Protocols for Secure Computations (Extended Abstract)

By A. Yao. 1982

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Citations in the corpus

Honeycrisp: large-scale differentially private aggregation without a trusted core (2019, methodology): “Our second insight is that, in this model, we can use a cocktail of cryptographic techniques – specifically, multiparty computation (MPC) [75] and a form of homomorphic encryption – to efficiently implement global differential privacy at scale, which enables us to leverage the sparse vector techniqu”
VPriv: Protecting Privacy in Location-Based Vehicular Services (2009, methodology): “However, these papers employ multiparty computations as a black box, relying on general results from the literature for reducing arbitrary functions to secure protocols [34].”
More Efficient Constant-Round Multi-Party Computation from BMR and SHE (2016, methodology): “Secure computation has been studied since the 1980s, and it has been shown that any functionality can be securely computed, even in the presence of a dishonest majority [28,14].”
Rmind: A Tool for Cryptographically Secure Statistical Analysis (2016, background): “SMC protocols can be built using homomorphic encryption [15], [16], garbled circuits [17] and secret sharing [18], [19], among others.”
Privacy-Preserving Classification on Deep Neural Network (2017, methodology): “To securely evaluate each threshold activation function processing, Yao’s solution to the millionaire’s problem [49] is suggested.”
Computational Differential Privacy (2009, background): “There is rich literature considering notions of security for mpc/two-pc simulation-based security [26,27], super-polynomial simulation [28,29], input indistinguishable computation [30], etc.”
Combining Differential Privacy and Secure Multiparty Computation (2015, methodology): “including garbled circuits [37] and homomorphic encryption [9].”
SoK: Privacy on Mobile Devices – It’s Complicated (2016, background): “One particularly promising way to do this is through the use of advanced cryptographic techniques such as homomorphic encryption [167], secure multi-party computation [168, 169], and functional encryption [170].”
Privacy-Preserving Face Recognition (2009, methodology): “Basic concepts for secure computations were introduced by Yao [38].”
The Bitcoin Backbone Protocol: Analysis and Applications (2015, unknown): “A notable example is secure MPC [46, 73] with properties such as fairness and guaranteed output delivery (current works in this direction, for example, [2, 12, 13, 54–56] assume an idealized version of the Bitcoin system).”
: Privacy on Mobile Devices – It ’ s Complicated (2016, background): “One particularly promising way to do this is through the use of advanced cryptographic techniques such as homomorphic encryption [167], secure multi-party computation [168, 169], and functional encryption [170].”
Secure Multiparty Computation (2011, unknown): “Yao, A.”
The Limits of Two-Party Differential Privacy (2010, unknown): “Such a function proves that the answer to Question 2 on the gap between two-party computational and information-theoretic differential privacy is linear: On the one hand, by property (1) the function can be approximated with differential privacy by a trusted third party (and thus in the computationa”
Secure and practical outsourcing of linear programming in cloud computing (2011, methodology): “Another large existing list of work that relates to (but is also significantly different from) ours is Secure Multi-party Computation (SMC), first introduced by Yao [11].”
Two Party Distribution Testing: Communication and Security (2018, methodology): “The latter includes various techniques for secure two-party computation ([Yao82] and followup work), fully homomorphic encryption ([Gen09] and followup work), or homomorphic secret sharing ([BGI16, BGI17]).”