Privacy-Preserving Statistical Analysis Method by Splitting Server Roles for Distributed Real-World Data

Jun ISHII; Hiroyuki MAEOMICHI; Akihiro TSUTSUI; Ikuo YODA

doi:10.1587/transcom.E97.B.1779

Privacy-Preserving Statistical Analysis Method by Splitting Server Roles for Distributed Real-World Data

Jun ISHII, Hiroyuki MAEOMICHI, Akihiro TSUTSUI, Ikuo YODA

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This paper propose a novel method for obtaining statistical results such as averages, variances, and correlations without leaking any raw data values from data-holders by using multiple pseudonyms. At present, to obtain statistical results using a large amount of data, we need to collect all data in the same storage device. However, gathering real-world data that were generated by different people is not easy because they often contain private information. The authors split the roles of servers into publishing pseudonyms and collecting answers. Splitting these roles, different entities can more easily join as pseudonym servers than in previous secure multi-party computation methods and there is less chance of collusion between servers. Thus, our method enables data holders to protect themselves against malicious attacks from data users. We also estimated a typical problem that occurred with our method and added a pseudonym availability confirmation protocol to prevent the problem. We report our evaluation of the effectiveness of our method through implementation and experimentation and discuss how we incorporated the WebSocket protocol and MySQL Memoty Storage Engine to remove the bottleneck and improve the implementation style. Finally, we explain how our method can obtain averages, variances, and correlation from 5000 data holders within 50 seconds.

Publication: IEICE TRANSACTIONS on Communications Vol.E97-B No.9 pp.1779-1789

Publication Date: 2014/09/01

Publicized

Online ISSN: 1745-1345

DOI: 10.1587/transcom.E97.B.1779

Type of Manuscript: Special Section PAPER (Special Section on Ambient Intelligence and Sensor Networks)

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Authors

Jun ISHII
  NTT Corporation
Hiroyuki MAEOMICHI
  NTT Corporation
Akihiro TSUTSUI
  NTT Corporation
Ikuo YODA
  NTT Corporation

Keyword

privacy-preserving, multiple pseudonyms, query auditing, WebSocket, in-memory database

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Jun ISHII, Hiroyuki MAEOMICHI, Akihiro TSUTSUI, Ikuo YODA, "Privacy-Preserving Statistical Analysis Method by Splitting Server Roles for Distributed Real-World Data" in IEICE TRANSACTIONS on Communications, vol. E97-B, no. 9, pp. 1779-1789, September 2014, doi: 10.1587/transcom.E97.B.1779.
Abstract: This paper propose a novel method for obtaining statistical results such as averages, variances, and correlations without leaking any raw data values from data-holders by using multiple pseudonyms. At present, to obtain statistical results using a large amount of data, we need to collect all data in the same storage device. However, gathering real-world data that were generated by different people is not easy because they often contain private information. The authors split the roles of servers into publishing pseudonyms and collecting answers. Splitting these roles, different entities can more easily join as pseudonym servers than in previous secure multi-party computation methods and there is less chance of collusion between servers. Thus, our method enables data holders to protect themselves against malicious attacks from data users. We also estimated a typical problem that occurred with our method and added a pseudonym availability confirmation protocol to prevent the problem. We report our evaluation of the effectiveness of our method through implementation and experimentation and discuss how we incorporated the WebSocket protocol and MySQL Memoty Storage Engine to remove the bottleneck and improve the implementation style. Finally, we explain how our method can obtain averages, variances, and correlation from 5000 data holders within 50 seconds.
URL: https://globals.ieice.org/en_transactions/communications/10.1587/transcom.E97.B.1779/_p

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@ARTICLE{e97-b_9_1779,
author={Jun ISHII, Hiroyuki MAEOMICHI, Akihiro TSUTSUI, Ikuo YODA, },
journal={IEICE TRANSACTIONS on Communications},
title={Privacy-Preserving Statistical Analysis Method by Splitting Server Roles for Distributed Real-World Data},
year={2014},
volume={E97-B},
number={9},
pages={1779-1789},
abstract={This paper propose a novel method for obtaining statistical results such as averages, variances, and correlations without leaking any raw data values from data-holders by using multiple pseudonyms. At present, to obtain statistical results using a large amount of data, we need to collect all data in the same storage device. However, gathering real-world data that were generated by different people is not easy because they often contain private information. The authors split the roles of servers into publishing pseudonyms and collecting answers. Splitting these roles, different entities can more easily join as pseudonym servers than in previous secure multi-party computation methods and there is less chance of collusion between servers. Thus, our method enables data holders to protect themselves against malicious attacks from data users. We also estimated a typical problem that occurred with our method and added a pseudonym availability confirmation protocol to prevent the problem. We report our evaluation of the effectiveness of our method through implementation and experimentation and discuss how we incorporated the WebSocket protocol and MySQL Memoty Storage Engine to remove the bottleneck and improve the implementation style. Finally, we explain how our method can obtain averages, variances, and correlation from 5000 data holders within 50 seconds.},
keywords={},
doi={10.1587/transcom.E97.B.1779},
ISSN={1745-1345},
month={September},}

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TY - JOUR
TI - Privacy-Preserving Statistical Analysis Method by Splitting Server Roles for Distributed Real-World Data
T2 - IEICE TRANSACTIONS on Communications
SP - 1779
EP - 1789
AU - Jun ISHII
AU - Hiroyuki MAEOMICHI
AU - Akihiro TSUTSUI
AU - Ikuo YODA
PY - 2014
DO - 10.1587/transcom.E97.B.1779
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E97-B
IS - 9
JA - IEICE TRANSACTIONS on Communications
Y1 - September 2014
AB - This paper propose a novel method for obtaining statistical results such as averages, variances, and correlations without leaking any raw data values from data-holders by using multiple pseudonyms. At present, to obtain statistical results using a large amount of data, we need to collect all data in the same storage device. However, gathering real-world data that were generated by different people is not easy because they often contain private information. The authors split the roles of servers into publishing pseudonyms and collecting answers. Splitting these roles, different entities can more easily join as pseudonym servers than in previous secure multi-party computation methods and there is less chance of collusion between servers. Thus, our method enables data holders to protect themselves against malicious attacks from data users. We also estimated a typical problem that occurred with our method and added a pseudonym availability confirmation protocol to prevent the problem. We report our evaluation of the effectiveness of our method through implementation and experimentation and discuss how we incorporated the WebSocket protocol and MySQL Memoty Storage Engine to remove the bottleneck and improve the implementation style. Finally, we explain how our method can obtain averages, variances, and correlation from 5000 data holders within 50 seconds.
ER -