Fast Time Series Discords Detection with Privacy Preserving

Chunkai Zhang; Ao Yin; Yulin Wu; Yingyang Chen; Xuan Wang

doi:10.1109/TrustCom/BigDataSE.2018.00157

Fast Time Series Discords Detection with Privacy Preserving

Chunkai Zhang
, Ao Yin
, Yulin Wu
, Yingyang Chen
, Xuan Wang

Harbin Institute of Technology Shenzhen

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

Abstract

Discords are the most inconsistent subsequences from others in time series data. In this paper, we propose an efficient scheme to detect time series discords with considering privacy protection in the case of multi-party participation. In our solution, we divide the detection task into two parts, one is that each data owner transforms their time series data into transaction tables based on SAX. For each transaction table, data owners make use of BCP cryptosystem, cryptographic hash function, and random disturbance to construct encrypted transaction tables, which are sent to the cloud. The other is that the cloud will execute our algorithm FMDP to obtain the discord scores of subsequences based on these received transaction tables. Since data is reduced before sent to the cloud, our solution greatly reduces cloud computing costs and communication costs.

Original language	English
Title of host publication	Proceedings - 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications and 12th IEEE International Conference on Big Data Science and Engineering, Trustcom/BigDataSE 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1129-1139
Number of pages	11
ISBN (Print)	9781538643877
DOIs	https://doi.org/10.1109/TrustCom/BigDataSE.2018.00157
State	Published - 5 Sep 2018
Externally published	Yes
Event	17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications and 12th IEEE International Conference on Big Data Science and Engineering, Trustcom/BigDataSE 2018 - New York, United States Duration: 31 Jul 2018 → 3 Aug 2018

Publication series

Name	Proceedings - 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications and 12th IEEE International Conference on Big Data Science and Engineering, Trustcom/BigDataSE 2018

Conference

Conference	17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications and 12th IEEE International Conference on Big Data Science and Engineering, Trustcom/BigDataSE 2018
Country/Territory	United States
City	New York
Period	31/07/18 → 3/08/18

Keywords

Discords detection
Privacy Preserving
SAX
Semi-honest
Time Series

Access to Document

10.1109/TrustCom/BigDataSE.2018.00157

Cite this

Zhang, C., Yin, A., Wu, Y., Chen, Y., & Wang, X. (2018). Fast Time Series Discords Detection with Privacy Preserving. In Proceedings - 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications and 12th IEEE International Conference on Big Data Science and Engineering, Trustcom/BigDataSE 2018 (pp. 1129-1139). Article 8456026 (Proceedings - 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications and 12th IEEE International Conference on Big Data Science and Engineering, Trustcom/BigDataSE 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/TrustCom/BigDataSE.2018.00157

Zhang, Chunkai ; Yin, Ao ; Wu, Yulin et al. / Fast Time Series Discords Detection with Privacy Preserving. Proceedings - 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications and 12th IEEE International Conference on Big Data Science and Engineering, Trustcom/BigDataSE 2018. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 1129-1139 (Proceedings - 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications and 12th IEEE International Conference on Big Data Science and Engineering, Trustcom/BigDataSE 2018).

@inproceedings{783c956d17524028b7e4343755746778,

title = "Fast Time Series Discords Detection with Privacy Preserving",

abstract = "Discords are the most inconsistent subsequences from others in time series data. In this paper, we propose an efficient scheme to detect time series discords with considering privacy protection in the case of multi-party participation. In our solution, we divide the detection task into two parts, one is that each data owner transforms their time series data into transaction tables based on SAX. For each transaction table, data owners make use of BCP cryptosystem, cryptographic hash function, and random disturbance to construct encrypted transaction tables, which are sent to the cloud. The other is that the cloud will execute our algorithm FMDP to obtain the discord scores of subsequences based on these received transaction tables. Since data is reduced before sent to the cloud, our solution greatly reduces cloud computing costs and communication costs.",

keywords = "Discords detection, Privacy Preserving, SAX, Semi-honest, Time Series",

author = "Chunkai Zhang and Ao Yin and Yulin Wu and Yingyang Chen and Xuan Wang",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications and 12th IEEE International Conference on Big Data Science and Engineering, Trustcom/BigDataSE 2018 ; Conference date: 31-07-2018 Through 03-08-2018",

