The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
La collecte et l'analyse des données personnelles sont importantes dans les applications d'information modernes. Bien que la confidentialité des fournisseurs de données doive être protégée, le besoin de suivre certains fournisseurs de données se fait souvent sentir, par exemple pour retrouver des patients spécifiques ou des utilisateurs adverses. Ainsi, suivre uniquement des personnes spécifiques sans révéler l'identité des utilisateurs normaux est très important pour le fonctionnement des systèmes d'information utilisant des données personnelles. Il est difficile de connaître à l’avance les règles permettant de préciser la nécessité du suivi puisque ces règles sont dérivées de l’analyse des données collectées. Ainsi, il serait utile de fournir une méthode générale pouvant utiliser n’importe quelle méthode d’analyse de données quel que soit le type de données et la nature des règles. Dans cet article, nous proposons une construction d'analyse de données préservant la confidentialité qui permet à une autorité de détecter des utilisateurs spécifiques tandis que d'autres utilisateurs honnêtes restent anonymes. En utilisant les techniques cryptographiques de signatures de groupe à ouverture dépendante du message (GS-MDO) et de chiffrement à clé publique à ouverture non interactive (PKENO), nous fournissons une table de correspondance qui relie un utilisateur et des données de manière sécurisée, et nous pouvons utiliser n’importe quelle technique d’anonymisation et méthode d’analyse des données. Il convient particulièrement de noter qu’il n’existe pas de « grand frère », ce qui signifie qu’aucune entité ne peut identifier les utilisateurs qui ne fournissent pas de données sur les anomalies, tandis que les mauvais comportements sont toujours traçables. Nous montrons le résultat de la mise en œuvre de notre construction. En bref, le temps système de notre construction est de l'ordre de 10 ms pour un seul thread. Nous confirmons également l'efficacité de notre construction en utilisant un ensemble de données du monde réel.
Hiromi ARAI
the RIKEN Center for Advanced Intelligence Project,JST PRESTO
Keita EMURA
the National Institute of Information and Communications Technology (NICT)
Takuya HAYASHI
the Digital Garage, Inc.
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Hiromi ARAI, Keita EMURA, Takuya HAYASHI, "Privacy-Preserving Data Analysis: Providing Traceability without Big Brother" in IEICE TRANSACTIONS on Fundamentals,
vol. E104-A, no. 1, pp. 2-19, January 2021, doi: 10.1587/transfun.2020CIP0001.
Abstract: Collecting and analyzing personal data is important in modern information applications. Though the privacy of data providers should be protected, the need to track certain data providers often arises, such as tracing specific patients or adversarial users. Thus, tracking only specific persons without revealing normal users' identities is quite important for operating information systems using personal data. It is difficult to know in advance the rules for specifying the necessity of tracking since the rules are derived by the analysis of collected data. Thus, it would be useful to provide a general way that can employ any data analysis method regardless of the type of data and the nature of the rules. In this paper, we propose a privacy-preserving data analysis construction that allows an authority to detect specific users while other honest users are kept anonymous. By using the cryptographic techniques of group signatures with message-dependent opening (GS-MDO) and public key encryption with non-interactive opening (PKENO), we provide a correspondence table that links a user and data in a secure way, and we can employ any anonymization technique and data analysis method. It is particularly worth noting that no “big brother” exists, meaning that no single entity can identify users who do not provide anomaly data, while bad behaviors are always traceable. We show the result of implementing our construction. Briefly, the overhead of our construction is on the order of 10 ms for a single thread. We also confirm the efficiency of our construction by using a real-world dataset.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2020CIP0001/_p
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@ARTICLE{e104-a_1_2,
author={Hiromi ARAI, Keita EMURA, Takuya HAYASHI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Privacy-Preserving Data Analysis: Providing Traceability without Big Brother},
year={2021},
volume={E104-A},
number={1},
pages={2-19},
abstract={Collecting and analyzing personal data is important in modern information applications. Though the privacy of data providers should be protected, the need to track certain data providers often arises, such as tracing specific patients or adversarial users. Thus, tracking only specific persons without revealing normal users' identities is quite important for operating information systems using personal data. It is difficult to know in advance the rules for specifying the necessity of tracking since the rules are derived by the analysis of collected data. Thus, it would be useful to provide a general way that can employ any data analysis method regardless of the type of data and the nature of the rules. In this paper, we propose a privacy-preserving data analysis construction that allows an authority to detect specific users while other honest users are kept anonymous. By using the cryptographic techniques of group signatures with message-dependent opening (GS-MDO) and public key encryption with non-interactive opening (PKENO), we provide a correspondence table that links a user and data in a secure way, and we can employ any anonymization technique and data analysis method. It is particularly worth noting that no “big brother” exists, meaning that no single entity can identify users who do not provide anomaly data, while bad behaviors are always traceable. We show the result of implementing our construction. Briefly, the overhead of our construction is on the order of 10 ms for a single thread. We also confirm the efficiency of our construction by using a real-world dataset.},
keywords={},
doi={10.1587/transfun.2020CIP0001},
ISSN={1745-1337},
month={January},}
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TY - JOUR
TI - Privacy-Preserving Data Analysis: Providing Traceability without Big Brother
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 2
EP - 19
AU - Hiromi ARAI
AU - Keita EMURA
AU - Takuya HAYASHI
PY - 2021
DO - 10.1587/transfun.2020CIP0001
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E104-A
IS - 1
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - January 2021
AB - Collecting and analyzing personal data is important in modern information applications. Though the privacy of data providers should be protected, the need to track certain data providers often arises, such as tracing specific patients or adversarial users. Thus, tracking only specific persons without revealing normal users' identities is quite important for operating information systems using personal data. It is difficult to know in advance the rules for specifying the necessity of tracking since the rules are derived by the analysis of collected data. Thus, it would be useful to provide a general way that can employ any data analysis method regardless of the type of data and the nature of the rules. In this paper, we propose a privacy-preserving data analysis construction that allows an authority to detect specific users while other honest users are kept anonymous. By using the cryptographic techniques of group signatures with message-dependent opening (GS-MDO) and public key encryption with non-interactive opening (PKENO), we provide a correspondence table that links a user and data in a secure way, and we can employ any anonymization technique and data analysis method. It is particularly worth noting that no “big brother” exists, meaning that no single entity can identify users who do not provide anomaly data, while bad behaviors are always traceable. We show the result of implementing our construction. Briefly, the overhead of our construction is on the order of 10 ms for a single thread. We also confirm the efficiency of our construction by using a real-world dataset.
ER -