Statistical learning based fully homomorphic encryption on encrypted data
Abstract Statistical learning has been widely used in many fields, such as science, engineering and finance, to extract important patterns, trends, and understand “what the data say”. Privacy of statistical learning, i.e., user and sensitive data, is significant problem of performing computation, es...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Jiang, Linzhi [verfasserIn] Xu, Chunxiang [verfasserIn] Wang, Xiaofang [verfasserIn] Lin, Chao [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Soft Computing - Springer-Verlag, 2003, 21(2016), 24 vom: 03. Aug., Seite 7473-7483 |
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| Übergeordnetes Werk: |
volume:21 ; year:2016 ; number:24 ; day:03 ; month:08 ; pages:7473-7483 |
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| DOI / URN: |
10.1007/s00500-016-2296-6 |
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SPR006495257 |
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| 520 | |a Abstract Statistical learning has been widely used in many fields, such as science, engineering and finance, to extract important patterns, trends, and understand “what the data say”. Privacy of statistical learning, i.e., user and sensitive data, is significant problem of performing computation, especially outsourcing cloud computing. Some fully homomorphic encryption schemes can achieve computation on the encrypted data, but most of them are lack of efficiency. Fully homomorphic encryption based on learning with errors over rings (RLWE) supports a finite number of addition and multiplication on the encrypted data, thus can be viewed as the polynomial computation in cyclotomic fields. Computation on the encrypted data can be converted into computation associated with polynomial. So, fully homomorphic encryption from RLWE is very efficient relative to other schemes. Our contribution includes two aspects. Firstly, we show a scheme to represent the training and testing data for statistical learning. The proposed scheme firstly transforms the data into integer and then encodes them into polynomial so that the encryption, decryption and homomorphic operation can be performed efficiently. We also carefully choose the parameters of fully homomorphic encryption from RLWE to meet the requirement of efficiency. User only needs to upload the encrypted data to cloud server, and then the server trains and tests the encrypted data, returns the analysis and prediction results to user. Secondly, we present a comparison scheme on the encrypted data for statistical learning algorithms, which is security on the known plain-text attack and ciphertext only attack model. | ||
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| 700 | 1 | |a Xu, Chunxiang |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Xiaofang |e verfasserin |4 aut | |
| 700 | 1 | |a Lin, Chao |e verfasserin |4 aut | |
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10.1007/s00500-016-2296-6 doi (DE-627)SPR006495257 (SPR)s00500-016-2296-6-e DE-627 ger DE-627 rakwb eng Jiang, Linzhi verfasserin aut Statistical learning based fully homomorphic encryption on encrypted data 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Statistical learning has been widely used in many fields, such as science, engineering and finance, to extract important patterns, trends, and understand “what the data say”. Privacy of statistical learning, i.e., user and sensitive data, is significant problem of performing computation, especially outsourcing cloud computing. Some fully homomorphic encryption schemes can achieve computation on the encrypted data, but most of them are lack of efficiency. Fully homomorphic encryption based on learning with errors over rings (RLWE) supports a finite number of addition and multiplication on the encrypted data, thus can be viewed as the polynomial computation in cyclotomic fields. Computation on the encrypted data can be converted into computation associated with polynomial. So, fully homomorphic encryption from RLWE is very efficient relative to other schemes. Our contribution includes two aspects. Firstly, we show a scheme to represent the training and testing data for statistical learning. The proposed scheme firstly transforms the data into integer and then encodes them into polynomial so that the encryption, decryption and homomorphic operation can be performed efficiently. We also carefully choose the parameters of fully homomorphic encryption from RLWE to meet the requirement of efficiency. User only needs to upload the encrypted data to cloud server, and then the server trains and tests the encrypted data, returns the analysis and prediction results to user. Secondly, we present a comparison scheme on the encrypted data for statistical learning algorithms, which is security on the known plain-text attack and ciphertext only attack model. Statistic learning (dpeaa)DE-He213 Comparison on encrypted data (dpeaa)DE-He213 Fully homomorphic encryption (dpeaa)DE-He213 RLWE (dpeaa)DE-He213 Xu, Chunxiang verfasserin aut Wang, Xiaofang verfasserin aut Lin, Chao verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 21(2016), 24 vom: 03. Aug., Seite 7473-7483 (DE-627)SPR006469531 nnns volume:21 year:2016 number:24 day:03 month:08 pages:7473-7483 https://dx.doi.org/10.1007/s00500-016-2296-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 21 2016 24 03 08 7473-7483 |
