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

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	A Workload Division Differential Privacy Algorithm to Improve the Accuracy for Linear Computations
Creator	Li, Jun Zhang, Lei Zhu, Bingqing Ma, Huan Hui, Zhen Ma, Bingnan Wu, Guangjun Zhang, Yanqin
source	PMC
abstract	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.
has issue date	2020-06-15(xsd:dateTime)
bibo:doi	10.1007/978-3-030-50417-5_33
has license	no-cc
sha1sum (hex)	9168a599013f90667cc8d00ff4a0599dfdcaef87
schema:url	https://doi.org/10.1007/978-3-030-50417-5_33
resource representing a document's title	A Workload Division Differential Privacy Algorithm to Improve the Accuracy for Linear Computations
has PubMed Central identifier	PMC7302815
schema:publication	Computational Science - ICCS 2020
resource representing a document's body	covid:9168a599013f90667cc8d00ff4a0599dfdcaef87#body_text
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