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A Workload Division Differential Privacy Algorithm to Improve the Accuracy for Linear Computations

Differential privacy algorithm is an effective technology to protect data privacy, and there are many pieces of research about differential privacy and some practical applications from the Internet companies, such as Apple and Google, etc. By differential privacy technology, the data organizations c...

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Detalles Bibliográficos
Autores principales: Li, Jun, Ma, Huan, Wu, Guangjun, Zhang, Yanqin, Ma, Bingnan, Hui, Zhen, Zhang, Lei, Zhu, Bingqing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7302815/
http://dx.doi.org/10.1007/978-3-030-50417-5_33
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author Li, Jun
Ma, Huan
Wu, Guangjun
Zhang, Yanqin
Ma, Bingnan
Hui, Zhen
Zhang, Lei
Zhu, Bingqing
author_facet Li, Jun
Ma, Huan
Wu, Guangjun
Zhang, Yanqin
Ma, Bingnan
Hui, Zhen
Zhang, Lei
Zhu, Bingqing
author_sort Li, Jun
collection PubMed
description Differential privacy algorithm is an effective technology to protect data privacy, and there are many pieces of research about differential privacy and some practical applications from the Internet companies, such as Apple and Google, etc. By differential privacy technology, the data organizations can allow external data scientists to explore their sensitive datasets, and the data owners can be ensured provable privacy guarantees meanwhile. It is inevitable that the query results that will cause the error, as a consequence that the differential privacy algorithm would disturb the data, and some differential privacy algorithms are aimed to reduce the introduced noise. However, those algorithms just adopt to the simple or relative uniform data, when the data distribution is complex, some algorithms will lose efficiency. In this paper, we propose a new simple [Formula: see text]-differential privacy algorithm. Our approach includes two key points: Firstly, we used Laplace-based noise to disturb answer to reduce the error of the linear computation queries under intensive data items by workload-aware noise; Secondly, we propose an optimized workload division method. We divide the queries recursively to reduce the added noise, which can reduce computation error when there exists query hot spot in the workload. We conduct extensive evaluation over six real-world datasets to examine the performance of our approach. The experimental results show that our approach can reduce nearly 40% computation error for linear computation when compared with MWEM, DAWA, and Identity. Meanwhile, our approach can achieve better response time to answer the query cases compared with the start-of-the-art algorithms.
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spelling pubmed-73028152020-06-19 A Workload Division Differential Privacy Algorithm to Improve the Accuracy for Linear Computations Li, Jun Ma, Huan Wu, Guangjun Zhang, Yanqin Ma, Bingnan Hui, Zhen Zhang, Lei Zhu, Bingqing Computational Science – ICCS 2020 Article Differential privacy algorithm is an effective technology to protect data privacy, and there are many pieces of research about differential privacy and some practical applications from the Internet companies, such as Apple and Google, etc. By differential privacy technology, the data organizations can allow external data scientists to explore their sensitive datasets, and the data owners can be ensured provable privacy guarantees meanwhile. It is inevitable that the query results that will cause the error, as a consequence that the differential privacy algorithm would disturb the data, and some differential privacy algorithms are aimed to reduce the introduced noise. However, those algorithms just adopt to the simple or relative uniform data, when the data distribution is complex, some algorithms will lose efficiency. In this paper, we propose a new simple [Formula: see text]-differential privacy algorithm. Our approach includes two key points: Firstly, we used Laplace-based noise to disturb answer to reduce the error of the linear computation queries under intensive data items by workload-aware noise; Secondly, we propose an optimized workload division method. We divide the queries recursively to reduce the added noise, which can reduce computation error when there exists query hot spot in the workload. We conduct extensive evaluation over six real-world datasets to examine the performance of our approach. The experimental results show that our approach can reduce nearly 40% computation error for linear computation when compared with MWEM, DAWA, and Identity. Meanwhile, our approach can achieve better response time to answer the query cases compared with the start-of-the-art algorithms. 2020-06-15 /pmc/articles/PMC7302815/ http://dx.doi.org/10.1007/978-3-030-50417-5_33 Text en © Springer Nature Switzerland AG 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Li, Jun
Ma, Huan
Wu, Guangjun
Zhang, Yanqin
Ma, Bingnan
Hui, Zhen
Zhang, Lei
Zhu, Bingqing
A Workload Division Differential Privacy Algorithm to Improve the Accuracy for Linear Computations
title A Workload Division Differential Privacy Algorithm to Improve the Accuracy for Linear Computations
title_full A Workload Division Differential Privacy Algorithm to Improve the Accuracy for Linear Computations
title_fullStr A Workload Division Differential Privacy Algorithm to Improve the Accuracy for Linear Computations
title_full_unstemmed A Workload Division Differential Privacy Algorithm to Improve the Accuracy for Linear Computations
title_short A Workload Division Differential Privacy Algorithm to Improve the Accuracy for Linear Computations
title_sort workload division differential privacy algorithm to improve the accuracy for linear computations
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7302815/
http://dx.doi.org/10.1007/978-3-030-50417-5_33
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