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10.1007/s00500-016-2296-6 doi (DE-627)SPR006495257 (SPR)s00500-016-2296-6-e DE-627 ger DE-627 rakwb eng Jiang, Linzhi verfasserin aut Statistical learning based fully homomorphic encryption on encrypted data 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Statistical learning has been widely used in many fields, such as science, engineering and finance, to extract important patterns, trends, and understand “what the data say”. Privacy of statistical learning, i.e., user and sensitive data, is significant problem of performing computation, especially outsourcing cloud computing. Some fully homomorphic encryption schemes can achieve computation on the encrypted data, but most of them are lack of efficiency. Fully homomorphic encryption based on learning with errors over rings (RLWE) supports a finite number of addition and multiplication on the encrypted data, thus can be viewed as the polynomial computation in cyclotomic fields. Computation on the encrypted data can be converted into computation associated with polynomial. So, fully homomorphic encryption from RLWE is very efficient relative to other schemes. Our contribution includes two aspects. Firstly, we show a scheme to represent the training and testing data for statistical learning. The proposed scheme firstly transforms the data into integer and then encodes them into polynomial so that the encryption, decryption and homomorphic operation can be performed efficiently. We also carefully choose the parameters of fully homomorphic encryption from RLWE to meet the requirement of efficiency. User only needs to upload the encrypted data to cloud server, and then the server trains and tests the encrypted data, returns the analysis and prediction results to user. Secondly, we present a comparison scheme on the encrypted data for statistical learning algorithms, which is security on the known plain-text attack and ciphertext only attack model. Statistic learning (dpeaa)DE-He213 Comparison on encrypted data (dpeaa)DE-He213 Fully homomorphic encryption (dpeaa)DE-He213 RLWE (dpeaa)DE-He213 Xu, Chunxiang verfasserin aut Wang, Xiaofang verfasserin aut Lin, Chao verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 21(2016), 24 vom: 03. Aug., Seite 7473-7483 (DE-627)SPR006469531 nnns volume:21 year:2016 number:24 day:03 month:08 pages:7473-7483 https://dx.doi.org/10.1007/s00500-016-2296-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 21 2016 24 03 08 7473-7483 |
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10.1007/s00500-016-2296-6 doi (DE-627)SPR006495257 (SPR)s00500-016-2296-6-e DE-627 ger DE-627 rakwb eng Jiang, Linzhi verfasserin aut Statistical learning based fully homomorphic encryption on encrypted data 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Statistical learning has been widely used in many fields, such as science, engineering and finance, to extract important patterns, trends, and understand “what the data say”. Privacy of statistical learning, i.e., user and sensitive data, is significant problem of performing computation, especially outsourcing cloud computing. Some fully homomorphic encryption schemes can achieve computation on the encrypted data, but most of them are lack of efficiency. Fully homomorphic encryption based on learning with errors over rings (RLWE) supports a finite number of addition and multiplication on the encrypted data, thus can be viewed as the polynomial computation in cyclotomic fields. Computation on the encrypted data can be converted into computation associated with polynomial. So, fully homomorphic encryption from RLWE is very efficient relative to other schemes. Our contribution includes two aspects. Firstly, we show a scheme to represent the training and testing data for statistical learning. The proposed scheme firstly transforms the data into integer and then encodes them into polynomial so that the encryption, decryption and homomorphic operation can be performed efficiently. We also carefully choose the parameters of fully homomorphic encryption from RLWE to meet the requirement of efficiency. User only needs to upload the encrypted data to cloud server, and then the server trains and tests the encrypted data, returns the analysis and prediction results to user. Secondly, we present a comparison scheme on the encrypted data for statistical learning algorithms, which is security on the known plain-text attack and ciphertext only attack model. Statistic learning (dpeaa)DE-He213 Comparison on encrypted data (dpeaa)DE-He213 Fully homomorphic encryption (dpeaa)DE-He213 RLWE (dpeaa)DE-He213 Xu, Chunxiang verfasserin aut Wang, Xiaofang verfasserin aut Lin, Chao verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 21(2016), 24 vom: 03. Aug., Seite 7473-7483 (DE-627)SPR006469531 nnns volume:21 year:2016 number:24 day:03 month:08 pages:7473-7483 https://dx.doi.org/10.1007/s00500-016-2296-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 21 2016 24 03 08 7473-7483 |
