.”
SoK: General Purpose Compilers for Secure Multi-Party Computation (2019, neutral): “The Sharemind MPC platform explicitly defines three parties: clients, who input values; servers, who define and run the secure computation; and outputs, who receive the output of the computation.”