Floating Point Arithmetic Protocols for Constructing Secure Data Analysis Application

A large variety of data mining and machine learning techniques are applied to a wide range of applications today. There- fore, there is a real need to develop technologies that allows data analysis while preserving the confidentiality of the data. Secure multi-party computation (SMC) protocols allows...

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Vydané v:Procedia computer science Ročník 22; s. 152 - 161
Hlavní autori: Liu, Yun-Ching, Chiang, Yi-Ting, Hsu, Tsan-Sheng, Liau, Churn-Jung, Wang, Da-Wei
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier B.V 2013
Predmet:
Linear Regression
Secure Floating Point Computation
Secure Multi-party Computations
Secure Scalar Product
Secure Multi-party Computations
Secure Floating Point Computation
Secure Scalar Product
Linear Regression
ISSN:1877-0509, 1877-0509
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Popis
Shrnutí:A large variety of data mining and machine learning techniques are applied to a wide range of applications today. There- fore, there is a real need to develop technologies that allows data analysis while preserving the confidentiality of the data. Secure multi-party computation (SMC) protocols allows participants to cooperate on various computations while retaining the privacy of their own input data, which is an ideal solution to this issue. Although there is a number of frameworks developed in SMC to meet this challenge, but they are either tailored to perform only on specific tasks or provide very limited precision. In this paper, we have developed protocols for floating point arithmetic based on secure scalar product protocols, which is re- quired in many real world applications. Our protocols follow most of the IEEE-754 standard, supporting the four fundamental arithmetic operations, namely addition, subtraction, multiplication, and division. We will demonstrate the practicality of these protocols through performing various statistical calculations that is widely used in most data analysis tasks. Our experiments show the performance of our framework is both practical and promising.
ISSN:1877-0509
1877-0509
DOI:10.1016/j.procs.2013.09.091