By Thomas Schneider
Secure two-party computation, referred to as safe functionality assessment (SFE), allows collectively mistrusting events, the buyer and server, to judge an arbitrary functionality on their respective deepest inputs whereas revealing not anything however the consequence. initially the method was once thought of to be too inefficient for useful privacy-preserving purposes, yet lately fast speed-up in pcs and verbal exchange networks, algorithmic advancements, automated iteration, and optimizations have enabled their program in lots of scenarios.
The writer deals an intensive assessment of the main functional and effective sleek thoughts utilized in the layout and implementation of safe computation and similar protocols. After an advent that units safe computation in its greater context of alternative privacy-enhancing applied sciences corresponding to safe channels and relied on computing, he covers the fundamentals of virtually effective safe functionality evaluate, circuit optimizations and structures, hardware-assisted garbled circuit protocols, and the modular layout of effective SFE protocols.
The objective of the author's study is to exploit set of rules engineering the way to engineer effective safe protocols, either as a commonly used device and for fixing sensible functions, and he achieves an exceptional stability among the speculation and applicability. The e-book is vital for researchers, scholars and practitioners within the sector of utilized cryptography and data protection who target to build sensible cryptographic protocols for privacy-preserving real-world applications.
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Additional info for Engineering Secure Two-Party Computation Protocols: Design, Optimization, and Applications of Efficient Secure Function Evaluation
The output bit z = [x < y] should be revealed to both. In the semi-honest case it is sufficient to consider the case where C obtains the output and forwards it to S. An example application that can be reduced to multiple invocations of secure comparison is the secure computation of the kth-ranked element of the union of two datasets held by two parties . When instantiated efficiently, Yao’s GC protocol (cf. Sect. 1) is the most efficient solution to solve the Millionaires Problem for computationally bounded players.
Definition 5 (XOR-trivial d-input gates) An XOR-trivial d-input gate G d is either a trivial d-input gate (cf. Definition 4), or it can be replaced by arbitrary many XOR gates and at most one gate with d < d inputs. XOR-non-trivial gates are not XORtrivial. When XOR gates are “for free”, an XOR-trivial d input gate has less cost than an XOR-non-trivial d-input gate: GdXORtriv < GdXORnontriv . XOR-trivial 2-input gates. For d = 2 we have as XOR-trivial gates the 6 trivial 2-input gates shown in Fig.
As all practical circuit constructions described in Sect. 3 are composed of d ≤ 3input gates and most of the 3-input gates in these constructions are XOR-trivial, we do not investigate XOR-triviality for d > 3 but leave this as an open problem. 38 3 Circuit Optimizations and Constructions Fig. 2 Replacement of XOR-trivial 3-input gates (not already listed in Fig. 3 Efficient Circuit Constructions In this section we present several frequently used circuit building blocks which benefit from the circuit optimizations of Sect.