Multilayer feedforward networks are universal approximators
Neural Networks
Speech recognition using neural networks
Speech recognition using neural networks
Journal of the ACM (JACM)
Parallel evolutionary training algorithms for “hardware-friendly“ neural networks
Natural Computing: an international journal
Priced Oblivious Transfer: How to Sell Digital Goods
EUROCRYPT '01 Proceedings of the International Conference on the Theory and Application of Cryptographic Techniques: Advances in Cryptology
Oblivious Polynomial Evaluation and Oblivious Neural Learning
ASIACRYPT '01 Proceedings of the 7th International Conference on the Theory and Application of Cryptology and Information Security: Advances in Cryptology
Cryptographic techniques for privacy-preserving data mining
ACM SIGKDD Explorations Newsletter
Non-Interactive CryptoComputing For NC1
FOCS '99 Proceedings of the 40th Annual Symposium on Foundations of Computer Science
Universal circuits (Preliminary Report)
STOC '76 Proceedings of the eighth annual ACM symposium on Theory of computing
Fairplay—a secure two-party computation system
SSYM'04 Proceedings of the 13th conference on USENIX Security Symposium - Volume 13
Oblivious neural network computing via homomorphic encryption
EURASIP Journal on Information Security
How to generate and exchange secrets
SFCS '86 Proceedings of the 27th Annual Symposium on Foundations of Computer Science
An Efficient Protocol for Secure Two-Party Computation in the Presence of Malicious Adversaries
EUROCRYPT '07 Proceedings of the 26th annual international conference on Advances in Cryptology
Improved Garbled Circuit: Free XOR Gates and Applications
ICALP '08 Proceedings of the 35th international colloquium on Automata, Languages and Programming, Part II
A Practical Universal Circuit Construction and Secure Evaluation of Private Functions
Financial Cryptography and Data Security
Financial Cryptography and Data Security
Implementing Two-Party Computation Efficiently with Security Against Malicious Adversaries
SCN '08 Proceedings of the 6th international conference on Security and Cryptography for Networks
Efficient two party and multi party computation against covert adversaries
EUROCRYPT'08 Proceedings of the theory and applications of cryptographic techniques 27th annual international conference on Advances in cryptology
Practical Secure Evaluation of Semi-private Functions
ACNS '09 Proceedings of the 7th International Conference on Applied Cryptography and Network Security
Garbled circuits for leakage-resilience: hardware implementation and evaluation of one-time programs
CHES'10 Proceedings of the 12th international conference on Cryptographic hardware and embedded systems
Constant-Round private function evaluation with linear complexity
ASIACRYPT'11 Proceedings of the 17th international conference on The Theory and Application of Cryptology and Information Security
Hi-index | 0.00 |
Secure Evaluation of Private Functions (PF-SFE) allows two parties to compute a private function which is known by one party only on private data of both. It is known that PF-SFE can be reduced to Secure Function Evaluation (SFE) of a Universal Circuit (UC). Previous UC constructions only simulated circuits with gates of d = 2 inputs while gates with d 2 inputs were decomposed into many gates with 2 inputs which is inefficient for large d as the size of UC heavily depends on the number of gates. We present generalized UC constructions to efficiently simulate any circuit with gates of d *** 2 inputs having efficient circuit representation. Our constructions are non-trivial generalizations of previously known UC constructions. As application we show how to securely evaluate private functions such as neural networks (NN) which are increasingly used in commercial applications. Our provably secure PF-SFE protocol needs only one round in the semi-honest model (or even no online communication at all using non-interactive oblivious transfer) and evaluates a generalized UC that entirely hides the structure of the private NN. This enables applications like privacy-preserving data classification based on private NNs without trusted third party while simultaneously protecting user's data and NN owner's intellectual property.