The Optimal Noise Distribution for Privacy Preserving in Mobile Aggregation Applications

In emerging mobile aggregation applications (e.g., large-scale mobile survey), individual privacy is a crucial factor to determine the effectiveness, for which the noise-addition method (i.e., a random noise value is added to the true value) is a simple yet powerful approach. However, improper addit...

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Main Authors: Hao Zhang, Nenghai Yu, Honggang Hu
Format: Article
Language:English
Published: Wiley 2014-02-01
Series:International Journal of Distributed Sensor Networks
Online Access:https://doi.org/10.1155/2014/678098
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author Hao Zhang
Nenghai Yu
Honggang Hu
author_facet Hao Zhang
Nenghai Yu
Honggang Hu
author_sort Hao Zhang
collection DOAJ
description In emerging mobile aggregation applications (e.g., large-scale mobile survey), individual privacy is a crucial factor to determine the effectiveness, for which the noise-addition method (i.e., a random noise value is added to the true value) is a simple yet powerful approach. However, improper additive noise could result in bias for the aggregate result. It demands an optimal noise distribution to reduce the deviation. In this paper, we develop a mathematical framework to derive the optimal noise distribution that provides privacy protection under the constraint of a limited value deviation. Specifically, we first derive a generic system dynamic function that the optimal noise distribution must satisfy and further investigate two special cases for the distribution of the original value (i.e., Gaussian and truncated Gaussian distribution). Our theoretical and numerical analysis suggests that the Gaussian distribution is the optimal solution for the Gaussian input and the asymptotically optimal solution for the truncated Gaussian input.
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institution Kabale University
issn 1550-1477
language English
publishDate 2014-02-01
publisher Wiley
record_format Article
series International Journal of Distributed Sensor Networks
spelling doaj-art-ce90a238941145dfaf8d2edd2d0d788c2025-02-03T05:44:20ZengWileyInternational Journal of Distributed Sensor Networks1550-14772014-02-011010.1155/2014/678098678098The Optimal Noise Distribution for Privacy Preserving in Mobile Aggregation ApplicationsHao ZhangNenghai YuHonggang HuIn emerging mobile aggregation applications (e.g., large-scale mobile survey), individual privacy is a crucial factor to determine the effectiveness, for which the noise-addition method (i.e., a random noise value is added to the true value) is a simple yet powerful approach. However, improper additive noise could result in bias for the aggregate result. It demands an optimal noise distribution to reduce the deviation. In this paper, we develop a mathematical framework to derive the optimal noise distribution that provides privacy protection under the constraint of a limited value deviation. Specifically, we first derive a generic system dynamic function that the optimal noise distribution must satisfy and further investigate two special cases for the distribution of the original value (i.e., Gaussian and truncated Gaussian distribution). Our theoretical and numerical analysis suggests that the Gaussian distribution is the optimal solution for the Gaussian input and the asymptotically optimal solution for the truncated Gaussian input.https://doi.org/10.1155/2014/678098
spellingShingle Hao Zhang
Nenghai Yu
Honggang Hu
The Optimal Noise Distribution for Privacy Preserving in Mobile Aggregation Applications
International Journal of Distributed Sensor Networks
title The Optimal Noise Distribution for Privacy Preserving in Mobile Aggregation Applications
title_full The Optimal Noise Distribution for Privacy Preserving in Mobile Aggregation Applications
title_fullStr The Optimal Noise Distribution for Privacy Preserving in Mobile Aggregation Applications
title_full_unstemmed The Optimal Noise Distribution for Privacy Preserving in Mobile Aggregation Applications
title_short The Optimal Noise Distribution for Privacy Preserving in Mobile Aggregation Applications
title_sort optimal noise distribution for privacy preserving in mobile aggregation applications
url https://doi.org/10.1155/2014/678098
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