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
A coleta e a análise de dados pessoais são importantes nas aplicações de informação modernas. Embora a privacidade dos fornecedores de dados deva ser protegida, muitas vezes surge a necessidade de rastrear determinados fornecedores de dados, como rastrear pacientes específicos ou utilizadores adversários. Assim, rastrear apenas pessoas específicas sem revelar as identidades normais dos utilizadores é muito importante para a operação de sistemas de informação que utilizam dados pessoais. É difícil conhecer antecipadamente as regras para especificar a necessidade de rastreamento, uma vez que as regras são derivadas da análise dos dados recolhidos. Assim, seria útil fornecer uma forma geral que pudesse empregar qualquer método de análise de dados, independentemente do tipo de dados e da natureza das regras. Neste artigo, propomos uma construção de análise de dados que preserva a privacidade e permite que uma autoridade detecte usuários específicos enquanto outros usuários honestos são mantidos anônimos. Utilizando as técnicas criptográficas de assinaturas de grupo com abertura dependente de mensagem (GS-MDO) e criptografia de chave pública com abertura não interativa (PKENO), fornecemos uma tabela de correspondência que vincula um usuário e dados de forma segura, e podemos empregar qualquer técnica de anonimato e método de análise de dados. É particularmente importante notar que não existe um “irmão mais velho”, o que significa que nenhuma entidade pode identificar utilizadores que não forneçam dados de anomalias, enquanto os maus comportamentos são sempre rastreáveis. Mostramos o resultado da implementação da nossa construção. Resumidamente, o overhead da nossa construção é da ordem de 10 ms para um único thread. Também confirmamos a eficiência da nossa construção usando um conjunto de dados do mundo real.
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 -