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10.1007/s00500-016-2296-6 doi (DE-627)SPR006495257 (SPR)s00500-016-2296-6-e DE-627 ger DE-627 rakwb eng Jiang, Linzhi verfasserin aut Statistical learning based fully homomorphic encryption on encrypted data 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Statistical learning has been widely used in many fields, such as science, engineering and finance, to extract important patterns, trends, and understand “what the data say”. Privacy of statistical learning, i.e., user and sensitive data, is significant problem of performing computation, especially outsourcing cloud computing. Some fully homomorphic encryption schemes can achieve computation on the encrypted data, but most of them are lack of efficiency. Fully homomorphic encryption based on learning with errors over rings (RLWE) supports a finite number of addition and multiplication on the encrypted data, thus can be viewed as the polynomial computation in cyclotomic fields. Computation on the encrypted data can be converted into computation associated with polynomial. So, fully homomorphic encryption from RLWE is very efficient relative to other schemes. Our contribution includes two aspects. Firstly, we show a scheme to represent the training and testing data for statistical learning. The proposed scheme firstly transforms the data into integer and then encodes them into polynomial so that the encryption, decryption and homomorphic operation can be performed efficiently. We also carefully choose the parameters of fully homomorphic encryption from RLWE to meet the requirement of efficiency. User only needs to upload the encrypted data to cloud server, and then the server trains and tests the encrypted data, returns the analysis and prediction results to user. Secondly, we present a comparison scheme on the encrypted data for statistical learning algorithms, which is security on the known plain-text attack and ciphertext only attack model. Statistic learning (dpeaa)DE-He213 Comparison on encrypted data (dpeaa)DE-He213 Fully homomorphic encryption (dpeaa)DE-He213 RLWE (dpeaa)DE-He213 Xu, Chunxiang verfasserin aut Wang, Xiaofang verfasserin aut Lin, Chao verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 21(2016), 24 vom: 03. Aug., Seite 7473-7483 (DE-627)SPR006469531 nnns volume:21 year:2016 number:24 day:03 month:08 pages:7473-7483 https://dx.doi.org/10.1007/s00500-016-2296-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 21 2016 24 03 08 7473-7483 |
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10.1007/s00500-016-2296-6 doi (DE-627)SPR006495257 (SPR)s00500-016-2296-6-e DE-627 ger DE-627 rakwb eng Jiang, Linzhi verfasserin aut Statistical learning based fully homomorphic encryption on encrypted data 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Statistical learning has been widely used in many fields, such as science, engineering and finance, to extract important patterns, trends, and understand “what the data say”. Privacy of statistical learning, i.e., user and sensitive data, is significant problem of performing computation, especially outsourcing cloud computing. Some fully homomorphic encryption schemes can achieve computation on the encrypted data, but most of them are lack of efficiency. Fully homomorphic encryption based on learning with errors over rings (RLWE) supports a finite number of addition and multiplication on the encrypted data, thus can be viewed as the polynomial computation in cyclotomic fields. Computation on the encrypted data can be converted into computation associated with polynomial. So, fully homomorphic encryption from RLWE is very efficient relative to other schemes. Our contribution includes two aspects. Firstly, we show a scheme to represent the training and testing data for statistical learning. The proposed scheme firstly transforms the data into integer and then encodes them into polynomial so that the encryption, decryption and homomorphic operation can be performed efficiently. We also carefully choose the parameters of fully homomorphic encryption from RLWE to meet the requirement of efficiency. User only needs to upload the encrypted data to cloud server, and then the server trains and tests the encrypted data, returns the analysis and prediction results to user. Secondly, we present a comparison scheme on the encrypted data for statistical learning algorithms, which is security on the known plain-text attack and ciphertext only attack model. Statistic learning (dpeaa)DE-He213 Comparison on encrypted data (dpeaa)DE-He213 Fully homomorphic encryption (dpeaa)DE-He213 RLWE (dpeaa)DE-He213 Xu, Chunxiang verfasserin aut Wang, Xiaofang verfasserin aut Lin, Chao verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 21(2016), 24 vom: 03. Aug., Seite 7473-7483 (DE-627)SPR006469531 nnns volume:21 year:2016 number:24 day:03 month:08 pages:7473-7483 https://dx.doi.org/10.1007/s00500-016-2296-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 21 2016 24 03 08 7473-7483 |
