Abstract:The main objective of the paper is to highlight the important aspects of the data management in condition monitoring and maintenance, especially when the emergent technologies, such as the cloud computing and big data, are to be considered in the maintenance department. In addition, one of the main data management elements highlighted in the current work are the cybersecurity issues which might be one of the biggest obstacles hindering the development of cloud based big data for condition-based maintenance (CB… Show more
“…e reason why this works is illustrated in Figure 5(C). For example, if C 1 contains an encryption of 1100 (2) (4 bits) and C 2 contains 1010 (2) , we can see that a carry is generated as a result of adding the 1's complements of C 1 and C 2 . Based on this, we can confirm that C 1 > C 2 .…”
Section: Comparator (Comp)mentioning
confidence: 99%
“…One significant difference of this approach from IEEE 754 is that the value of the mantissa is assumed to be in the range [0, 1). is is because the HEAAN operation works better when the hidden plaintext in the ciphertext in [0, 1) than [1,2). Figure 6 shows the procedure on how a single floatingpoint number is encrypted using three ciphertexts.…”
Section: Heaan-basedmentioning
confidence: 99%
“…We discuss how the following code works. Because Two n-slot ciphertexts (input) a 0 a 1 a 2 ... .... a n-2 a n- Case 2 (C 1 ≤ C 2 ): C 1 = 1010 (2) , C 2 = 1100 (2) C 1 + ~C 2 = 1010 (2) + 0011 (2) = 1101 (2) → carry = 0…”
Section: Addition/subtractionmentioning
confidence: 99%
“…If C m [0] is 1, the fraction part of the multiplication result is C m [1] ∼ C m [22], and the exponent value should be incremented by one. If C m [0] is 0, that means C m [1] is 1, so the exponent does not increase and the fraction part of the result is C m [2]∼C m [24]. e method that reflects this is shown in Figure 22 below.…”
Section: Multiplicationmentioning
confidence: 99%
“…Using these properties, we can process and analyze sensitive medical and financial information without exposing them if we use FHE. erefore, FHE is expected to be an indispensable core technology for the prevalence of artificial intelligence and data mining, which is the core technology of the 4th industrial revolution [1,2].…”
As a method of privacy-preserving data analysis (PPDA), a fully homomorphic encryption (FHE) has been in the spotlight recently. Unfortunately, because many data analysis methods assume that the type of data is of real type, the FHE-based PPDA methods could not support the enough level of accuracy due to the nature of FHE that fixed-point real-number representation is supported easily. In this paper, we propose a new method to represent encrypted floating-point real numbers on top of FHE. e proposed method is designed to have analogous range and accuracy to 32-bit floating-point number in IEEE 754 representation. We propose a method to perform arithmetic operations and size comparison operations. e proposed method is designed using two different FHEs, HEAAN and TFHE. As a result, HEAAN is proven to be very efficient for arithmetic operations and TFHE is efficient in size comparison. is study is expected to contribute to practical use of FHE-based PPDA.
“…e reason why this works is illustrated in Figure 5(C). For example, if C 1 contains an encryption of 1100 (2) (4 bits) and C 2 contains 1010 (2) , we can see that a carry is generated as a result of adding the 1's complements of C 1 and C 2 . Based on this, we can confirm that C 1 > C 2 .…”
Section: Comparator (Comp)mentioning
confidence: 99%
“…One significant difference of this approach from IEEE 754 is that the value of the mantissa is assumed to be in the range [0, 1). is is because the HEAAN operation works better when the hidden plaintext in the ciphertext in [0, 1) than [1,2). Figure 6 shows the procedure on how a single floatingpoint number is encrypted using three ciphertexts.…”
Section: Heaan-basedmentioning
confidence: 99%
“…We discuss how the following code works. Because Two n-slot ciphertexts (input) a 0 a 1 a 2 ... .... a n-2 a n- Case 2 (C 1 ≤ C 2 ): C 1 = 1010 (2) , C 2 = 1100 (2) C 1 + ~C 2 = 1010 (2) + 0011 (2) = 1101 (2) → carry = 0…”
Section: Addition/subtractionmentioning
confidence: 99%
“…If C m [0] is 1, the fraction part of the multiplication result is C m [1] ∼ C m [22], and the exponent value should be incremented by one. If C m [0] is 0, that means C m [1] is 1, so the exponent does not increase and the fraction part of the result is C m [2]∼C m [24]. e method that reflects this is shown in Figure 22 below.…”
Section: Multiplicationmentioning
confidence: 99%
“…Using these properties, we can process and analyze sensitive medical and financial information without exposing them if we use FHE. erefore, FHE is expected to be an indispensable core technology for the prevalence of artificial intelligence and data mining, which is the core technology of the 4th industrial revolution [1,2].…”
As a method of privacy-preserving data analysis (PPDA), a fully homomorphic encryption (FHE) has been in the spotlight recently. Unfortunately, because many data analysis methods assume that the type of data is of real type, the FHE-based PPDA methods could not support the enough level of accuracy due to the nature of FHE that fixed-point real-number representation is supported easily. In this paper, we propose a new method to represent encrypted floating-point real numbers on top of FHE. e proposed method is designed to have analogous range and accuracy to 32-bit floating-point number in IEEE 754 representation. We propose a method to perform arithmetic operations and size comparison operations. e proposed method is designed using two different FHEs, HEAAN and TFHE. As a result, HEAAN is proven to be very efficient for arithmetic operations and TFHE is efficient in size comparison. is study is expected to contribute to practical use of FHE-based PPDA.
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