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Privacy data aggregation method based on multi-dimensional decomposition in sensor network

A sensor network, privacy data technology, applied in the field of privacy protection, can solve the problems of global sensitivity over-disturbance, destroy data utility, etc., and achieve the effect of good data utility

Inactive Publication Date: 2017-04-26
XI AN JIAOTONG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Generally, the differential privacy method based on the Laplacian noise mechanism may cause over-disturbance due to the large global sensitivity, thus destroying the utility of the data.

Method used

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  • Privacy data aggregation method based on multi-dimensional decomposition in sensor network
  • Privacy data aggregation method based on multi-dimensional decomposition in sensor network
  • Privacy data aggregation method based on multi-dimensional decomposition in sensor network

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Embodiment Construction

[0039] The present invention will be described in further detail below in conjunction with the accompanying drawings.

[0040] The privacy data aggregation method based on multidimensional decomposition in the sensor network of the present invention comprises the following steps:

[0041] Step1 initialization: given the dimension base b (generally 2, which is consistent with the computer binary representation) and the global sensitivity g (the maximum value of the sensor monitoring value can be taken), the obtained dimension d is

[0042]

[0043] Local sensitivity s in each dimension i (where i=1,2,...,d) is

[0044]

[0045] Step2 data acquisition and decomposition: reference figure 2 , obtain the monitoring data directly through the underlying hardware device, and obtain the original monitoring value X corresponding to each time slot t t , and decompose it into d-dimensional data, thus It can be expressed as

[0046]

[0047] in, is the original monitoring...

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Abstract

The invention discloses a private data aggregating method based on multidimensional decomposition in a sensor network. In the sensor network, an over-disturbance phenomenon is possibly caused by a general differential privacy method based on a Laplace noise mechanism due to higher global sensitiveness, so as to destroy the effectiveness of aggregated data. According to the method provided by the invention, the differential privacy protection is realized by decomposing a single data stream into an exponential-weighted multidimensional data stream and adding independent noise on every dimension of data stream according to the local sensitiveness and the privacy estimation of every dimension. In comparison with a general aggregation process under the Laplace noise mechanism, by using the method, the better data effectiveness is provided while the privacy of a user at a same extent is guaranteed.

Description

Technical field: [0001] The invention belongs to the field of privacy protection, and in particular relates to a method for gathering private data based on multidimensional decomposition in a sensor network. Background technique: [0002] Various sensors in many new sensor networks (such as smart grids, participatory perception systems, etc.) collect time-series data related to users or the environment and transmit them to the server. High-level statistical analysis and data mining operations. On the one hand, the data aggregation operation reduces data redundancy and transmission volume, thereby reducing energy consumption and data delay time. On the other hand, the data aggregation operation only transmits key or purpose-related data to upper-layer nodes or servers, which also reduces the possibility of fine-grained data being leaked to a certain extent. Although fine-grained information cannot be directly accessed, detailed monitoring results may still be inferred from ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F21/62
CPCG06F21/6245G06F21/6263
Inventor 杨新宇任雪斌蔺杰王路辉李庄园王腾赵聪
Owner XI AN JIAOTONG UNIV
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