We present two secure two party computation (STPC) protocols for piecewise function approximation on private data. The protocols rely on a piecewise approximation of the to-be-computed function easing the implementation in an STPC setting. The first protocol relies entirely on garbled circuits (GCs), while the second one exploits a hybrid construction where GC and homomorphic encryption are used together. In addition to piecewise constant and linear approximation, polynomial interpolation is also considered. From a communication complexity perspective, the full-GC implementation is preferable when the input and output variables can be represented with a small number of bits, while the hybrid solution is preferable otherwise. With regard to computational complexity, the full-GC solution is generally more convenient.
Piecewise Function Approximation with Private Data / Lazzeretti, Riccardo; Pignata, Tommaso; Barni, Mauro. - In: IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY. - ISSN 1556-6013. - 11:3(2016), pp. 642-657. [10.1109/TIFS.2015.2503268]
Piecewise Function Approximation with Private Data
LAZZERETTI, RICCARDO
;
2016
Abstract
We present two secure two party computation (STPC) protocols for piecewise function approximation on private data. The protocols rely on a piecewise approximation of the to-be-computed function easing the implementation in an STPC setting. The first protocol relies entirely on garbled circuits (GCs), while the second one exploits a hybrid construction where GC and homomorphic encryption are used together. In addition to piecewise constant and linear approximation, polynomial interpolation is also considered. From a communication complexity perspective, the full-GC implementation is preferable when the input and output variables can be represented with a small number of bits, while the hybrid solution is preferable otherwise. With regard to computational complexity, the full-GC solution is generally more convenient.File | Dimensione | Formato | |
---|---|---|---|
Lazzeretti_Piecewise-Function-Approximation_2016.pdf
solo gestori archivio
Tipologia:
Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza:
Tutti i diritti riservati (All rights reserved)
Dimensione
3.31 MB
Formato
Adobe PDF
|
3.31 MB | Adobe PDF | Contatta l'autore |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.