Floating Point Arithmetic Protocols for Constructing Secure Data Analysis Application

Liu, Yun-Ching; Chiang, Yi-Ting; Hsu, Tsan-sheng; Liau, Churn‐Jung; Wang, Dawei

doi:10.1016/j.procs.2013.09.091

Cited by 16 publications

(16 citation statements)

References 15 publications

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“…The IEEE 754 division and square root perform very well compared to approximation-based versions, whereas the error function and multiplication are slower on larger input sizes. Our protocols well outperform the results from [27] and our double precision addition and division are faster than the implementation of [1], however, multiplication is slightly slower. The latter is expected, since it is efficient to implement floating-point multiplication using secret sharing and less is gained from a GC approach.…”

Section: Performance Analysismentioning

confidence: 72%

“…For example, overflows and underflows will result in infinities, multiplying 0 with infinity results in NaN etc. Previous implementations [1][20] [27] did not explicitly handle such cases and produced valid but meaningless results in error situations. Nonetheless, our approach would easily support raising standardized exception flags also in between operations, for example by adding an exception flag variable as an input and output to all circuits.…”

Section: Circuits For Ieee 754 Primitivesmentioning

confidence: 99%

“…However, floating-point arithmetic is much more sophisticated and contains a composition of different operations, both integer arithmetic as well as bitwise operations. Existing implementations based on secret sharing provide only near approximations to the IEEE 754 standard [1][20] [27]. Although [14] proposes IEEE 754 protocols, no implementation is provided.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Combining Secret Sharing and Garbled Circuits for Efficient Private IEEE 754 Floating-Point Computations

Pullonen

Siim

2015

Lecture Notes in Computer Science

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Abstract. Two of the major branches in secure multi-party computation research are secret sharing and garbled circuits. This work succeeds in combining these to enable seamlessly switching to the technique more efficient for the required functionality. As an example, we add garbled circuits based IEEE 754 floating-point numbers to a secret sharing environment achieving very high efficiency and the first, to our knowledge, fully IEEE 754 compliant secure floating-point implementation.

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Section: Performance Analysismentioning

confidence: 72%

Section: Circuits For Ieee 754 Primitivesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Combining Secret Sharing and Garbled Circuits for Efficient Private IEEE 754 Floating-Point Computations

Pullonen

Siim

2015

Lecture Notes in Computer Science

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“…In order to access the full power of numerical methods, one needs an implementation of floating point arithmetic. This has been done by three groups of authors, Aliasgari et al [1], Liu et al [15], and Kamm and Willemson [11]. All these approaches follow the same basic pattern -the floating point number x is represented as…”

Section: Previous Workmentioning

confidence: 99%

“…Then all the authors proceed to build elementary operations of addition and multiplication, followed by some selection of more complicated functions. Liu et al [15] consider two-party additive secret sharing over a ring Z N and only develop addition, subtraction, multiplication and division.…”

Section: Previous Workmentioning

confidence: 99%

Hybrid Model of Fixed and Floating Point Numbers in Secure Multiparty Computations

Krips

Willemson

2014

Lecture Notes in Computer Science

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This paper develops a new hybrid model of floating point numbers suitable for operations in secure multi-party computations. The basic idea is to consider the significand of the floating point number as a fixed point number and implement elementary function applications separately of the significand. This gives the greatest performance gain for the power functions (e.g. inverse and square root), with computation speeds improving up to 18 times in certain configurations. Also other functions (like exponent and Gaussian error function) allow for the corresponding optimisation.We have proposed new polynomials for approximation, and implemented and benchmarked all our algorithms on the Sharemind secure multi-party computation framework.

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The design and implementation of a 16 bit floating point arithmetic unit using BZK.SAU.FPGA microcomputer assembly language

Öztekin

Kisioglu

Gülbağ

et al. 2022

Comp Applic In Engineering

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The microcomputer architecture named BZK.SAU.FPGA, which was developed in 2014, was used to increase efficiency in the teaching of the Computer Architecture and Organization course, which is one of the basic courses of the Department of Computer Engineering. In the last version released in 2018, the architecture has been started to be used in many engineering disciplines with its modular structure and a basic operating system. However, this architecture uses fixed-point numbers in arithmetic operations. Although this situation contributes to its use as an educational tool at the beginner level for new learners, the fact that it cannot operate with floating point numbers is one of the biggest shortcomings of the educational tool. Floating point numbers are used extensively in applications such as mathematical analysis, signal processing, image processing, and so on. Therefore, in this study, the FPA unit for the computation of addition, subtraction, multiplication, and division operations is integrated into this architecture. For FPA unit implementation, an assembly-level program has been embedded in the architecture using an IEEE-754 half-precision binary format (FP16) with the aim of presenting the fundamental of the floating-point computation. The FPA unit proposed to observe the behavior of floating point numbers in arithmetic operations and increase awareness of these issues was used as an auxiliary tool in the computer architecture course. A pre-and postsurvey study was conducted to see the learning outcomes of students using this tool on floating point numbers. We found a significant association between pre-and postsurvey, indicating that students' knowledge about the behavior of floating point numbers increased (p < .001).

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Floating Point Arithmetic Protocols for Constructing Secure Data Analysis Application

Cited by 16 publications

References 15 publications

Combining Secret Sharing and Garbled Circuits for Efficient Private IEEE 754 Floating-Point Computations

Combining Secret Sharing and Garbled Circuits for Efficient Private IEEE 754 Floating-Point Computations

Hybrid Model of Fixed and Floating Point Numbers in Secure Multiparty Computations

The design and implementation of a 16 bit floating point arithmetic unit using BZK.SAU.FPGA microcomputer assembly language

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