year = "2018",

month = sep,

day = "5",

doi = "10.1109/TrustCom/BigDataSE.2018.00157",

language = "英语",

isbn = "9781538643877",

series = "Proceedings - 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications and 12th IEEE International Conference on Big Data Science and Engineering, Trustcom/BigDataSE 2018",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1129--1139",

booktitle = "Proceedings - 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications and 12th IEEE International Conference on Big Data Science and Engineering, Trustcom/BigDataSE 2018",

address = "美国",

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Zhang, C, Yin, A, Wu, Y, Chen, Y & Wang, X 2018, Fast Time Series Discords Detection with Privacy Preserving. in Proceedings - 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications and 12th IEEE International Conference on Big Data Science and Engineering, Trustcom/BigDataSE 2018., 8456026, Proceedings - 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications and 12th IEEE International Conference on Big Data Science and Engineering, Trustcom/BigDataSE 2018, Institute of Electrical and Electronics Engineers Inc., pp. 1129-1139, 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications and 12th IEEE International Conference on Big Data Science and Engineering, Trustcom/BigDataSE 2018, New York, United States, 31/07/18. https://doi.org/10.1109/TrustCom/BigDataSE.2018.00157

Fast Time Series Discords Detection with Privacy Preserving. / Zhang, Chunkai; Yin, Ao; Wu, Yulin et al.
Proceedings - 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications and 12th IEEE International Conference on Big Data Science and Engineering, Trustcom/BigDataSE 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 1129-1139 8456026 (Proceedings - 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications and 12th IEEE International Conference on Big Data Science and Engineering, Trustcom/BigDataSE 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Fast Time Series Discords Detection with Privacy Preserving

AU - Zhang, Chunkai

AU - Yin, Ao

AU - Wu, Yulin

AU - Chen, Yingyang

AU - Wang, Xuan

PY - 2018/9/5

Y1 - 2018/9/5

N2 - Discords are the most inconsistent subsequences from others in time series data. In this paper, we propose an efficient scheme to detect time series discords with considering privacy protection in the case of multi-party participation. In our solution, we divide the detection task into two parts, one is that each data owner transforms their time series data into transaction tables based on SAX. For each transaction table, data owners make use of BCP cryptosystem, cryptographic hash function, and random disturbance to construct encrypted transaction tables, which are sent to the cloud. The other is that the cloud will execute our algorithm FMDP to obtain the discord scores of subsequences based on these received transaction tables. Since data is reduced before sent to the cloud, our solution greatly reduces cloud computing costs and communication costs.

AB - Discords are the most inconsistent subsequences from others in time series data. In this paper, we propose an efficient scheme to detect time series discords with considering privacy protection in the case of multi-party participation. In our solution, we divide the detection task into two parts, one is that each data owner transforms their time series data into transaction tables based on SAX. For each transaction table, data owners make use of BCP cryptosystem, cryptographic hash function, and random disturbance to construct encrypted transaction tables, which are sent to the cloud. The other is that the cloud will execute our algorithm FMDP to obtain the discord scores of subsequences based on these received transaction tables. Since data is reduced before sent to the cloud, our solution greatly reduces cloud computing costs and communication costs.

KW - Discords detection

KW - Privacy Preserving

KW - SAX

KW - Semi-honest

KW - Time Series

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DO - 10.1109/TrustCom/BigDataSE.2018.00157

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AN - SCOPUS:85054065945

SN - 9781538643877

T3 - Proceedings - 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications and 12th IEEE International Conference on Big Data Science and Engineering, Trustcom/BigDataSE 2018

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BT - Proceedings - 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications and 12th IEEE International Conference on Big Data Science and Engineering, Trustcom/BigDataSE 2018

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications and 12th IEEE International Conference on Big Data Science and Engineering, Trustcom/BigDataSE 2018

Y2 - 31 July 2018 through 3 August 2018

ER -

Zhang C, Yin A, Wu Y, Chen Y, Wang X. Fast Time Series Discords Detection with Privacy Preserving. In Proceedings - 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications and 12th IEEE International Conference on Big Data Science and Engineering, Trustcom/BigDataSE 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 1129-1139. 8456026. (Proceedings - 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications and 12th IEEE International Conference on Big Data Science and Engineering, Trustcom/BigDataSE 2018). doi: 10.1109/TrustCom/BigDataSE.2018.00157

Fast Time Series Discords Detection with Privacy Preserving

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Keywords

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