Engineering Secure Two-Party Computation Protocols: Design, by Thomas Schneider

By Thomas Schneider

Secure two-party computation, known as safe functionality evaluate (SFE), allows collectively mistrusting events, the customer and server, to guage an arbitrary functionality on their respective inner most inputs whereas revealing not anything however the end result. initially the method used to be thought of to be too inefficient for sensible privacy-preserving purposes, yet in recent times fast speed-up in pcs and communique networks, algorithmic advancements, automated iteration, and optimizations have enabled their software in lots of scenarios.

The writer bargains an intensive assessment of the main sensible and effective smooth concepts utilized in the layout and implementation of safe computation and comparable protocols. After an creation that units safe computation in its greater context of alternative privacy-enhancing applied sciences comparable to safe channels and depended on computing, he covers the fundamentals of essentially 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 learn is to take advantage of set of rules engineering ways to engineer effective safe protocols, either as a standard software and for fixing functional functions, and he achieves an outstanding stability among the speculation and applicability. The booklet is vital for researchers, scholars and practitioners within the zone of utilized cryptography and knowledge safety who goal to build functional cryptographic protocols for privacy-preserving real-world applications.

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Extra info for Engineering Secure Two-Party Computation Protocols: Design, Optimization, and Applications of Efficient Secure Function Evaluation

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Optimization. , G n in topological order 1: procedure Optimize(C) 2: Step 1: Eliminate constant gates 3: for all constant non-output gates G j = ()[v j ] do 4: for all gates G i having the output of G j as ki -th input do 5: eliminateConstInput(G i , ki , v j ) 6: end for 7: end for 8: end Step 9: Step 2: Eliminate 1-input non-output gates 10: for all non-output gates G i with di = 1 do 11: integrateInSucc(G i ) 12: end for 13: end Step 14: Step 3: Eliminate 1-input output gates 15: for all output gates G i with di = 1 do 16: {G p } ← pr ed(G i ) 17: if G i is not input gate and fanout(G p ) = 1 then 18: integrateInPred(G i ,G p ) 19: end if 20: end for 21: end Step 22: end procedure • Step 1: Eliminate constant gates.

Using techniques such as peek-hole optimization, duplicate code removal, or dead code elimination. , as used in [180, Chap. 3] to minimize the AES circuit). A special case of our algorithm for propagating 1-input gates into their predecessor or successor gates was mentioned for NOT gates in [233]. Further, they give explicit circuit sizes for standard functionalities such as addition, subtraction, multiplication, multiplexer, and comparison of unsigned integers when circuits are decomposed into 2-input gates.

2 Secure Mobile Agents In the mobile agents scenario, the originator creates SW agents that can perform tasks on behalf of the originator. After creating the agents for some specific purpose, the originator sends them out to visit various remote hosts, where the agents perform computations on behalf of the originator. When the agents return home, the originator retrieves the results of these computations from the agents. The utility of this paradigm is based on the ability of the originator to go offline after sending the agents out, and, ideally, no further interaction between the agent and the originator or the host should be required.

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