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Abstract Statistical learning has been widely used in many fields, such as science, engineering and finance, to extract important patterns, trends, and understand “what the data say”. Privacy of statistical learning, i.e., user and sensitive data, is significant problem of performing computation, especially outsourcing cloud computing. Some fully homomorphic encryption schemes can achieve computation on the encrypted data, but most of them are lack of efficiency. Fully homomorphic encryption based on learning with errors over rings (RLWE) supports a finite number of addition and multiplication on the encrypted data, thus can be viewed as the polynomial computation in cyclotomic fields. Computation on the encrypted data can be converted into computation associated with polynomial. So, fully homomorphic encryption from RLWE is very efficient relative to other schemes. Our contribution includes two aspects. Firstly, we show a scheme to represent the training and testing data for statistical learning. The proposed scheme firstly transforms the data into integer and then encodes them into polynomial so that the encryption, decryption and homomorphic operation can be performed efficiently. We also carefully choose the parameters of fully homomorphic encryption from RLWE to meet the requirement of efficiency. User only needs to upload the encrypted data to cloud server, and then the server trains and tests the encrypted data, returns the analysis and prediction results to user. Secondly, we present a comparison scheme on the encrypted data for statistical learning algorithms, which is security on the known plain-text attack and ciphertext only attack model. |
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Abstract Statistical learning has been widely used in many fields, such as science, engineering and finance, to extract important patterns, trends, and understand “what the data say”. Privacy of statistical learning, i.e., user and sensitive data, is significant problem of performing computation, especially outsourcing cloud computing. Some fully homomorphic encryption schemes can achieve computation on the encrypted data, but most of them are lack of efficiency. Fully homomorphic encryption based on learning with errors over rings (RLWE) supports a finite number of addition and multiplication on the encrypted data, thus can be viewed as the polynomial computation in cyclotomic fields. Computation on the encrypted data can be converted into computation associated with polynomial. So, fully homomorphic encryption from RLWE is very efficient relative to other schemes. Our contribution includes two aspects. Firstly, we show a scheme to represent the training and testing data for statistical learning. The proposed scheme firstly transforms the data into integer and then encodes them into polynomial so that the encryption, decryption and homomorphic operation can be performed efficiently. We also carefully choose the parameters of fully homomorphic encryption from RLWE to meet the requirement of efficiency. User only needs to upload the encrypted data to cloud server, and then the server trains and tests the encrypted data, returns the analysis and prediction results to user. Secondly, we present a comparison scheme on the encrypted data for statistical learning algorithms, which is security on the known plain-text attack and ciphertext only attack model. |
| abstract_unstemmed |
Abstract Statistical learning has been widely used in many fields, such as science, engineering and finance, to extract important patterns, trends, and understand “what the data say”. Privacy of statistical learning, i.e., user and sensitive data, is significant problem of performing computation, especially outsourcing cloud computing. Some fully homomorphic encryption schemes can achieve computation on the encrypted data, but most of them are lack of efficiency. Fully homomorphic encryption based on learning with errors over rings (RLWE) supports a finite number of addition and multiplication on the encrypted data, thus can be viewed as the polynomial computation in cyclotomic fields. Computation on the encrypted data can be converted into computation associated with polynomial. So, fully homomorphic encryption from RLWE is very efficient relative to other schemes. Our contribution includes two aspects. Firstly, we show a scheme to represent the training and testing data for statistical learning. The proposed scheme firstly transforms the data into integer and then encodes them into polynomial so that the encryption, decryption and homomorphic operation can be performed efficiently. We also carefully choose the parameters of fully homomorphic encryption from RLWE to meet the requirement of efficiency. User only needs to upload the encrypted data to cloud server, and then the server trains and tests the encrypted data, returns the analysis and prediction results to user. Secondly, we present a comparison scheme on the encrypted data for statistical learning algorithms, which is security on the known plain-text attack and ciphertext only attack model. |
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Statistical learning based fully homomorphic encryption on encrypted data |
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https://dx.doi.org/10.1007/s00500-016-2296-6 |
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Xu, Chunxiang Wang, Xiaofang Lin, Chao |
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Xu, Chunxiang Wang, Xiaofang Lin, Chao |
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10.1007/s00500-016-2296-6 |
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2024-07-03T23:16:55.278Z |
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