Privacy Protection Method on Publishing Dynamic Set-Valued Data

DOI

10.2991/cimns-16.2016.66How to use a DOI?

Keywords

set-valued data; differential privacy; dynamic release; taxonomy tree; sample

Abstract

After researching many protection methods for releasing sensitive information, we found that differential privacy could provide strong guarantees on it, but it will adding too much noise and spending too much times in releasing dynamic set-valued data. To solve these problems, this paper present a privacy protection method based on Diffpart. For a dataset need to be published, set it by Diffpart algorithm firstly, then using sampling method to sample some nodes and add the Laplace noise to them to protect the sensitive information when dataset needs to be updated. Then, generate a transposing number randomly to adjust the sampling nodes for subsequent update. Through the experiment, compared with the Diffpart algorithm, the method that we raised reached a ideal effect in practicability and protective for data release.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Jian Zhang
AU  - Yu Yang
PY  - 2016/09
DA  - 2016/09
TI  - Privacy Protection Method on Publishing Dynamic Set-Valued Data
BT  - Proceedings of the 2016 International Conference on Communications, Information Management and Network Security
PB  - Atlantis Press
SP  - 262
EP  - 265
SN  - 2352-538X
UR  - https://doi.org/10.2991/cimns-16.2016.66
DO  - 10.2991/cimns-16.2016.66
ID  - Zhang2016/09
ER  -